Integrating AI and self-management in cancer care: Advancing patient-centered approaches

Cancer care remains a significant global healthcare challenge, with critical concerns surrounding symptom management and overall quality of life for patients. Despite advances in early detection and treatment options, challenges still persist in effectively managing symptoms for diverse cancer populations. Recently, the focus has shifted towards integrating artificial intelligence (AI) solutions with patient-centered self-management strategies to address these challenges more effectively. AI technologies, such as predictive analytics and natural language processing, promise to revolutionize real-time symptom monitoring, personalized treatment, and streamlining clinical workflows. Moreover, empowering patients with digital health interventions, remote monitoring devices, and curated educational resources has the potential to offer patients greater control of their treatment plans. However, the need for further research and collaborative efforts in these areas are essential as we continue to strive for improved patient experiences and outcomes.

This Research Topic aims to examine the integration of AI-powered tools and patient-centered self-management strategies in cancer care. By focusing on emerging AI-enabled approaches and evidence-based self-management methods, the research will seek to enhance early intervention, improve communication among healthcare professionals, and enrich overall patient experiences during cancer treatment. Key questions include how AI can facilitate personalized treatment plans, enhance health outcomes, and foster collaboration among medical teams, both from a technological and patient-oriented perspective.

To gather further insights into the integration of AI and self-management strategies in cancer care, we welcome articles addressing, but not limited to, the following themes:
o AI-enabled patient-reported symptom monitoring and real-time feedback
o AI tools that support patient symptom understanding, self-triage, and safe escalation
o Digital interventions that deliver personalized self-management plans for treatment-related symptoms
o AI to improve patient–provider communication (symptom summarization, visit prep, shared decision support)
o AI-supported clinical workflows for triaging and responding to patient-reported symptoms (such as alert prioritization, escalation pathways)
o Implementation and evaluation: usability, engagement, adherence, clinical outcomes, workload, equity, safety, privacy
o Ethical issues of AI application in cancer symptom care
o AI-powered technology in cancer symptom detection, alarm, and management.
o Telemedicine and digital health tools for self-care in cancer patients or patient self-report in various clinical settings.
o AI technology in the follow-up of cancer patients during and post-anti-cancer treatment.
o AI-driven predictive models for cancer treatment effectiveness, including cancer prognosis, recurrence risk, and treatment-related adversities.
o AI-driven predictions and monitoring of patient's condition deterioration.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: artificial intelligence, AI-powered technology, large language model, big data, telemedicine, digital health tool, cancer, cancer-related symptom, self-care, patient self-report

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.