Joonsoo Sean Lyeo1
Angelica Blais1Paula Cloutier1
Addo Boafo1,2
Aroldo Dargél2,3Amanda Helleman1
Tanya Tanya3
Esperance Kashala-Abotnes1,2Christina Honeywell1
Kathleen Pajer1,2*- 1Children's Hospital of Eastern Ontario (CHEO) Research Institute, Ottawa, ON, Canada
- 2Department of Psychiatry, University of Ottawa Medical School, Ottawa, ON, Canada
- 3The Ottawa Hospital, Ottawa, ON, Canada
Introduction: Precision child and youth mental health (PCYMH) offers a promising array of tools and methodologies to address the intensifying burden of mental health challenges in child and youth populations. However, the current state of PCYMH research requires better characterization. To this end, we conducted a scoping review aiming to provide a ‘lay of the literature’ for this emerging field.
Methods: Following the Joanna Briggs Institute methodology for scoping reviews, we searched PubMed and Embase for PCYMH studies from January 1, 1980 to November 30, 2023, updating the search on November 1, 2024. The final dataset comprised 124 publications, summarized with descriptive quantitative analysis and qualitative content analysis.
Results: Quantitative analyses revealed that 48% (60/124) of studies had been published between 2020 and 2024, with the majority (51% (63/124)) studying populations in the U.S. Most studies were observational in design. Content analysis revealed four categories of PCYMH research focus: (1) Biomarkers (68% (84/124)); (2) Non-Biological Markers (17% (22/124)); (3) Implementation of PCYMH Interventions (14% (17/124)); and (4) Predictive Algorithms (5% (6/124)). PCYMH tools were underutilized and infrequently combined. Studies producing multimodal profiles of participants, e.g., using neuroimaging, genetics, digital health data, and lifestyle data were scarce. No study used reporting guidelines.
Discussion: Our findings indicate that this body of research is still in its infancy. We highlight opportunities to advance the study of PCYMH and provide recommendations to support the maturation of this new field.
Introduction
Child and youth mental health (CYMH) problems have intensified and are now considered a public health crisis (1). Nearly 15% of youth ages 10–19 years have experienced a mental illness, accounting for 13% of the global burden of disease within this population (1). As many as 1 in 5 people are diagnosed with a mental illness before the age of 25, with 70% experiencing their first symptoms before the age of 18 (2). Emerging evidence demonstrates a rising prevalence and severity of mental illness, particularly anxiety and mood disorders, as reflected in increasing rates of mental health service utilization and psychotropic medication use (3). The burden of CYMH problems is projected to intensify in the coming years, driven by the rise of social media consumption, enduring consequences of the COVID-19 pandemic, heightened experiences of loneliness and social isolation, and mounting concerns about a future shaped by global instability and climate change (4, 5).
In recent years, a growing proportion of CYMH cases have been identified as having ‘complex mental health needs’, requiring higher intensity services and more frequent care with sustained involvement from CYMH agencies (6). Emergency departments (EDs) are becoming the default sites for CYMH care (7), with ED visits surpassing increases in CYMH outpatient visits (8). This is problematic as EDs have limited capacity to provide such care (8, 9). In addition, nearly a quarter of CYMH patients re-visit EDs for more mental healthcare within six months (9, 10). This suggests that many CYMH patients cannot get their needs met, highlighting the inability of current healthcare systems to provide treatment in an effective or timely manner (11).
The limitations of current CYMH care have received increased scrutiny (12). For instance, Bickman et al. (13) called into question the utility, reliability, and validity of psychiatric diagnoses in CYMH care, determining in a three-part study of affective and behavior disorders of children and youth that: few CYMH outcomes were diagnosed more consistently than a random selection of symptoms; there was low diagnostic inter-rater agreement between parents, youth, and clinicians; and that comorbidities posed a significant barrier to clinician-based diagnoses (13). Insel further questioned the status quo of CYMH care, highlighting the need to pivot away from reliance on behavioral symptoms, the predominant method for diagnosis of CYMH disorders, to instead create a neurodevelopmental framework (14).
It is clear that transformation of CYMH care and the research that drives it is necessary. The Precision Child and Youth Mental Health (PCYMH) paradigm has the potential to accomplish this (15, 16). Such a transformation will not be easy and must not discard the advances made in evidence-based care (17, 18), but the standard ‘one size fits all’ approach, doesn't work for all (18).
Furthermore, the growing popularity of the PCYMH paradigm is, in part, a response to the problems stemming for questionable validity of CYMH diagnoses, concerns about underlying heterogeneity in patients labeled with the current nomenclature, and the difficulty of applying statistical mean-based results from randomized controlled trials (RCTs) to the individual needs of children and youth (15, 19).
Advances in PCYMH have already yielded several promising avenues for tailoring mental health services (15). For instance, at the Children’s Hospital of Eastern Ontario (CHEO), the creation of a participatory logic model demonstrated how the synthesis of implementation science and artificial intelligence (AI) data science can be used to plan a PCYMH research and clinical care transformation program (20). Another example is the development of a clinical decision support system by a Norwegian research team, that has shown how big data analytics, and the electronic health record (EHR) can be used to create an Individualized Digital Decision Assist System (IDDEAS) to enhance precision and timeliness of medical decisions via clinician decision-making support based on targeted clinical knowledge and patient health information (21, 22).
However, many developments in the growing field of PCYMH research remain fragmented and siloed. For PCYMH methods to be smoothly integrated into the wider corpus of CYMH care and research, it is essential to catalogue the work done to date. Such efforts are the first step towards establishing consistent definitions and terminology within the field and may help guide funders on strategic allocation of their resources.
To this end, we conducted a scoping review aiming to provide a ‘lay of the literature’ review of the emerging field of PCYMH. Our goal was to provide a snapshot of current scientific work published under the rubric of PCYMH or its synonyms and make recommendations for future research.
Methods
We used the most recent version of the Joanna Briggs Institute (JBI) methodology to conduct scoping reviews (23). The review was organized into 5 stages: (1) specification of a research question; (2) systematic retrieval of studies from the scientific literature; (3) screening them for relevance to the research question; (4) extracting information about the studies; (5) conducting descriptive quantitative and qualitative content analysis of retained studies; and (6) synthesizing these data into a final summary.
Research question and search strategy
We formulated the research question using the Population, Concept, Context (PCC) framework (23): What are the characteristics or features of all published research studies involving precision child and youth mental health? This exercise facilitated development of the search strategy, guided creation of inclusion and exclusion criteria, and structured the data collection.
The review took a broad view, rather than focusing on a specific disorder or method. Our study was structured according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA-ScR) (see Supplementary Table 1 for framework components' locations in the paper) (23). We searched PubMed and Embase, in consultation with an in-house librarian. Together, these two databases provide extensive coverage of medical and biomedical academic literature from the past eight decades, with each database comprising over 25 million records. Because the concept of precision medicine had its roots earlier than 1999 (24), the usual landmark date cited, we conducted our search from January 1, 1980 to November 30, 2023, updating it on November 1, 2024.
The search strategy was mapped onto the following three domains with synonyms: (1) precision health (e.g., ‘precision medicine’, ‘precision health’, ‘personalized medicine’); (2) child and youth (e.g., ‘child’, ‘adolescent’, ‘youth’); and (3) mental health (e.g., ‘mental health’, ‘behavioral health’, ‘child psychiatry’). Because child and youth mental health research often takes place in pediatric settings, we set the age range at 0 to <18 years. Relevant keywords and synonyms for each conceptual construct were joined using ‘OR’, with ‘AND’ being used to join the three constructs into a single search strategy. The search strategy and results for PubMed and Embase are presented in Supplementary Tables 2, 3 respectively.
Inclusion and exclusion criteria
PCYMH research was defined as studies with aims or goals that addressed precision diagnosis, treatment, prognosis, or prevention of a mental health, psychiatric, or behavioral health condition based on differences in individuals’ biological characteristics, lifestyle, and environment, in addition to symptoms. Studies were included if they: (1) investigated the topic of precision mental health; (2) had a primary study population between the ages of 0 and up to 18 years old or, if in a study of adults and children/youth, separate findings on the 0-18-year-olds were provided; (3) were published in English; and 4) had “precision” or one of its synonyms in the title or abstract. Documents and publications not presenting published original research were excluded, e.g., conference abstracts, commentaries, literature reviews, meta-analyses, book chapters, and reports.
Prior to starting the screening process, inclusion and exclusion criteria were iteratively refined by JSL and KP through a series of consultations with other members of the research team. The research team pointed out that our initial inclusion criteria of “0–18 years of age” could be interpreted as those “up to” or “including the 18th year of life (which would make them 19)”. They gave examples from other studies showing that many papers with this age range often were included in studies of adults, without any separate results for the 18- year-olds. Therefore, this inclusion criterion was adjusted to specify “up to, but not including, the 18th year of life”.
Screening and data extraction
Covidence (25) was used to process papers retrieved from the search. Screening comprised two steps, both of which were carried out following the eligibility criteria by pairs of ten raters. Ratings were conducted blind to ratings by others. The first step was title and abstract screening. Articles remaining eligible after this step were next subjected to full-text review, again conducted by pairs of raters blind to each other’s work. For both steps, reviewer discrepancies were resolved through deliberation between KP and JSL.
All papers still meeting criteria after full-text review underwent final data extraction by two blinded raters, using the rubric in Table 1. In addition to data more commonly collected on study characteristics, we also collected information on “type of PCYMH study” (defined by the aim(s) or objective(s)), each study’s use of PCYMH tools, e.g., use of big data, AI, omics (19, 26),and whether or not a reporting guideline was used to structure the paper.
Table 1. Data extraction rubric.
Data analysis
Quantitative data were summarized with descriptive statistics. Qualitative analysis, particularly looking for patterns in clinical disorders or problems, type of PCYMH study, and use of PCYMH tools was based on the approach to latent pattern content analysis outlined by Potter & Levine-Donnerstein (27). As per this approach, analysis comprised the following four stages: (1) decontextualization – identification of recurrent units of meaning; (2) recontextualization – organization of the data into codes; (3) categorization – grouping of codes into categories and sub-categories of shared meaning; and (4) compilation – final refinement of categories and sub-categories. This process was iterative and recursive, with the findings generated during each stage of the analysis informing the coder’s approach to subsequent stages. All steps of the qualitative analysis were independently conducted by the first and senior authors.
Results
Article screening
The search yielded 1,266 studies (see Figure 1 for the PRISMA diagram). Of these studies, 272 were flagged by Covidence as duplicates and removed. All automatic duplications were manually verified by the first author. The remaining 995 studies underwent title and abstract screening, during which 600 studies were dropped as not meeting criteria. Full text screening identified 271 more that did not meet criteria. The remaining 124 (28–151) articles comprised the final set for review.
