Artificial Intelligence and Machine Learning in Pediatrics

Artificial Intelligence (AI) and Machine Learning (ML) in pediatrics represent a burgeoning field within healthcare, driven by the exponential growth of complex healthcare data. Over the past few decades, the advent of advanced sensor technologies such as biosensors, medical imaging, laboratory tests, and genetic tests has generated vast volumes of data that surpass human analytical capabilities. This surge in data necessitates the development of intelligent tools and methods to harness these resources effectively. Current research highlights the potential of AI/ML techniques to uncover intricate nonlinear relationships and patterns within pediatric datasets, leading to timely and accurate insights for early disease diagnosis, improved treatment protocols, and enhanced decision-making processes. Despite these advancements, significant gaps remain, particularly in the integration and application of AI/ML in pediatric healthcare, necessitating further investigation to optimize these technologies for better patient outcomes.

This research topic aims to explore the application of AI and ML in pediatrics, focusing on leveraging big data analytics to improve healthcare outcomes for children. The primary objectives include addressing specific questions such as how AI/ML can enhance early diagnosis, treatment, and prognosis of pediatric diseases, and testing hypotheses related to the efficacy of AI/ML-driven precision medicine in pediatrics. By examining these questions, the research seeks to provide a comprehensive understanding of the potential and limitations of AI/ML in pediatric healthcare, ultimately contributing to safer, more effective, and personalized medical care for children.

To gather further insights into the application of AI/ML in pediatrics, we welcome articles addressing, but not limited to, the following themes:
- Big data analytics in pediatrics
- Disease databases in pediatrics (e.g., rare diseases, disorders)
- Intelligent applications using pediatric disease databases
- Intelligent methods processing various types of pediatric data
- Data mining in pediatrics
- Precision medicine/Personalized medicine in pediatrics using AI/ML
- Diagnostics in pediatrics using AI/ML
- Prognosis in pediatrics using AI/ML
- Deep learning (DL) and Reinforcement Learning (RL) in pediatrics
- Development of new treatment methods in pediatrics using AI/ML
- Reduction of medical errors in pediatrics using AI/ML
- Intelligent machines in pediatrics
- Telemedicine in pediatrics using AI/ML
- Discovery of new drugs in pediatrics using AI/ML
- Use of cloud computing to process pediatric data
- Ethics and regulatory framework of using AI/ML in pediatrics
- Review papers discussing the use of AI/ML in pediatrics

We are looking for original articles, opinion pieces, and reviews that focus on these subjects, ensuring that manuscripts are accessible to a broad readership of clinicians and avoid overly technical language.

Keywords: AI-guided diagnosis and treatment, AI in precision medicine, AI for end-to-end drug discovery and development, AI in telemedicine, Machine learning, Big data analytics, Natural Language Processing, Ethical and legal considerations

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.