AI in Healthcare: Transforming Clinical Risk Prediction, Medical Large Language Models, and Beyond

The integration of Artificial Intelligence (AI) into healthcare has opened new frontiers in diagnostics, treatment, and patient care. With applications ranging from clinical risk prediction to the utilization of medical large language models (LLMs), AI is reshaping the way healthcare professionals tackle complex medical challenges, delivering transformative improvements in accuracy, efficiency, and accessibility.

The goal of this Research Topic is to highlight the latest advancements in AI applications within the healthcare domain, including clinical risk prediction, medical LLMs, AI-assisted diagnostics, personalized decision support for treatment planning and adaptive clinical pathway management. By providing a platform for researchers and practitioners to share their insights, we seek to address current challenges, explore ethical considerations, requirements for building trustworthy medical AI solutions and promote the adoption of AI technologies to improve healthcare outcomes globally.

Suitable themes for manuscripts include (but are not limited to):

• AI-Based Clinical Risk Prediction Models
• Construction and Application of Medical Large Language Models (LLMs)
• AI in Medical Imaging and Diagnostics
• AI for Personalized Medicine and Treatment Recommendations
• Ethical, Regulatory, and Trust Considerations in AI-Driven Healthcare
• AI for Public Health and Epidemiology
• Real-World Implementation of AI in Healthcare Systems
• AI for Clinical Workflows Management
• Synthetic Real World Evidence data for medical ML and LLM

Keywords: Clinical Risk Prediction Models, Medical Large Language Models, AI for Medical Imaging and Diagnostics, Personalized Medicine, Adaptive Clinical Workflow, Public Health and Epidemiology, Real-world Application

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.