Figure 1. PRISMA Diagram.
Characteristics of articles
Dates of publication for the articles are displayed in Figure 2 by four-year blocks of time from pre-2000 to 2024. Only one paper meeting our criteria was published before 2000. This was followed by a slow but steady rise in publication rate until an inflection point occurred between 2010-2014, signaling a sharp uptick in the publication rates every four years over the subsequent decade. The highest rate to date was between 2020-2024, during which 48% (60/124) (29–31, 35, 39, 43, 44, 48, 50, 51, 54, 59, 61–63, 66–69, 74, 76–78, 80–83, 89, 90, 93–96, 98, 99, 105, 108–113, 115, 117–119, 121, 123, 124, 128, 131–133, 137, 139, 146–150) of the entire set was published.
Figure 2. Proportion of studies by publication dates (N=124).
Articles were from 26 countries, with multi-site study papers coded according to the primary site, determined through descriptions of the study setting, the senior author’s address, and locations of funding sources. As can be seen on the map in Figure 3, 51% (63/124) (28, 31, 32, 35, 37, 41, 43, 45–48, 50, 52–56, 58, 59, 64, 66, 69, 70, 74–76, 80, 82–84, 88–92, 96, 97, 99, 103, 106–109, 113–117, 120, 123, 126, 132–136, 139–142, 144, 146, 150) of the papers were from the United States, with China and the UK representing 8% (10/124) (68, 87, 95, 111, 125, 137, 143, 147–149) and 6% (7/124) (86, 93, 94, 104, 122, 128, 145), respectively. The remaining countries each contributed 1–6 papers.
Figure 3. Proportion of studies by country of origin (N=124).
Study features
None of the included studies used a reporting guideline. Table 2 provides an overview of the studies included, organized by first author’s last name and displaying CYMH focus, type of PCYMH study as defined by aim, PCYMH tool(s), and key findings. Supplementary Tables 4-7 present more details on each study.
Table 2. High-level summary of included studies by MH focus, type of PCYMH study, PCYMH tool(s), and key findings (N = 124) by first author’s last name.
The most common study design category was observational: cohort studies (35% (43/124)) (40–44, 53, 57, 61, 64, 65, 67, 69, 72, 74, 77, 78, 80, 82, 83, 85, 91, 94, 98, 102, 109, 112, 113, 115, 116, 118, 120, 121, 126, 131, 134, 136, 137, 139, 142, 147–150); case-control studies (32% (40/124)) (28, 30, 32–36, 38, 45, 47, 50, 52, 55, 56, 62, 68, 70, 79, 81, 84, 85, 87, 95, 99, 100, 103, 104, 108, 110, 111, 114, 119, 122, 124, 125, 127, 130, 140, 143, 145); cross-sectional studies (5% (6/124)) (49, 54, 90, 105, 117, 132); and case series (3% (4/124)) 39, 66, 76, 130. RCTs were used in 19% (24/124) (29, 31, 37, 46, 48, 58–60, 63, 88, 89, 92, 96, 97, 106, 107, 119, 123, 129, 133, 135, 141, 146, 151) of the studies. Two studies used non-randomized controlled trials (75, 102), and one was a qualitative study (93).
The clinical focus of a study was defined as either a primary mental health diagnosis, e.g., attention deficit hyperactivity disorder (ADHD) or mental health problem, e.g., suicidality. There were 11 clinical foci, as can be seen in the tree map diagram in Figure 4. The most frequent studies were about ADHD (28% (35/124)) (32–34, 36, 41, 42, 44, 50, 59–61, 65, 71–73, 79, 82, 83, 86, 88, 89, 92, 96–98, 101, 102, 105, 110, 121, 122, 125, 129, 149, 151) autism spectrum disorders (ASD) (28% (35/124)) (28, 30, 31, 35, 38, 48, 51, 55, 56, 64, 66, 68, 70, 81, 87, 90, 95, 99, 100, 104, 108, 109, 114, 120, 123, 124, 126, 127, 135, 139, 140, 142–145, 150); and depression (13% (16/124)) (45–47, 53, 54, 74, 77, 84, 111, 115, 116, 128, 133, 134, 144, 146). The fewest studies were on bipolar disorder (52), aggression (62, 75), psychosis (67, 69) and post-traumatic stress disorder (132, 138), each of which only comprised 2% of the study’s set.
Figure 4. Tree map of clinical foci in studies (n=124).
A total of 110,386 participants were studied, the count excluding publications which used the same sample more than once. Sample sizes ranged from 6 to 26,055 children and youth, with a mean sample size and standard deviation of 912 ± 3120 and a median of 110 subjects with an interquartile range of 61 to 221. It was found that 12% (15/124) of studies included more than 1,000 participants. In contrast, 10% (12/124) of studies had 20 or fewer participants.
Children were defined as those between the ages of 0 and 10 years, while youth or adolescents were defined as those between the ages of 10 and up to, but not including, 18 years. Samples were further categorized as children only 30% (37/124), youth only 19% (24/124), or mixed 51% (63/124). Girl-only populations were investigated in less than 1% (1/124) (134) of the studies, boy-only populations in 3% (4/124) (33, 36, 79, 86) and mixed-gender populations in 91% (113/124) (28–32, 34–37, 40–45, 47–64, 66–75, 77, 78, 80–85, 87–117, 119–133, 135–146, 148–151). The remaining 6% (7/124) (38, 39, 46, 65, 76, 118, 147) of studies did not adequately describe the gender composition of the populations.
Constructed from the qualitative content analysis were four categories of PCYMH research foci aiming to develop more precise diagnoses, mental health problem definition (e.g., suicidality) prognoses, or prediction of treatment response. Studies collecting data from any biological system with any method were labeled the Biomarker category and comprised (68% (84/124)) of the publications (28, 30, 32–36, 38, 41–45, 47–49, 52–57, 59–62, 64, 65, 67–75, 77, 79, 81, 83, 84, 86, 88–92, 95–97, 99–105, 108, 110–115, 119, 122, 124–126, 128, 130, 131, 134, 138, 140, 142, 143, 145, 147–149, 151).
The Non-Biological Markers category investigated potential markers consisting of specific symptoms of disorders, demographic information, or family history (without genetics data) as predictors in addition to components of the general external exposome, defined by Neufcourt, et al (152) as exposures outside the body, such as social, cultural, and ecological contexts without have specific biological effects. These constituted 17% (22/124) of the papers (46, 48, 50, 54, 82, 85, 94, 98, 109, 116, 120, 121, 123, 127, 129, 132, 137–139, 141, 147, 150).
The third most commonly used PCYMH focus was categorized as Implementation of PCYMH interventions, defined as studies of the feasibility, effectiveness, or acceptability of a novel PCYMH-driven intervention in a clinical setting. These constituted 14% (17/124) of the total dataset (29, 31, 37, 39, 40, 58, 63, 66, 76, 78, 93, 106, 107, 117, 133, 135, 146). The smallest category was Predictive Algorithms (5% (6/124)) (51, 78, 80, 87, 118, 136). Most used machine learning or other AI-assisted modelling to investigate the likelihood that an individual might develop a specific diagnosis, suicidality, or present with repeat emergency department visits.
Categories were not mutually exclusive. Several studies (4% (5/124)) (29, 48, 54, 78, 138) fell into two categories, three of which were in the Biomarker and Non-Biological Marker categories (48, 54, 138), one which was in the Implementation and Biomarker categories (29) and one which was in the Implementation and Predictive Algorithm categories (78). As a result, the percentages presented above do not necessarily add up to 100%.
Table 3 presents the sub-categories for each of the main categories of PCYMH focus. For instance, of the 84 studies investigating biomarkers, 46% (39/84) (32–36, 38, 43, 45, 47, 48, 53, 54, 59, 60, 64, 68, 69, 71, 72, 74, 75, 79, 83, 84, 86, 88, 89, 96, 101, 102, 105, 108, 110, 125, 126, 134, 142, 144, 148) examined neural biomarkers, 33% (28/84) (30, 41, 42, 44, 49, 57, 65, 67, 71–73, 77, 90, 92, 97, 103, 111–113, 115, 119, 122, 128, 130, 131, 138, 149, 151) investigated genetic biomarkers, and 8% (7/84) (30, 61, 62, 70, 131, 140, 145) investigated metabolite biomarkers. The remaining sub-categories accounted for less than 10% of the studies, including eye tracking, gut biome, peptide, protein, antibody, voice analysis, sleep, circadian rhythm, reaction time and skin conductance biomarkers. Again, the sub-categories were not mutually exclusive, and as such five studies (30, 52, 71, 72, 131) fell into multiple sub-categories.
Table 3. Content Analysis: Major types of studies and sub-categories (N = 124).
In contrast, the 22 studies investigating non-biological markers were relatively evenly distributed across the category’s composite sub-categories. Of the studies investigating non-biological markers, 68% (15/22) (46, 54, 82, 85, 94, 98, 109, 116, 121, 123, 129, 132, 137, 141, 150) investigated phenotypic, i.e. symptoms markers, 45% (10/22) (48, 50, 94, 120, 121, 127, 132, 137, 139, 147) investigated behavioral markers, and 36% (8/22) (46, 85, 94, 121, 123, 129, 132, 138) investigated sociodemographic markers. As 36% (8/22) (46, 85, 94, 121, 123, 129, 132, 137) of studies employed multiple types of non-biological markers in tandem, the percentages do not necessarily add up to 100%. Neither the Biomarker nor the Non-Biological Marker categories contained work beyond Phase 1, i.e. identifying a putative marker, in the process of marker development for clinical use (153). A list of PCYMH tools was developed using key foundational literature (19, 26). Of our study set, we found that 19% (23/124) (30, 40, 41, 57, 62, 65, 66, 70–72, 77, 90, 95, 111–113, 124, 125, 130, 133, 140, 143, 145) used -omics methods, 17% (21/124) (51, 52, 59, 61, 68, 72, 78, 79, 82, 87, 91, 99, 100, 118, 125, 127, 136, 139, 140, 148, 150) employed machine learning techniques, 8% (10/124) (39, 54–56, 59, 63, 72, 76, 79, 150) used multimodal profiling, 9% (11/124) (61, 68–70, 72, 77, 78, 82, 118, 148, 150) worked with big data, less than 3% (3/124) (52, 91, 135) employed the use of digital health data and less than1% (1/124) (61) employed virtual populations.
