Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing various sectors, including medicine. In pediatrics, especially in cardiology, there is a crucial need to integrate these technologies to enhance diagnostics, treatment, and patient management. Despite the significant advancements in AI and ML applications within adult healthcare settings, pediatric cardiology remains an area with untapped potential, showing a gap between technological advancements and their application in treating younger patients.
This Research Topic aims to collect and synthesize new findings in the use of AI and ML specifically within the field of Pediatric Cardiology. By focusing on this sub-speciality, the goal is to uncover unique challenges and opportunities that AI and ML can address in young patients, thereby enhancing diagnosis accuracy, treatment efficacy, and overall patient outcomes.
To further our understanding of AI and ML in Pediatric Cardiology, this collection invites research that delves into the boundaries and limitations of current technology and explores future improvements. We welcome articles addressing, but are not limited to, the following themes:
• AI-guided diagnosis and treatment in pediatric cardiology
• Machine learning applications for real-time data interpretation
• Big data analytics in pediatric patient monitoring
• Ethical and legal implications of AI in pediatric healthcare
• The role of AI in pediatric precision medicine and drug development
• Integrative AI approaches for improving telemedicine practices
Natural Language Processing for pediatric clinical documentation
Keywords: artificial intelligence, machine learning, pediatric cardiology
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.
