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Skip search results from other journals and go to results- 15 Journal of Medical Internet Research
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The recent introduction of large language models (LLMs) for public use has generated both excitement and debate. Their adoption has rapidly grown across various human activities [6]. Many foresee the immense potential benefits of applying such technology to medical practice, while others harbor concerns about the dangers it might pose if left unregulated and misaligned [7-12].
Without a doubt, LLMs like Chat GPT represent a new generation of CDSS with unparalleled assistance capabilities.
JMIR Med Educ 2025;11:e55709
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LLMs are models trained on large amounts of textual data that are capable of generating language similar to that of humans. LLMs’ capabilities span a diverse array of tasks, including question-answering, summarization, translation, and conversing. The development and integration of LLMs is advancing rapidly across different sectors. In particular, LLMs demonstrate impressive performance in automated analyses and syntheses of data [6].
J Med Internet Res 2025;27:e64364
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The rapid development of artificial intelligence (AI) and the emergence of large language models (LLMs), such as Chat GPT, have increasingly blurred the lines between human-written and AI-generated text. This has created a significant challenge in identifying the authorship of written work, especially as the use of AI has become ubiquitous since chatbots have become freely available [1,2].
JMIR Med Educ 2025;11:e62779
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The integration of artificial intelligence (AI) in health care has rapidly evolved, with large language models (LLMs) such as Chat GPT at the forefront of this change [1-3]. These models, trained on vast datasets, have shown remarkable potential in various domains, including health care, by understanding and generating human-like text [4].
JMIR Cancer 2025;11:e66633
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Transforming Informed Consent Generation Using Large Language Models: Mixed Methods Study
With the advancement of large language models (LLMs), a possible solution to improving ICF has emerged. LLMs show significant potential in health informatics, including tasks such as name entity extraction [11], patient trial matching [12,13], biomedical reasoning and classification [14], prediction of admissions [15], automation of administrative tasks [16], and so forth.
JMIR Med Inform 2025;13:e68139
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Large language models (LLMs) have the potential to replace internet searches for clinicians and patients. LLMs, such as Chat GPT, have demonstrated promising performance in clinical decision-making [11] and diagnosis [12].
J Med Internet Res 2025;27:e64290
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This retrospective cross-sectional study evaluated the performance of 7 LLMs on 1200 multiple-choice questions (MCQs) from the CNNLE administered between 2019 and 2023. The study design was chosen for its suitability in systematically analyzing preexisting datasets and providing the capabilities of LLMs across various question types and levels of complexity. A head-to-head evaluation approach was adopted to compare the LLMs.
JMIR Med Inform 2025;13:e63731
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Revolutionizing Health Care: The Transformative Impact of Large Language Models in Medicine
The potential of LLMs to optimize digital health care workflows is undeniable. With further technological advancements and empirical research, LLMs are expected to play an increasingly significant role in the future of health care (Figure 3).
Integration of LLMs in health care systems across different scales.
J Med Internet Res 2025;27:e59069
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Transformer models, a deep learning architecture introduced in the paper “Attention is All You Need” by Vaswani et al [6], have revolutionized the field of NLP, establishing themselves as the foundation upon which modern large language models (LLMs) have been developed. LLMs, such as Open AI's generative pre-trained transformers (GPTs), are models trained on vast amounts of text to learn complex linguistic patterns.
JMIR Med Inform 2025;13:e58457
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Large Language Models in Worldwide Medical Exams: Platform Development and Comprehensive Analysis
To address these gaps, we proposed developing a comprehensive platform that provides a centralized system for collecting, analyzing, and comparing evidence-based knowledge regarding the performance of various LLMs across a wide range of medical exams around the world.
In this study, we introduce Med Exam LLM, a platform specifically designed for benchmarking LLMs for medical exams around the world.
J Med Internet Res 2024;26:e66114
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