Discussion
To our knowledge, this is the first scoping review to comprehensively map the PCYMH literature. We retrieved publications using PCYMH and synonymous terms in the title/abstract, advancing understanding of the field’s current scope. Although several position papers and commentaries have outlined the potential of the PCYMH paradigm to improve child and youth mental health (15, 154, 155), the empirical evidence remains limited (156, 157). This is reflected in a relatively small number of eligible studies, the absence of replication, few clinical validation efforts, and a scarcity of implementation-focused PCYMH research. Even the CYMH diagnosis categories with the highest number of articles (ADHD and ASD) only had 1–3 papers about each biomarker or non-biological marker and few implementation studies. Finally, none of the included studies used a reporting guideline, highlighting the lack of standardization across this emerging field.
The recent explosion in publications (49% since 2020) indicates the field is just coming into its own, but the dominance of biomarker studies (67%) compared to the scarcity of implementation studies (14%) tells us that the focus remains on basic discovery rather than clinical application. These features, in combination with only ten studies using individualized multimodal profiles in PCYMH research or care, and the absence of reporting guidelines, leads us to conclude that PCYMH research is in the infancy of its development, with great potential, with many hopes still unrealized.
We know of no other PCYMH reviews with which to compare our results. However, adult precision MH research does appear to be further along. For example, in a systematic review of research through 2019 about precision health or medicine in adults with myriad mental health conditions such as psychotic, mood, anxiety, and substance use disorders, Salazar de Pablo et al. (158) were able to identify 584 prediction modeling studies, estimating individualized risks for diagnosis, prognosis, or treatment response. We suspect that the field of PCYMH may just need more time, as in our dataset, the majority of studies weren’t published until after 2015 (and most in the last five years), when President Obama declared the beginning of the precision medicine era (159).
Another comparator is precision medicine or health research in the non-CYMH pediatric population. Here we also find a more well-developed corpus of work, especially studies identifying molecular targets for treatment in pediatric cancer (160), cardiac disorders (161), and rare diseases (162).
Why isn’t PCYMH research as advanced as precision research in other pediatric disorders? We speculate that PCYMH, like previous research in CYMH diagnosis, treatment, and prevention, is particularly difficult because causation is complex and non-singular (163). Complicating this issue is the transmutation of symptoms and problems within the context of evolving child and youth development (164, 165). Precision health research in non-CYMH pediatric disorders also wrestles with constant change in the individual from normal development, but most pediatric medical disorders have objective markers of pathology, the lack of which has always been a major challenge in CYMH research and care. However, the tools now available in precision health are, for the first time, enabling biological, lifestyle, and environmental discoveries about psychopathology beyond the description of symptoms and behaviours, giving reason for optimism about the future transformational power of PCYMH (154).
In addition, the effects of social determinants of health, including exposure to adverse events, makes the development of PCYMH more complicated (166). It is now accepted that these determinants derail normal child and adolescent development and have consequences well into adulthood, but their effects on CYMH problems are particularly severe and consequential for an individual’s entire life (167). Furthermore, correlates or possible etiologic factors such as poverty, abuse, neglect, or out of home placement are complicated to “treat” and not under the control of any one societal system, including health care.
Precision mental health or psychiatry has been touted as the next scientific revolution (154, 168), but potential barriers to widespread operationalization and uptake are significant, including possible psychological harm to patients, unknown economic consequences, potential increases in mental healthcare disparities, failure to deliver on promises of increased treatment success, and inadequately trained clinical staff, with poor integration of research into care (169).
However, if PCYMH is in its infancy, this is the ideal time to shape this body of research to facilitate maximal opportunity for success. First, standardization of how PCYMH research is planned, conducted, and reported would enhance the quality of studies and enhance inter-study comparison, allowing better of results from multiple studies. The first reporting guideline was published in 1996 (170) and this line of work has steadily expanded in scope and impact. Using such guidelines could have obviated publication of the papers we found with inadequate information on samples and methods.
Second, while developing studies, it is always important to consider and mitigate biases. However, in the context of PCYMH, where the main objective is typically to identify sub-groups needing different diagnosis, treatments or prevention interventions than the “the average” research participant or patient, issues such as selection bias run the risk of significantly undermining the validity of any observations made. Studies using AI, e.g., various types of machine learning to make relevant clinical predictions, are particularly vulnerable to myriad biases (171). Since these studies have the potential to have a large impact on care, identifying and mitigating AI-related bias is particularly important. Evidence-based medicine has significantly advanced CYMH research and it remains relevant and essential for translating PCYMH research findings into real-world impact for children and youth (17). AI-generated algorithms, biomarker, and non-biomarker studies all need to be replicated and validated and a large proportion of this work requires evidence-based medicine methods. Very few studies in our dataset have progressed beyond identification of markers or early development of Ai generated predictive algorithms and this dearth of such studies will undermine advancement of PCYMH (172). Furthermore, validation of algorithms, biomarkers, and non-biological markers by clinicians and persons with lived experience who represent the population of interest, whether a clinical condition or the community, is critical to close the standard multi-year gap between research discoveries and impact. PCYMH will not succeed if results do not penetrate clinical care settings or the community.
Evaluating the implementation and outcomes of PCYMH interventions—both in care and prevention—is essential. There was only one study that did this for a PCYMH care intervention feasibility study (39). If there were more such studies, the knowledge mobilization rate would increase and sharing of ideas and data could significantly advance the field.
Our data show that PCYMH tools are not yet being used to full capacity. Some studies aimed to answer PCYMH questions, but they used no PCYMH tools at all, relying on more traditional designs or data analytic methods. Neither were most studies yet at the stage of using multiple types of data, e.g. neuroimaging, genetics, wearable digital health cardiovascular function information, and lifestyle characteristics to develop the individualized multimodal profiles crucial to administering tailored mental health care. The five studies (29, 48, 54, 78, 138) falling into two categories of types of PCYMH research may be harbingers of future research in which studies will routinely characterize individuals using multiple types of data.
A goal of our review was to determine if there were any diagnostic or MH problem groups for which there was adequate PCYMH research to conduct a systematic review or meta-analysis. Unfortunately, even for ASD and ADHD, the two disorders with the largest number of articles, there too few papers about any specific PCYMH sub-category or tool to proceed to more comprehensive types of reviews.
Strengths and limitations
This scoping review has several strengths. Its broad, but structured guiding question and the comprehensive analysis of studies provide a valuable “snapshot” of the current PCYMH research landscape. By mapping the existing literature, the review offers an overview that can inform future, more focused inquiries.
There are also three limitations. First, restricting inclusion to English-language publications introduces the potential for language bias, with possible exclusion of useful non-English papers. Second, using two databases, PubMed and Embase, while appropriate for a scoping review, may have resulted in missing relevant publications indexed in other databases, e.g., PsycInfo. In addition, exclusion of the grey literature, a decision made because it would not answer our core question about published literature, means that some emerging research or practice-based knowledge may not have been discovered. Future reviews, whether scoping or systematic, should ensure interrogation of these other sources.
Finally, as with all scoping reviews, the intent was to map and describe rather than to evaluate the quality or strength of evidence. This approach is valuable for the first high-level analysis of a scoping review, but limits the certainty with which conclusions can be drawn.
Conclusions
This review shows that PCYMH research, while still in its infancy, has made rapid progress between 2020 and 2024. Among all the publications, there were four types of PCYMH research: (1) Biomarker; (2) Non-Biological Marker; (3) Implementation of PCYMH Interventions: and (4) Predictive Algorithms studies. This corpus of research investigated eleven CYMH diagnoses or problems, the latter most prominently represented by studies on suicidality and self-harm. The current state of knowledge and implementation of the PCYMH paradigm is primed for improvement in the depth and breadth of studies, sharpening the focus to fill gaps in the discovery process, and improving knowledge mobilization.
Recommendations for future PCYMH research
We recommend the following changes in PCYMH research to advance the paradigm from early development to practice.
1. The clinical foci of this body of work were dominated by studies of ASD and ADHD. While important CYMH diagnoses, we recommend that the clinical scope of PCYMH research be expanded to include anxiety and mood disorders. Like ASD and ADHD, these clinical designations identify heterogeneous groups of children and youth, which could be improved with the PCYMH paradigm. Expanding the body of PCYMH work to include these two could have a tremendous impact on the CYMH population, as these disorders affect 20-25% of 3-17-year-olds worldwide (173, 174).
2. We found gaps in the specific types of PCYMH research. For example, of the four phases of biomarker development (identification, verification, evaluation against a gold standard, and testing in a clinical setting) (153), the PCYMH biomarker research primarily is in the early phases. We recommend that researchers continue to advance their work further along the well-described phases of validation and clinical care pathway developments, with large enough samples to detect sub-group-based differences informing PCYMH usage.
3. PCYMH research about Non-Biological Markers could benefit from using a similar structure as biomarker development (recommendation #2), where phases instead capture functional, social, or psychological outcomes rather than biological ones.
4. Implementation of PCYMH intervention studies should be driven by implementation science methodology. While many healthcare-based, implementation science frameworks do not directly address precision medicine, Mogagka and colleagues (175) synthesized the four most commonly used frameworks and aligned the constructs with the tenets of precision medicine to create a precision medicine implementation framework. Future PCYMH researchers may find this helpful.
5. The Predictive Algorithms PCYMH focus category also provides opportunities for improvement, especially since we expect increased growth in AI in this category. None of the studies we found reported an AI bias analysis in the project design, which can be of considerable concern with AI research. We recommend structured analysis of possible biases, accompanied by prevention or mitigation strategies while developing study plans for PCYMH research (176, 177) or when carrying out prediction studies (171).
6. Plan PCYMH programs of research that can produce multimodal profiles of individuals, i.e., biological and non-biological predictors for diagnosis, treatment response, prognosis, or prevention.
7. Given that privacy and ethics concerns (178), as well as clinician skepticism (169) abound in PCYMH research, we recommend research teams include clinicians and persons with lived experience from the design stage throughout a study to optimize research uptake and validity.
8. Use reporting guidelines in study planning and to increase knowledge mobilization of PCYMH research findings. Examples of such reporting guidelines are: the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) (179), the Better Precision-data Reporting of Evidence from Clinical Intervention Studies & Epidemiology (BePRECISE) (180), and the Generative Artificial intelligence tools in MEdical Research (GAMER) (181).
9. Incorporate evaluation of implementation outcomes in all PCYMH care or prevention studies and ensure widespread knowledge mobilization through publication and conference presentation of these evaluations.
Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author/s.
Author contributions
JL: Writing – original draft, Writing – review & editing, Conceptualization, Investigation, Methodology, Validation, Formal analysis, Data curation. AnB: Formal analysis, Data curation, Methodology, Writing – review & editing. PC: Writing – review & editing, Methodology, Data curation. AdB: Writing – review & editing, Methodology, Data curation. AD: Methodology, Writing – review & editing, Data curation. AH: Data curation, Methodology, Writing – review & editing. TT: Data curation, Writing – review & editing, Methodology. EK-A: Writing – review & editing, Methodology, Data curation. CH: Writing – review & editing, Methodology, Data curation. KP: Data curation, Methodology, Investigation, Validation, Conceptualization, Supervision, Writing – review & editing, Resources, Project administration, Writing – original draft, Formal analysis, Funding acquisition.
