Training future physicians with artificial intelligence: Enhancing clinical decision-making abilities through AI-driven simulations and real-world data analysis

Artificial Intelligence (AI) is transforming healthcare and medical education by reshaping clinical reasoning, diagnostics, and treatment planning. In medical training, AI-enabled simulations and real-time data analytics allow students and physicians to practice decision-making in controlled, risk-free environments. AI technologies including Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP) are widely applied across medical disciplines. In surgical education, AI-powered virtual 3D models enable trainees to rehearse complex procedures, while computer vision systems analyze surgical videos to identify errors and inefficiencies. In diagnostics, AI systems integrate imaging data, genomic profiles, and clinical histories to improve early detection of diseases such as cancer and cardiovascular disorders. Despite these advancements, significant barriers limit AI adoption, particularly in Low and Middle-Income Countries (LMICs). Challenges include limited infrastructure, insufficient trained personnel, data privacy concerns, financial constraints, and unclear regulatory frameworks. Ethical considerations such as algorithmic bias, transparency, accountability, and equitable access must also be addressed to ensure responsible AI deployment. To effectively integrate AI into healthcare, physicians and educators must develop competencies in data literacy, AI fundamentals, ethics, and interdisciplinary collaboration. Medical curricula should incorporate structured AI training to prepare future healthcare professionals for technology-enhanced clinical environments. While high-income countries are rapidly advancing AI integration, LMICs face systemic constraints. However, emerging national AI strategies and global collaboration initiatives offer promising pathways toward equitable adoption. Integrating AI into health professions education is essential for preparing the next generation of clinicians to operate within increasingly data-driven healthcare systems.