Article ID: JMJ25-0019-R
Artificial intelligence (AI) is rapidly transforming internal medicine by enhancing diagnostic accuracy, enabling personalized treatment, and optimizing patient management. As of August 2024, the U.S. Food and Drug Administration has authorized nearly 950 AI/ML-enabled medical devices, while an American Medical Association survey reported that 66% of physicians already incorporate AI into clinical practice. This review provides a comprehensive overview of AI’s expanding role across internal medicine, highlighting its applications in medical interviews, text-based communication, and the interpretation of core diagnostic modalities such as electrocardiography, chest X-ray, and auscultation. While several FDA-approved AI tools are already integrated into clinical workflows, many technologies remain at the research or proof-of-concept stage, with validation often limited to retrospective or controlled trial settings.
The transformative potential of AI is particularly relevant in urban healthcare, where population density, limited resources, and disproportionate burdens of chronic and lifestyle-related diseases underscore the need for innovative solutions. AI can mitigate physician shortages, streamline care in overburdened systems, and support equitable access to diagnostics and treatment in metropolitan areas. Key technologies, including machine learning, deep learning, and large language models, are critically examined, along with emerging innovations such as EHR-based foundation models.
Despite its promise, AI integration raises ethical, legal, social, and regulatory challenges, including algorithmic bias, data privacy, validation standards, and workforce adaptation. This paper explores these multifaceted aspects, emphasizing the importance of collaborative efforts to ensure responsible and equitable implementation, ultimately aiming to improve patient outcomes and public health in the digital era.