Funding
The author(s) declared that financial support was received for this work and/or its publication. This study was funded by the CHEO RI Precision Child and Youth Mental Health (PCYMH) Collaboratory, supported by a generous donation from Waverley House to the CHEO and SickKids Foundations. Neither the donor nor the Foundations influenced any part of this study.
Acknowledgments
We would like to express our appreciation to Lisa Shaver, CHEO’s in-house librarian who assisted with the development of the initial search strategy and Dr. Clare Gray, who assisted with the title and abstract screening process.
Conflict of interest
The authors declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1691548/full#supplementary-material
References
1. Mental health of adolescents. Geneva, Switzerland: World Health Organization (2024). Available online at: https://www.who.int/news-room/fact-sheets/detail/adolescent-mental-health.
2. Canadian Institute for Health Information. Child and Youth Mental Health (2025). Available online at: https://www.cihi.ca/en/child-and-youth-mental-health (Accessed July 24, 2025).
3. Hossain MM, Nesa F, Das J, Aggad R, Tasnim S, Bairwa M, et al. Global burden of mental health problems among children and adolescents during COVID-19 pandemic: An umbrella review. Psychiatry Res. (2022) 317:114814-114814. doi: 10.1016/j.psychres.2022.114814
4. Magson NR, Freeman JYA, Rapee RM, Richardson CE, Oar EL, and Fardouly J. Risk and protective factors for prospective changes in adolescent mental health during the COVID-19 pandemic. J Youth Adolescence. (2021) 50:44–57. doi: 10.1007/s10964-020-01332-9
5. Stubbing J, Rihari T, Bardsley A, and Gluckman P. Exploring Factors Influencing Youth Mental Health: What We Know and Don’t Know about the Determinants of Young People’s Mental Health. Auckland (NZ): Koi Tū Centre for Informed Futures (2023). Available online at: https://hdl.handle.net/2292/66784 (Accessed July 24, 2025).
6. Gallant C and Good D. Mental health complexity among children and youth: current conceptualizations and future directions. Can J Community Ment Health. (2023) 42:1–12. doi: 10.7870/cjcmh-2023-018
7. Beaudry G, Drouin O, Gravel J, Smyrnova A, Bender A, Orri M, et al. A comparative analysis of pediatric mental health-related emergency department utilization in Montréal, Canada, before and during the COVID-19 pandemic. Ann Gen Psychiatry. (2022) 21:7–10. doi: 10.1186/s12991-022-00398-y
8. Brattfjell ML, Jozefiak T, Lydersen S, and Wichstrøm L. Service use as a predictor of change in mental health problems among children: A prospective cohort study. Psychiatr Services. (2023) 74:1256–62. doi: 10.1176/appi.ps.20220079
9. Wilson R, Jennings A, Redaniel MT, Samarakoon K, Dawson S, Lyttle MD, et al. Factors associated with repeat emergency department visits for mental health care in adolescents: A scoping review. Am J Emergency Med. (2024) 81:23–34. doi: 10.1016/j.ajem.2024.04.018
10. Rosic T, Duncan L, Wang L, Eltorki M, Boyle M, Sassi R, et al. Trends and predictors of repeat mental health visits to a pediatric emergency department in hamilton, ontario. J Can Acad Child Adolesc Psychiatry. (2019) 28:82. Available online at: https://pmc.ncbi.nlm.nih.gov/articles/PMC6691794/.
11. Cushing AM, Liberman DB, Pham PK, Michelson KA, Festekjian A, Chang TP, et al. Mental health revisits at US pediatric emergency departments. JAMA Pediatrics. (2023) 177:168–76. doi: 10.1001/jamapediatrics.2022.4885
12. Radez J, Reardon T, Creswell C, Lawrence PJ, Evdoka-Burton G, and Waite P. Why do children and adolescents (not) seek and access professional help for their mental health problems? A systematic review of quantitative and qualitative studies. Eur Child Adolesc Psychiatry. (2021) 30:183–211. doi: 10.1007/s00787-019-01469-4
13. Bickman L, Wrighton LG, Lambert EW, Karver MS, and Steding L. Problems in using diagnosis in child and adolescent mental health services research. J Methods Measurement Soc Sci. (2012) 3:1–26. doi: 10.2458/jmm.v3i1.16110
14. Insel TR. Mental disorders in childhood: shifting the focus from behavioral symptoms to neurodevelopmental trajectories. JAMA. (2014) 311:1727–8. doi: 10.1001/jama.2014.1193
15. Bickman L. Improving mental health services: A 50-year journey from randomized experiments to artificial intelligence and precision mental health. Administration Policy Ment Health Ment Health Serv Res. (2020) 47:795–843. doi: 10.1007/s10488-020-01065-8
16. Leikauf JE. Precision psychiatry implementation in child and adolescent psychiatric clinical practice: opportunities and challenges. Psychiatr Annals. (2024) 54:e108–12. doi: 10.3928/00485713-20240314-01
17. Szatmari P and Susser E. Being precise about precision mental health. JAMA Psychiatry. (2022) 79:1149–50. doi: 10.1001/jamapsychiatry.2022.3391
18. Blackstone EH. Precision medicine versus evidence-based medicine: individual treatment effect versus average treatment effect. Circulation. (2019) 140:1236–8. doi: 10.1161/CIRCULATIONAHA.119.043014
19. Naithani N, Sinha S, Misra P, Vasudevan B, and Sahu R. Precision medicine: Concept and tools. Med J Armed Forces India. (2021) 77:249–57. doi: 10.1016/j.mjafi.2021.06.021
20. Pajer K, Honeywell C, Howley H, Sheridan N, Affleck W, Terekhov I, et al. Participatory logic model for a precision child and youth mental health start-up: scoping review, case study, and lessons learned. Front Health Services. (2024) 4:1405426. doi: 10.3389/frhs.2024.1405426
21. Clausen C, Leventhal B, Nytrø Ø, Koposov R, Røst TB, Westbye OS, et al. Usability of the IDDEAS prototype in child and adolescent mental health services: A qualitative study for clinical decision support system development. Front Psychiatry. (2023) 14:1033724. doi: 10.3389/fpsyt.2023.1033724
22. Røst TB, Clausen C, Nytrø O, Koposov R, Leventhal B, Westbye OS, et al. Local, early, and precise: designing a clinical decision support system for child and adolescent mental health services. Front Psychiatry. (2020) 11:564205. doi: 10.3389/fpsyt.2020.564205
23. Peters MDJ, Marnie C, Tricco AC, Pollock D, Munn Z, Alexander L, et al. Updated methodological guidance for the conduct of scoping reviews. JBI Evidence Synthesis. (2020) 18:2119–26. doi: 10.11124/jbies-20-00167
24. Perlman RL, Govindaraju DR, and Archibald E. Garrod: The father of precision medicine. Genet Med. (2016) 18:1088–9. doi: 10.1038/gim.2016.5
25. Covidence systematic review software. In: Veritas Health Innovation. (Veritas Health Innovation, Melbourne, Australia) Available online at: www.covidence.org.
26. Basharat S, Smith A, Darvesh N, and Rader T. Watch List: Top 10 Precision Medicine Technologies and Issues: CADTH Horizon Scan [Internet]. Ottawa (ON): Canadian Agency for Drugs and Technologies in Health. (2023) Mar). Available online at: https://www.ncbi.nlm.nih.gov/books/NBK596300/ (Accessed Jul 24, 2025).
