ReviewJuntendo medical journal2025
Transformative Impact of Artificial Intelligence on Internal Medicine: Current Applications, Challenges, and Future Horizons for Urban Health.
Review in Juntendo medical journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.