e.g. mhealth
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Skip search results from other journals and go to results- 112 Journal of Medical Internet Research
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Several studies have shown that Chat GPT provides appropriate, accurate, and reliable knowledge across a wide range of cardiac and noncardiac medical conditions, including heart failure [11-16]. In addition to accuracy, Chat GPT has been found to deliver more empathetic responses to real-world patient questions than physicians in online forums [17]. As prior data regarding accuracy have been promising, an emerging focus has been on investigating the readability of the model’s output.
JMIR Cardio 2025;9:e68817
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As of April 2024, a pilot program in Louisiana incorporated Chat GPT-4.0 into electronic health record (EHR) messaging to generate preliminary responses that clinicians subsequently reviewed for validity [3]. Despite Chat GPT-4.0’s advances, the study demonstrated that human oversight in AI-generated communication remains essential [3].
Such initiatives demonstrate AI’s potential to reduce administrative workload, but they also underscore its role in improving patient education.
JMIR Dermatol 2025;8:e72706
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In this research project, we aimed to co-design an awareness-raising fact sheet for an oral cancer screening program with people experiencing homelessness as experts by experience and Chat GPT. The latter was used to present textual alternatives for this health information piece, so we could also test the usability of Chat GPT in designing adequate information materials serving the needs of people experiencing homelessness.
JMIR Form Res 2025;9:e68316
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In an exploratory case study, we asked Chat GPT (Chat GPT-4o and Chat GPT-o1) to synthesize two datasets: one qualitative and one quantitative, inspired by existing real-world datasets the authors had previously analyzed. The generation of synthetic datasets and subsequent data analysis took place in October and November of 2024.
JMIR Form Res 2025;9:e73248
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Reference 13: ChatGPT outperforms humans in emotional awareness evaluations Reference 15: Evaluation of ChatGPT for NLP-based mental health applications(https://arxiv.org/abs/2303.15727 Reference 16: Bias in emotion recognition with ChatGPT(https://arxiv.org/abs/2310.11753) Reference 19: Human vs. machine: a comparative analysis of qualitative coding by humans and ChatGPT-4chatgptGenerative Language Models Including ChatGPT
J Med Internet Res 2025;27:e53332
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Custom GPTs are derivations of the baseline Chat GPT model (at the time of evaluation: GPT-4o) developed by Open AI that have been modified by members of the public with customized instructions and behavior for specific applications (eg, psychotherapy chatbot). In May 2024, we indexed both sites using the search feature to emulate what an end-user may experience with the following search terms: “therapy,” “anxiety,” “depression,” “mental health,” “therapist,” and “psychologist.”
JMIR Form Res 2025;9:e65605
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Recent analyses of Chat GPT-4o (Open AI) have demonstrated its effectiveness and accuracy in summarizing diagnostic radiology reports, with the ability to translate medical terminology to an 8th-grade reading level [3]. When prompted to explain medical imaging reports to a child using simplified and basic language, Chat GPT-4o generated 15 different reports, which were evaluated by 15 radiologists.
JMIR AI 2025;4:e69654
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Research has shown that Chat GPT, primarily trained on English-language corpora, is able to pass the United States Medical Licensing Examination [5], and can effectively addressing various clinical medical inquiries [6]. However, in some non-English medical examinations, Chat GPT fails to even reach a passing grade [7-9].
JMIR Med Inform 2025;13:e69485
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For transformer-based models and LLMs, we used pretrained embeddings from models like BERT and Chat GPT. These models capture deep contextual meanings by analyzing entire sentences rather than individual words. Unlike traditional methods, transformers dynamically understand context, improving sentiment analysis accuracy by recognizing complex language patterns.
The inverse document frequency of a term reflects the inverse proportion of documents that contain that term.
JMIR Infodemiology 2025;5:e70525
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Large Language Model Architectures in Health Care: Scoping Review of Research Perspectives
The last years brought an unmatched rise of large language models (LLMs) such as Open AI’s Chat GPT [1,2], Google’s Bidirectional Encoder Representations from Transformers (BERT) [3], and Meta’s Llama [4]. LLMs have dramatically extended the abilities of natural language processing through generating text by repeatedly adding the most likely following words [5]. Most LLMs are based on large amounts of general-purpose text data on which the models were trained [6-8].
J Med Internet Res 2025;27:e70315
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