27. Potter WJ and Levine-Donnerstein D. Rethinking validity and reliability in content analysis. J Appl Communication Res. (1999) 27:258–84. doi: 10.1080/00909889909365539
28. Adams JB, Johansen LJ, Powell LD, Quig D, and Rubin RA. Gastrointestinal flora and gastrointestinal status in children with autism – comparisons to typical children and correlation with autism severity. BMC Gastroenterology. (2011) 11:22. doi: 10.1186/1471-230x-11-22
29. Aggensteiner P, Bottinger B, Baumeister S, Hohmann S, Heintz S, Kaiser A, et al. Randomized controlled trial of individualized arousal-biofeedback for children and adolescents with disruptive behavior disorders (DBD). Eur Child Adolesc Psychiatry. (2024) 33:3055–66. doi: 10.1007/s00787-023-02368-5
30. Al-Ali Z, Yasseen AA, Al-Dujailli A, Al-Karaqully AJ, McAllister KA, and Jumaah AS. The oxytocin receptor gene polymorphism rs2268491 and serum oxytocin alterations are indicative of autism spectrum disorder: A case-control paediatric study in Iraq with personalized medicine implications. PloS One. (2022) 17:e0265217. doi: 10.1371/journal.pone.0265217
31. Almirall D, DiStefano C, Chang YC, Shire S, Kaiser A, Lu X, et al. Longitudinal effects of adaptive interventions with a speech-generating device in minimally verbal children with ASD. J Clin Child Adolesc Psychol. (2016) 45:442–56. doi: 10.1080/15374416.2016.1138407
32. Arnett AB, Rutter TM, and Stein MA. Neural markers of methylphenidate response in children with attention deficit hyperactivity disorder. Front Behav Neurosci. (2022) 16:887622. doi: 10.3389/fnbeh.2022.887622
33. Arns M, Gunkelman J, Breteler M, and Spronk D. EEG phenotypes predict treatment outcome to stimulants in children with ADHD. J Integr Neurosci. (2008) 07:421–38. doi: 10.1142/S0219635208001897
34. Arns M, Vollebregt MA, Palmer D, Spooner C, Gordon E, Kohn M, et al. Electroencephalographic biomarkers as predictors of methylphenidate response in attention-deficit/hyperactivity disorder. Eur Neuropsychopharmacol. (2018) 28:881–91. doi: 10.1016/j.euroneuro.2018.06.002
35. Baker E, Veytsman E, Choy T, Blacher J, and Stavropoulos KKM. Investigating changes in reward-related neural correlates after PEERS intervention in adolescents with ASD: preliminary evidence of a “Precision medicine” Approach. Front Psychiatry. (2021) 12:742280. doi: 10.3389/fpsyt.2021.742280
36. Bazanova OM, Auer T, and Sapina EA. On the efficiency of individualized theta/beta ratio neurofeedback combined with forehead EMG training in ADHD children. Front Hum Neurosci. (2018) 12:3. doi: 10.3389/fnhum.2018.00003
37. Beidas RS, Lindhiem O, Brodman DM, Swan A, Carper M, Cummings C, et al. A probabilistic and individualized approach for predicting treatment gains: an extension and application to anxiety disordered youth. Behav Ther. (2014) 45:126–36. doi: 10.1016/j.beth.2013.05.001
38. Bernas A, Aldenkamp AP, and Zinger S. Wavelet coherence-based classifier: A resting-state functional MRI study on neurodynamics in adolescents with high-functioning autism. Comput Methods Programs Biomedicine. (2018) 154:143–51. doi: 10.1016/j.cmpb.2017.11.017
39. Blais A, Holahan AL, Helleman A, Pajer K, Honeywell C, Salehi R, et al. Using neuropsychological profiling to tailor mental health care for children and youth: a quality improvement project to measure feasibility. Arch Clin Neuropsychology. (2025) 40:394–408. doi: 10.1093/arclin/acae087
40. Blasco-Fontecilla H. Clinical utility of pharmacogenetic testing in children and adolescents with severe mental disorders. J Neural Transmission. (2019) 126:101–7. doi: 10.1007/s00702-018-1882-4
41. Brown J, Abdel-Rahman S, Van Haandel L, Gaedigk A, Lin Y, and Leeder J. Single dose, CYP2D6 genotype-stratified pharmacokinetic study of atomoxetine in children with ADHD. Clin Pharmacol Ther. (2016) 99:642–50. doi: 10.1002/cpt.319
42. Bruxel EM, Salatino-Oliveira A, Genro JP, Zeni CP, Polanczyk GV, Chazan R, et al. Association of a carboxylesterase 1 polymorphism with appetite reduction in children and adolescents with attention-deficit/hyperactivity disorder treated with methylphenidate. Pharmacogenomics J. (2013) 13:476–80. doi: 10.1038/tpj.2012.25
43. Cardinale EM, Bezek J, Morales S, Filippi C, Smith AR, Haller S, et al. Cross-sectional and longitudinal associations of anxiety and irritability with adolescents’ Neural responses to cognitive conflict. Biol Psychiatry: Cogn Neurosci Neuroimaging. (2023) 8:436–44. doi: 10.1016/j.bpsc.2022.03.007
44. Carpentieri V, Lambacher G, Troianiello M, Pucci M, Di Pietro D, Laviola G, et al. Methylation dynamics on 5’-UTR of DAT1 gene as a bio-marker to recognize therapy success in ADHD children. Children. (2023) 10:584. doi: 10.3390/children10030584
45. Connolly CG, Ho TC, Blom EH, LeWinn KZ, Sacchet MD, Tymofiyeva O, et al. Resting-state functional connectivity of the amygdala and longitudinal changes in depression severity in adolescent depression. J Affect Disord. (2017) 207:86–94. doi: 10.1016/j.jad.2016.09.026
46. Curry J, Rohde P, Simons A, Silva S, Vitiello B, Kratochvil C, et al. Predictors and moderators of acute outcome in the treatment for adolescents with depression study (TADS). J Am Acad Child Adolesc Psychiatry. (2006) 45:1427–39. doi: 10.1097/01.chi.0000240838.78984.e2
47. Doruk Camsari D, Lewis CP, Sonmez AI, Nandakumar AL, Gresbrink MA, Daskalakis ZJ, et al. Transcranial magnetic stimulation markers of antidepressant treatment in adolescents with major depressive disorder. Int J Neuropsychopharmacol. (2019) 22:435–44. doi: 10.1093/ijnp/pyz021
48. Edmunds SR, MacNaughton GA, Rueda MR, Combita LM, and Faja S. Beyond group differences: Exploring the preliminary signals of target engagement of an executive function training for autistic children. Autism Res. (2022) 15:1261–73. doi: 10.1002/aur.2735
49. Efstathopoulos P, Andersson F, Melas PA, Yang LL, Villaescusa JC, Rűegg J, et al. NR3C1 hypermethylation in depressed and bullied adolescents. Trans Psychiatry. (2018) 8:121. doi: 10.1038/s41398-018-0169-8
50. Elahi H, Iosif AM, Mukherjee P, Hinshaw SP, and Schweitzer JB. Using hot and cool measures to phenotype and predict functional outcomes across dimensions of ADHD and typical development in adolescents. Res Child Adolesc Psychopathology. (2024) 52:579–93. doi: 10.1007/s10802-023-01149-7
51. Eni M, Dinstein I, Ilan M, Menashe I, Meiri G, and Zigel Y. Estimating autism severity in young children from speech signals using a deep neural network. IEEE Access. (2020) 8:139489–500. doi: 10.21037/pm-20-78
52. Faedda GL, Ohashi K, Hernandez M, McGreenery CE, Grant MC, Baroni A, et al. Actigraph measures discriminate pediatric bipolar disorder from attention-deficit/hyperactivity disorder and typically developing controls. J Child Psychol Psychiatry. (2016) 57:706–16. doi: 10.1111/jcpp.12520
53. Forbes EE, Olino TM, Ryan ND, Birmaher B, Axelson D, Moyles DL, et al. Reward-related brain function as a predictor of treatment response in adolescents with major depressive disorder. Cognitive Affective Behav Neurosci. (2010) 10:107–18. doi: 10.3758/CABN.10.1.107
54. Ford SH, Bruckner L, Thoyre S, Baker MJ, Bartlett TR, and Hodges EA. Gut-brain axis perspective on negative symptoms and their neighbors in early adolescence: can we move care upstream? J Psychosocial Nurs Ment Health. (2023) 61:29–38. doi: 10.3928/02793695-20230221-03
55. Frazier TW, Klingemier EW, Beukemann M, Speer L, Markowitz L, Parikh S, et al. Development of an objective autism risk index using remote eye tracking. J Am Acad Child Adolesc Psychiatry. (2016) 55:301–9. doi: 10.1016/j.jaac.2016.01.011
56. Frazier TW, Klingemier EW, Parikh S, Speer L, Strauss MS, Eng C, et al. Development and validation of objective and quantitative eye tracking–based measures of autism risk and symptom levels. J Am Acad Child Adolesc Psychiatry. (2018) 57:858–66. doi: 10.1016/j.jaac.2018.06.023
57. Gassó P, Rodríguez N, Mas S, Pagerols M, Blázquez A, Plana MT, et al. Effect of CYP2D6, CYP2C9 and ABCB1 genotypes on fluoxetine plasma concentrations and clinical improvement in children and adolescent patients. Pharmacogenomics J. (2014) 14:457–62. doi: 10.1038/tpj.2014.12
58. Gewirtz AH, Lee SS, August GJ, and He Y. Does giving parents their choice of interventions for child behavior problems improve child outcomes? Prev Sci. (2019) 20:78–88. doi: 10.1007/s11121-018-0865-x
59. Ging-Jehli NR, Kraemer HC, Eugene Arnold L, Roley-Roberts ME, and deBeus R. Cognitive markers for efficacy of neurofeedback for attention-deficit hyperactivity disorder - personalized medicine using computational psychiatry in a randomized clinical trial. J Clin Exp Neuropsychology. (2023) 45:118–31. doi: 10.1080/13803395.2023.2206637
60. Griffiths KR, Jurigova BG, Leikauf JE, Palmer D, Clarke SD, Tsang TW, et al. A signature of attention-elicited electrocortical activity distinguishes response from non-response to the non-stimulant atomoxetine in children and adolescents with ADHD. J Attention Disord. (2019) 23:744–53. doi: 10.1177/1087054717733044
61. Gutiérrez-Casares JR, Quintero J, Jorba G, Junet V, Martínez V, Pozo-Rubio T, et al. Methods to develop an in silico clinical trial: computational head-to-head comparison of lisdexamfetamine and methylphenidate. Front Psychiatry. (2021) 12:741170. doi: 10.3389/fpsyt.2021.741170
62. Hagenbeek FA, Roetman PJ, Pool R, Kluft C, Harms AC, van Dongen J, et al. Urinary amine and organic acid metabolites evaluated as markers for childhood aggression: the ACTION biomarker study. Front Psychiatry. (2020) 11:165. doi: 10.3389/fpsyt.2020.00165
63. Hautmann C, Dose C, Hellmich M, Scholz K, Katzmann J, Pinior J, et al. Behavioural and nondirective parent training for children with externalising disorders: First steps towards personalised treatment recommendations. Behav Res Ther. (2023) 163:104271. doi: 10.1016/j.brat.2023.104271
