AI in Healthcare: Promise, Pitfalls, and Future Directions

Abstract

Artificial intelligence (AI) has rapidly moved from a niche computational tool to an active participant in healthcare delivery and medical education. Its expanding role now extends beyond diagnosis to clinical trial screening platforms, decision support, physician-facing knowledge tools, and early pilots involving supervised prescription workflow. This review examines the evolution of healthcare AI, from traditional rule-based systems to machine learning, large language models, and multimodal AI, and explores its current applications across clinical practice, public health, and medical education. We discuss the major promises of AI, including improved diagnostic support, earlier disease detection, reduced administrative burden, scalable education, and augmented clinical reasoning. At the same time, we highlight important limitations and risks, including bias, hallucinations, limited generalizability, privacy concerns, automation bias, threats to humanistic care, and challenges to academic integrity. We further examine barriers to implementation, including technical integration, workflow disruption, regulatory uncertainty, and workforce preparedness. While AI offers substantial opportunities to improve healthcare systems and training environments, its impact will depend less on algorithmic capability alone and more on responsible integration, continuous oversight, and preservation of human judgment. Future efforts should prioritize trustworthy, equitable, and human-centered approaches that position AI as a tool to augment rather than replace clinician-patient relationship.