64. Hegarty JP, Gengoux GW, Berquist KL, Millán ME, Tamura SM, Karve S, et al. A pilot investigation of neuroimaging predictors for the benefits from pivotal response treatment for children with autism. J Psychiatr Res. (2019) 111:140–4. doi: 10.1016/j.jpsychires.2019.02.001
65. Hong SB, Kim JW, Cho SC, Shin MS, Kim BN, and Yoo HJ. Dopaminergic and noradrenergic gene polymorphisms and response to methylphenidate in korean children with attention-deficit/hyperactivity disorder: is there an interaction? J Child Adolesc Psychopharmacol. (2012) 22:343–52. doi: 10.1089/cap.2011.0076
66. Huang J, Liu J, Tian R, Liu K, Zhuang P, Sherman HT, et al. A next generation sequencing-based protocol for screening of variants of concern in autism spectrum disorder. Cells. (2021) 11:10. doi: 10.3390/cells11010010
67. Ivashchenko DV, Khoang SZ, Makhmudova BV, Buromskaya NI, Shimanov PV, Deitch RV, et al. Pharmacogenetics of antipsychotics in adolescents with acute psychotic episode during first 14 days after admission: effectiveness and safety evaluation. Drug Metab Personalized Ther. (2020) 35:24xߝ31. doi: 10.1515/dmpt-2020-0102
68. Jiang X, Shou XJ, Zhao Z, Chen Y, Meng FC, Le J, et al. A brain structural connectivity biomarker for autism spectrum disorder diagnosis in early childhood. Psychoradiology. (2023) 3:kkad005. doi: 10.1093/psyrad/kkad005
69. Karcher NR, Modi H, Kochunov P, Gao S, and Barch DM. Regional vulnerability indices in youth with persistent and distressing psychoticlike experiences. JAMA Network. (2023) 6:e2343081. doi: 10.1001/jamanetworkopen.2023.43081
70. Kelly R, Boulin A, Laranjo N, Lee-Sarwar K, Chu SH, Yadama AP, et al. Metabolomics and communication skills development in children; evidence from the ages and stages questionnaire. Metabolites. (2019) 9:42. doi: 10.3390/metabo9030042
71. Kim BN, Cummins TDR, Kim JW, Bellgrove MA, Hong SB, Song SH, et al. Val/Val genotype of brain-derived neurotrophic factor (BDNF) Val66Met polymorphism is associated with a better response to OROS-MPH in Korean ADHD children. Int J Neuropsychopharmacol. (2011) 14:1399–410. doi: 10.1017/S146114571100099X
72. Kim JW, Sharma V, and Ryan ND. Predicting methylphenidate response in ADHD using machine learning approaches. Int J Neuropsychopharmacol. (2015) 18:pyv052. doi: 10.1093/ijnp/pyv052
73. Kirley A, Lowe N, Hawi Z, Mullins C, Daly G, Waldman I, et al. Association of the 480 bp DAT1 allele with methylphenidate response in a sample of Irish children with ADHD. Am J Med Genet Part B Neuropsychiatr Genet. (2003) 121B:50–4. doi: 10.1002/ajmg.b.20071
74. Klimes-Dougan B, Başgöze Z, Mueller B, Wiglesworth A, Carosella KA, Westlund Schreiner M, et al. Structural and functional neural correlates of treatment response for interpersonal psychotherapy for depressed adolescents. J Clin Med. (2022) 11:1878. doi: 10.3390/jcm11071878
75. Klimes-Dougan B, Klingbeil DA, Houri A, Cullen KR, Gunlicks-Stoessel M, and August G. A pilot study of stress system activation in children enrolled in a targeted prevention program: implications for personalization. Int J Mol Sci. (2018) 19:361. doi: 10.3390/ijms19020361
76. Kuehn KS, Foster KT, Czyz EK, and King CA. Identifying person-specific coping responses to suicidal urges: A case series analysis and illustration of the idiographic method. Suicide Life-Threatening Behavior. (2022) 52:490–9. doi: 10.1111/sltb.12841
77. Kurkinen K, Kärkkäinen O, Lehto SM, Luoma I, Kraav SL, Kivimäki P, et al. The associations between metabolic profiles and sexual and physical abuse in depressed adolescent psychiatric outpatients: an exploratory pilot study. Eur J Psychotraumatology. (2023) 14:2191396. doi: 10.1080/20008066.2023.2191396
78. Kusuma K, Larsen M, Quiroz JC, and Torok M. Age-stratified predictions of suicide attempts using machine learning in middle and late adolescence. J Affect Disord. (2024) 365:126–33. doi: 10.1016/j.jad.2024.08.043
79. Kyeong S, Kim JJ, and Kim E. Novel subgroups of attention-deficit/hyperactivity disorder identified by topological data analysis and their functional network modular organizations. PloS One. (2017) 12:e0182603. doi: 10.1371/journal.pone.0182603
80. Lamb R, Firestone J, Kavner A, Almusharraf N, Choi I, Owens T, et al. Machine learning prediction of mental health strategy selection in school aged children using neurocognitive data. Comput Hum Behavior. (2024) 156:108197. doi: 10.1016/j.chb.2024.108197
81. Latrèche K, Kojovic N, Franchini M, and Schaer M. Attention to face as a predictor of developmental change and treatment outcome in young children with autism spectrum disorder. Biomedicines. (2021) 9:942. doi: 10.3390/biomedicines9080942
82. Lavigne JV, Hopkins J, Ballard RJ, Gouze KR, Ariza AA, and Martin CP. A precision mental health model for predicting stability of 4-year-olds’ ADHD symptoms to age 6 diagnostic status. Acad Pediatrics. (2024) 24:433–41. doi: 10.1016/j.acap.2023.09.003
83. Lee KS, Xiao J, Luo J, Leibenluft E, Liew Z, and Tseng WL. Characterizing the neural correlates of response inhibition and error processing in children with symptoms of irritability and/or attention-deficit/hyperactivity disorder in the ABCD study. Front Psychiatry. (2022) 13:803891. doi: 10.3389/fpsyt.2022.803891
84. Lewis CP, Nakonezny PA, Ameis SH, Vande Voort JL, Husain MM, Emslie GJ, et al. Cortical inhibitory and excitatory correlates of depression severity in children and adolescents. J Affect Disord. (2016) 190:566–75. doi: 10.1016/j.jad.2015.10.020
85. Li M, D’Arcy C, and Meng X. Predictors of functional improvement in children and adolescents at a publicly funded specialist outpatient treatment clinic in a Canadian Prairie City. Psychiatry Res. (2019) 273:613–23. doi: 10.1016/j.psychres.2019.01.093
86. Lim L, Marquand A, Cubillo AA, Smith AB, Chantiluke K, Simmons A, et al. Disorder-specific predictive classification of adolescents with attention deficit hyperactivity disorder (ADHD) relative to autism using structural magnetic resonance imaging. PloS One. (2013) 8:e63660. doi: 10.1371/journal.pone.0063660
87. Liu W, Li M, and Yi L. Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework. Autism Res. (2016) 9:888–98. doi: 10.1002/aur.1615
88. Loo SK, Bilder RM, Cho AL, Sturm A, Cowen J, Walshaw P, et al. Effects of d-methylphenidate, guanfacine, and their combination on electroencephalogram resting state spectral power in attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry. (2016) 55:674–682.e1. doi: 10.1016/j.jaac.2016.04.020
89. Loo SK, Salgari GC, Ellis A, Cowen J, Dillon A, and McGough JJ. Trigeminal nerve stimulation for attention-deficit/hyperactivity disorder: cognitive and electroencephalographic predictors of treatment response. J Am Acad Child Adolesc Psychiatry. (2021) 60:856–864.e1. doi: 10.1016/j.jaac.2020.09.021
90. Mahjani B, De Rubeis S, Gustavsson Mahjani C, Mulhern M, Xu X, Klei L, et al. Prevalence and phenotypic impact of rare potentially damaging variants in autism spectrum disorder. Mol Autism. (2021) 12:65. doi: 10.1186/s13229-021-00465-3
91. McGinnis RS, McGinnis EW, Hruschak J, Lopez-Duran NL, Fitzgerald K, Rosenblum KL, et al. Rapid detection of internalizing diagnosis in young children enabled by wearable sensors and machine learning. PloS One. (2019) 14:e0210267. doi: 10.1371/journal.pone.0210267
92. McGough J, McCracken J, Swanson J, Riddle M, Kollins S, Greenhill L, et al. Pharmacogenetics of methylphenidate response in preschoolers with ADHD. J Am Acad Child Adolesc Psychiatry. (2006) 45:1314–22. doi: 10.1097/01.chi.0000235083.40285.08
93. McKay K, Kennedy E, Senior R, Scott S, Hill J, Doolan M, et al. Informing the personalisation of interventions for parents of children with conduct problems: a qualitative study. BMC Psychiatry. (2020) 20:513. doi: 10.1186/s12888-020-02917-1
94. Meehan AJ, Latham RM, Arseneault L, Stahl D, Fisher HL, and Danese A. Developing an individualized risk calculator for psychopathology among young people victimized during childhood: A population-representative cohort study. J Affect Disord. (2020) 262:90–8. doi: 10.1016/j.jad.2019.10.034
95. Meng W, Huan Y, and Gao Y. Urinary proteome profiling for children with autism using data-independent acquisition proteomics. Trans Pediatrics. (2021) 10:1765–78. doi: 10.21037/tp-21-193
96. Michelini G, Lenartowicz A, Vera JD, Bilder RM, McGough JJ, McCracken JT, et al. Electrophysiological and clinical predictors of methylphenidate, guanfacine, and combined treatment outcomes in children with attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry. (2023) 62:415–26. doi: 10.1016/j.jaac.2022.08.001
97. Michelson D, Read HA, Ruff DD, Witcher J, Zhang S, and McCracken J. CYP2D6 and clinical response to atomoxetine in children and adolescents with ADHD. J Am Acad Child Adolesc Psychiatry. (2007) 46:242–51. doi: 10.1097/01.chi.0000246056.83791.b6
98. Molavi P, Nadermohammadi M H, Vicario CM, Nitsche MA, and Salehinejad MA. ADHD subtype-specific cognitive correlates and association with self-esteem: a quantitative difference. BMC Psychiatry. (2020) 20:502. doi: 10.1186/s12888-020-02887-4
99. Nag A, Haber N, Voss C, Tamura S, Daniels J, Ma J, et al. Toward continuous social phenotyping: analyzing gaze patterns in an emotion recognition task for children with autism through wearable smart glasses. J Med Internet Res. (2020) 22:e13810. doi: 10.2196/13810
100. Nakai Y, Takiguchi T, Matsui G, Yamaoka N, and Takada S. Detecting abnormal word utterances in children with autism spectrum disorders: machine-learning-based voice analysis versus speech therapists. Perceptual Motor Skills. (2017) 124:961–73. doi: 10.1177/0031512517716855
101. Ogrim G, Hestad KA, Kropotov J, Sandvik L, Candrian G, and Brunner JF. Predicting the clinical outcome of stimulant medication in pediatric attention-deficit/hyperactivity disorder: data from quantitative electroencephalography, event-related potentials, and a go/no-go test. Neuropsychiatr Dis Treat. (2014) 10:231–42. doi: 10.2147/NDT.S56600
102. Ogrim G and Kropotov JD. Predicting clinical gains and side effects of stimulant medication in pediatric attention-deficit/hyperactivity disorder by combining measures from qEEG and ERPs in a cued GO/NOGO task. Clin EEG Neurosci. (2019) 50:34–43. doi: 10.1177/1550059418782328
103. Oruche UM, Ross SE, Carpenter JS, and Renbarger J. Identifying genetic variants in adolescents with oppositional defiant disorders and/or conduct disorders: A brief report. J Child Adolesc Psychiatr Nursing. (2016) 29:154–7. doi: 10.1111/jcap.12153
104. Parracho HM, Bingham MO, Gibson GR, and McCartney AL. Differences between the gut microflora of children with autistic spectrum disorders and that of healthy children. J Med Microbiol. (2005) 54:987–91. doi: 10.1099/jmm.0.46101-0
105. Pereira-Sanchez V, Franco AR, de Castro-Manglano P, Fernandez-Seara MA, Vallejo-Valdivielso M, Díez-Suárez A, et al. Resting-state fMRI to identify the brain correlates of treatment response to medications in children and adolescents with attention-deficit/hyperactivity disorder: lessons from the CUNMET study. Front Psychiatry. (2021) 12:759696. doi: 10.3389/fpsyt.2021.759696
106. Peris TS and Piacentini J. Optimizing treatment for complex cases of childhood obsessive compulsive disorder: a preliminary trial. J Clin Child Adolesc Psychol. (2013) 42:1–8. doi: 10.1080/15374416.2012.673162
107. Peris TS, Rozenman MS, Sugar CA, McCracken JT, and Piacentini J. Targeted family intervention for complex cases of pediatric obsessive-compulsive disorder: A randomized controlled trial. J Am Acad Child Adolesc Psychiatry. (2017) 56:1034–1042.e1. doi: 10.1016/j.jaac.2017.10.008
108. Pines AR, Sussman B, Wyckoff SN, McCarty PJ, Bunch R, Frye RE, et al. Locked-in intact functional networks in children with autism spectrum disorder: A case-control study. J Personalized Med. (2021) 11:854. doi: 10.3390/jpm11090854
109. Pugliese CE, Handsman R, You X, Anthony LG, Vaidya C, and Kenworthy L. Probing heterogeneity to identify individualized treatment approaches in autism: Specific clusters of executive function challenges link to distinct co-occurring mental health problems. Autism. (2024) 28:2834–47. doi: 10.1177/13623613241246091
110. Rádosi A, Ágrez K, Pászthy B, Réthelyi JM, Ulbert I, and Bunford N. Concurrent and prospective associations of reward response with affective and alcohol problems: ADHD-related differential vulnerability. J Youth Adolescence. (2023) 52:1856–72. doi: 10.1007/s10964-023-01794-7
111. Ran LY, Kong YT, Xiang JJ, Zeng Q, Zhang CY, Shi L, et al. Serum extracellular vesicle microRNA dysregulation and childhood trauma in adolescents with major depressive disorder. Bosnian J Basic Med Sci. (2022) 22:959–71. doi: 10.17305/bjbms.2022.7110
112. Rijlaarsdam J, Barker ED, Caserini C, Koopman-Verhoeff ME, Mulder RH, Felix JF, et al. Genome-wide DNA methylation patterns associated with general psychopathology in children. J Psychiatr Res. (2021) 140:214–20. doi: 10.1016/j.jpsychires.2021.05.029
113. Roberts TA, Wagner JA, Sandritter T, Black BT, Gaedigk A, and Stancil SL. Retrospective review of pharmacogenetic testing at an academic children’s hospital. Clin Trans Science. (2021) 14:412–21. doi: 10.1111/cts.12895
114. Rossi CC, Van De Water J, Rogers SJ, and Amaral DG. Detection of plasma autoantibodies to brain tissue in young children with and without autism spectrum disorders. Brain Behavior Immunity. (2011) 25:1123–35. doi: 10.1016/j.bbi.2011.02.011
115. Rossow KM, Aka IT, Maxwell-Horn AC, Roden DM, and Van Driest SL. Pharmacogenetics to predict adverse events associated with antidepressants. Pediatrics. (2020) 146:e20200957. doi: 10.1542/peds.2020-0957
116. Rudolph KD, Davis MM, and Monti JD. Cognition–emotion interaction as a predictor of adolescent depressive symptoms. Dev Psychol. (2017) 53:2377–83. doi: 10.1037/dev0000397
117. Sabatello M, Chen Y, Herrera CF, Brockhoff E, Austin J, and Appelbaum PS. Teenagers and precision psychiatry: A window of opportunity. Public Health Genomics. (2021) 24:14–25. doi: 10.1159/000512475
118. Saggu S, Daneshvar H, Samavi R, Pires P, Sassi RB, Doyle TE, et al. Prediction of emergency department revisits among child and youth mental health outpatients using deep learning techniques. BMC Med Inf Decision Making. (2024) 24:42. doi: 10.1186/s12911-024-02450-1
119. Segura AG, de la S E, Sugranyes G, Baeza I, Valli I, Díaz-Caneja C, et al. Epigenetic age deacceleration in youth at familial risk for schizophrenia and bipolar disorder. Trans Psychiatry. (2023) 13:155. doi: 10.1038/s41398-023-02463-w
120. Shih W, Patterson SY, and Kasari C. Developing an adaptive treatment strategy for peer-related social skills for children with autism spectrum disorders. J Clin Child Adolesc Psychol. (2016) 45:469–79. doi: 10.1080/15374416.2014.915549
121. Shirafkan H, Mahmoudi-Gharaei J, Fotouhi A, Mozaffarpur SA, Yaseri M, and Hoseini M. Individualizing the dosage of Methylphenidate in children with attention deficit hyperactivity disorder. BMC Med Res Methodology. (2020) 20:56. doi: 10.1186/s12874-020-00934-y
122. Stergiakouli E, Martin J, Hamshere ML, Langley K, Evans DM, St Pourcain B, et al. Shared genetic influences between attention-deficit/hyperactivity disorder (ADHD) traits in children and clinical ADHD. J Am Acad Child Adolesc Psychiatry. (2015) 54:322–7. doi: 10.1016/j.jaac.2015.01.010
123. Storch EA, Wood JJ, Guzick AG, Small BJ, Kerns CM, Ordaz DL, et al. Moderators of response to personalized and standard care cognitive-behavioral therapy for youth with autism spectrum disorder and comorbid anxiety. J Autism Dev Disord. (2022) 52:950–8. doi: 10.1007/s10803-021-05000-0
124. Suganya V, Geetha A, and Sujatha S. Urine proteome analysis to evaluate protein biomarkers in children with autism. Clinica Chimica Acta. (2015) 450:210–9. doi: 10.1016/j.cca.2015.08.015
125. Sun H, Chen Y, Huang Q, Lui S, Huang X, Shi Y, et al. Psychoradiologic utility of MR imaging for diagnosis of attention deficit hyperactivity disorder: A radiomics analysis. Radiology. (2018) 287:620–30. doi: 10.1148/radiol.2017170226
126. Swatzyna RJ, Tarnow JD, Turner RP, Roark AJ, MacInerney EK, and Kozlowski GP. Integration of EEG into psychiatric practice: A step toward precision medicine for autism spectrum disorder. J Clin Neurophysiology. (2017) 34:230–5. doi: 10.1097/WNP.0000000000000365
127. Tariq Q, Fleming SL, Schwartz JN, Dunlap K, Corbin C, Washington P, et al. Detecting developmental delay and autism through machine learning models using home videos of Bangladeshi children: development and validation study. J Med Internet Res. (2019) 21:e13822. doi: 10.2196/13822
128. Thng G, Shen X, Stolicyn A, Harris MA, Adams MJ, Barbu MC, et al. Comparing personalized brain-based and genetic risk scores for major depressive disorder in large population samples of adults and adolescents. Eur Psychiatry. (2022) 65:e44. doi: 10.1192/j.eurpsy.2022.2301
129. Thomson JB and Varley CK. Prediction of stimulant response in children with attention-deficit/hyperactivity disorder. J Child And Adolesc Psychopharmacology. (1998) 8:125–32. doi: 10.1089/cap.1998.8.125
130. Thümmler S, Dor E, David R, Leali G, Battista M, David A, et al. Pharmacoresistant severe mental health disorders in children and adolescents: functional abnormalities of cytochrome P450 2D6. Front Psychiatry. (2018) 9:2. doi: 10.3389/fpsyt.2018.00002
131. Tini E, Smigielski L, Romanos M, Wewetzer C, Karwautz A, Reitzle K, et al. Therapeutic drug monitoring of sertraline in pediatric population: A naturalistic study with insights into the clinical response of obsessive-compulsive disorder. Compr Psychiatry. (2022) 115:152301. doi: 10.1016/j.comppsych.2022.152301
132. Tumlin KI, Crowley A, Turner B, Riley E, and Lyons J. Detection of traumatic stress in the presence of traumatic experiences: the role of resilience factors in foster care children five years or younger. Int J Ment Health Systems. (2023) 17:39. doi: 10.1186/s13033-023-00610-w
133. Vande Voort JL, Orth SS, Shekunov J, Romanowicz M, Geske JR, Ward JA, et al. A randomized controlled trial of combinatorial pharmacogenetics testing in adolescent depression. J Am Acad Child Adolesc Psychiatry. (2022) 61:46–55. doi: 10.1016/j.jaac.2021.03.011
134. Vilgis V, Gelardi KL, Helm JL, Forbes EE, Hipwell AE, Keenan K, et al. Dorsomedial prefrontal activity to sadness predicts later emotion suppression and depression severity in adolescent girls. Child Dev. (2018) 89:758–72. doi: 10.1111/cdev.13023
135. Voss C, Schwartz J, Daniels J, Kline A, Haber N, Washington P, et al. Effect of wearable digital intervention for improving socialization in children with autism spectrum disorder: A randomized clinical trial. JAMA Pediatrics. (2019) 173:446. doi: 10.1001/jamapediatrics.2019.0285
136. Walsh CG, Ribeiro JD, and Franklin JC. Predicting suicide attempts in adolescents with longitudinal clinical data and machine learning. J Child Psychol Psychiatry. (2018) 59:1261–70. doi: 10.1111/jcpp.12916
137. Wang JJF, Lin S, Best JR, Selles RR, and Stewart SE. Race and ethnicity in pediatric OCD: An exploratory study of a clinical North American sample. Ann Clin Psychiatry. (2021) 33:4–17. doi: 10.12788/acp.0007
138. Wang J, Jia K, Guo Q, Liu J, Cai J, Shen Y, et al. Post-traumatic stress disorder is associated with elevated plasma cholesterol in female TT homozygotes of LDLR rs5925. Int J Mol Sci. (2023) 24:9016. doi: 10.3390/ijms24109016
139. Washington P, Leblanc E, Dunlap K, Penev Y, Kline A, Paskov K, et al. Precision telemedicine through crowdsourced machine learning: testing variability of crowd workers for video-based autism feature recognition. J Personalized Med. (2020) 10:86. doi: 10.3390/jpm10030086
140. West PR, Amaral DG, Bais P, Smith AM, Egnash LA, Ross ME, et al. Metabolomics as a tool for discovery of biomarkers of autism spectrum disorder in the blood plasma of children. PloS One. (2014) 9:e112445. doi: 10.1371/journal.pone.0112445
141. White SW, Schry AR, Miyazaki Y, Ollendick TH, and Scahill L. Effects of verbal ability and severity of autism on anxiety in adolescents with ASD: one-year follow-up after cognitive behavioral therapy. J Clin Child And Adolesc Psychol. (2015) 44:839–45. doi: 10.1080/15374416.2014.893515
142. Yang D, Pelphrey KA, Sukhodolsky DG, Crowley MJ, Dayan E, Dvornek NC, et al. Brain responses to biological motion predict treatment outcome in young children with autism. Trans Psychiatry. (2016) 6:e948. doi: 10.1038/tp.2016.213
143. Yang J, Chen Y, Xiong X, Zhou X, Han L, Ni L, et al. Peptidome analysis reveals novel serum biomarkers for children with autism spectrum disorder in China. Proteomics Clin Applications. (2018) 12:1700164. doi: 10.1002/prca.201700164
144. Yang TT, Simmons AN, Matthews SC, Tapert SF, Frank GK, Bischoff-Grethe A, et al. Depressed adolescents demonstrate greater subgenual anterior cingulate activity. Neuroreport. (2009) 20:440–4. doi: 10.1097/WNR.0b013e3283262e10
145. Yap IKS, Angley M, Veselkov KA, Holmes E, Lindon JC, and Nicholson JK. Urinary metabolic phenotyping differentiates children with autism from their unaffected siblings and age-matched controls. J Proteome Res. (2010) 9:2996–3004. doi: 10.1021/pr901188e
146. Young JF, Jones JD, Gallop R, Benas JS, Schueler CM, Garber J, et al. Personalized depression prevention: A randomized controlled trial to optimize effects through risk-informed personalization. J Am Acad Child Adolesc Psychiatry. (2021) 60:1116–1126.e1. doi: 10.1016/j.jaac.2020.11.004
147. Zhang X, Zhang Y, Bu L, Li H, Ye H, Wang D, et al. The trajectories and associations of sleep disturbance symptoms with suicidal ideation in adolescents: A three-wave longitudinal study. Sleep Med. (2024) 124:591–7. doi: 10.1016/j.sleep.2024.10.031
148. Zhang Z. Early warning model of adolescent mental health based on big data and machine learning. Soft Computing. (2024) 28:811–28. doi: 10.1007/s00500-023-09422-z
149. Zhong Y, Yang B, Su Y, Qian Y, Cao Q, Chang S, et al. The association with quantitative response to attention-deficit/hyperactivity disorder medication of the previously identified neurodevelopmental network genes. J Child Adolesc Psychopharmacology. (2020) 30:348–54. doi: 10.1089/cap.2018.0164
150. Mandelli V, Landi I, Busuoli EM, Courchesne E, Pierce K, and Lombardo MV. Prognostic early snapshot stratification of autism based on adaptive functioning. Nat Ment Health. (2023) 1:327–36. doi: 10.1038/s44220-023-00056-6
151. Sengupta S, Grizenko N, Schmitz N, Schwartz G, Bellingham J, Polotskaia A, et al. COMT Val108/158Met polymorphism and the modulation of task-oriented behavior in children with ADHD. Neuropsychopharmacology. (2008) 33:3069–77. doi: 10.1038/npp.2008.85
152. Neufcourt L, Castagné R, Mabile L, Khalatbari-Soltani S, Delpierre C, and Kelly-Irving M. Assessing how social exposures are integrated in exposome research: A scoping review. Environ Health Perspectives. (2022) 130:116001. doi: 10.1289/ehp11015
153. Navanandan N, Searns J, and Ambroggio L. Method/ology of phases of biomarker discovery. Hosp Pediatrics. (2023) 13:E181–5. doi: 10.1542/hpeds.2022-007012
154. Posner J. The role of precision medicine in child psychiatry: what can we expect and when? J Am Acad Child Adolesc Psychiatry. (2018) 57:813–7. doi: 10.1289/ehp11015
155. Zima BT and Edgcomb JB. Editorial: national trends in child mental health disorders signal opportunities and challenges for precision child psychiatry. J Am Acad Child Adolesc Psychiatry. (2025) 64:876–8. doi: 10.1016/j.jaac.2025.01.008
156. Kas MJH, Penninx BWJH, Knudsen GM, Cuthbert B, Falkai P, Sachs GS, et al. Precision psychiatry roadmap: towards a biology-informed framework for mental disorders. Mol Psychiatry. (2025) 30:3846–55. doi: 10.1038/s41380-025-03070-5
157. Delgadillo J and Lutz W. A development pathway towards precision mental health care. JAMA Psychiatry. (2020) 77:889–90. doi: 10.1001/jamapsychiatry.2020.1048
158. Salazar de Pablo G, Studerus E, Vaquerizo-Serrano J, Irving J, Catalan A, Oliver D, et al. Implementing precision psychiatry: A systematic review of individualized prediction models for clinical practice. Schizophr Bulletin. (2021) 47:284–97. doi: 10.1093/schbul/sbaa120
159. Collins FS and Varmus H. A new initiative on precision medicine. New Engl J Med. (2015) 372:793–5. doi: 10.1056/NEJMp1500523
160. Lee J, Gillam L, Visvanathan K, Hansford JR, and McCarthy MC. Clinical utility of precision medicine in pediatric oncology: A systematic review. JCO Precis Oncol. (2021) 5:1088–102. doi: 10.1200/PO.20.00405
161. Zhou S, Liu L, Jin X, Dorikun D, and Ma S. Biomarkers predicting postoperative adverse outcomes in children with congenital heart disease: a systematic review and meta-analysis. Front Pediatrics. (2025) 13. doi: 10.3389/fped.2025.1508329
162. Pandey R, Brennan NF, Trachana K, Katsandres S, Bodamer O, Belmont J, et al. A meta-analysis of diagnostic yield and clinical utility of genome and exome sequencing in pediatric rare and undiagnosed genetic diseases. Genet Med. (2025) 27101398. doi: 10.1016/j.gim.2025.101398
163. McGorry P, Gunasiri H, Mei C, Rice S, and Gao CX. The youth mental health crisis: analysis and solutions. Front Psychiatry. (2025) 15:1517533. doi: 10.3389/fpsyt.2024.1517533
164. Fazel M and Soneson E. Current evidence and opportunities in child and adolescent public mental health: a research review. J Child Psychol Psychiatry. (2023) 64:1699–719. doi: 10.1111/jcpp.13889
165. Uhlhaas PJ, Davey CG, Mehta UM, Shah J, Torous J, Allen NB, et al. Towards a youth mental health paradigm: a perspective and roadmap. Mol Psychiatry. (2023) 28:3171–81. doi: 10.1038/s41380-023-02202-z
166. Zima BT, Edgcomb JB, and Fortuna LR. Identifying precise targets to improve child mental health care equity: leveraging advances in clinical research informatics and lived experience. Child Adolesc Psychiatr Clinics North America. (2024) 33:471–83. doi: 10.1016/j.chc.2024.03.009
167. Abraham A and Walker-Harding L. The key social determinants of mental health: their effects among children globally and strategies to address them: a narrative review. Pediatr Med. (2022) 5:7. doi: 10.21037/pm-20-78
168. Del Casale A, Bronzatti L, Arena JF, Gentile G, Lai C, Girardi P, et al. The era of precision psychiatry: toward a new paradigm in diagnosis and care. Psychiatry Int. (2025) 6:146. doi: 10.3390/psychiatryint6040146
169. Baldwin H, Loebel-Davidsohn L, Oliver D, Salazar de Pablo G, Stahl D, Riper H, et al. Real-world implementation of precision psychiatry: A systematic review of barriers and facilitators. Brain Sci. (2022) 12:934. doi: 10.3390/brainsci12070934
170. Altman DG and Simera I. A history of the evolution of guidelines for reporting medical research: the long road to the EQUATOR Network. J R Soc Med. (2016) 109:67–77. doi: 10.1177/0141076815625599
171. Hasanzadeh F, Josephson CB, Waters G, Adedinsewo D, Azizi Z, and White JA. Bias recognition and mitigation strategies in artificial intelligence healthcare applications. NPJ Digital Med. (2025) 8:154–13. doi: 10.1038/s41746-025-01503-7
172. Abi-Dargham A, Moeller SJ, Ali F, DeLorenzo C, Domschke K, Horga G, et al. Candidate biomarkers in psychiatric disorders: state of the field. World Psychiatry. (2023) 22:236–62. doi: 10.1002/wps.21078
173. Kieling C, Buchweitz C, Caye A, Silvani J, Ameis S, Brunoni A, et al. Worldwide prevalence and disability from mental disorders across childhood and adolescence: evidence from the global burden of disease study. JAMA Psychiatry. (2024) 81:347–56. doi: 10.1001/jamapsychiatry.2023.5051
174. Racine N, McArthur BA, Cooke JE, Eirich R, Zhu J, and Madigan S. Global prevalence of depressive and anxiety symptoms in children and adolescents during COVID-19: A meta-analysis. JAMA Pediatrics. (2021) 175:1142–50. doi: 10.1001/jamapediatrics.2021.2482
175. Mogaka JJO, James SE, and Chimbari MJ. Leveraging implementation science to improve implementation outcomes in precision medicine. Am J Trans Res. (2020) 12:4853.
176. Hall CE, Michel BD, and Baron AS. Editorial: advances in clinical science and practice need research on implicit bias. J Am Acad Child Adolesc Psychiatry. (2025) 64:671–3. doi: 10.1016/j.jaac.2024.08.486
177. Vrljičak Davidović N, Komić L, Mešin I, Kotarac M, Okmažić D, and Franić T. Registry versus publication: discrepancy of primary outcomes and possible outcome reporting bias in child and adolescent mental health. Eur Child Adolesc Psychiatry. (2022) 31:757–69. doi: 10.1007/s00787-020-01710-5
178. Fusar-Poli P, Manchia M, Koutsouleris N, Leslie D, Woopen C, Calkins M, et al. Ethical considerations for precision psychiatry: A roadmap for research and clinical practice. Eur Neuropsychopharmacol. (2022) 63:17–34. doi: 10.1016/j.euroneuro.2022.08.001
179. Collins GS, Moons KGM, Dhiman P, Riley RD, Beam AL, Van Calster B, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. (2024) 385:e078378. doi: 10.1136/bmj-2023-078378
180. Lim SS, Semnani-Azad Z, Morieri ML, Ng AH, Ahmad A, Fitipaldi H, et al. Reporting guidelines for precision medicine research of clinical relevance: the BePRECISE checklist. Nat Med. (2024) 30:1874–81. doi: 10.1038/s41591-024-03033-3
Keywords: precision mental health, precision behavioural health, precision psychiatry, children, adolescents, youth
Citation: Lyeo JS, Blais A, Cloutier P, Boafo A, Dargél A, Helleman A, Tanya T, Kashala-Abotnes E, Honeywell C and Pajer K (2026) Scoping review of precision child and youth mental health research: dwelling in possibility. Front. Psychiatry 16:1691548. doi: 10.3389/fpsyt.2025.1691548
Received: 11 September 2025; Accepted: 15 December 2025; Revised: 12 December 2025;
Published: 09 February 2026.
Edited by:
Qing Zhao, Chinese Academy of Sciences (CAS), ChinaReviewed by:
Cole Hooley, Brigham Young University, United StatesLuis-Javier Márquez-Álvarez, University of A Coruña, Spain
Copyright © 2026 Lyeo, Blais, Cloutier, Boafo, Dargél, Helleman, Tanya, Kashala-Abotnes, Honeywell and Pajer. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Kathleen Pajer, a3BhamVyQGNoZW8ub24uY2E=