e.g. mhealth
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Integrating Nurse Preferences Into AI-Based Scheduling Systems: Qualitative Study
llm
JMIR Form Res 2025;9:e67747
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Therefore, we hypothesize that, given the differences in patterns of speech by individuals across psychiatric disorders, spontaneous use of language will occupy diagnosis-specific subspaces in the LLM embedding space.
JMIR AI 2025;4:e67369
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Transparent communication about data collection, robust validation mechanisms, and regulatory frameworks prioritizing inclusivity and fairness are crucial to maximize the potential of LLM-driven health interventions while ensuring scientifically grounded and emotionally supportive recommendations [29,31].
One critical yet underexplored aspect of LLM development in ERs and PA is the lack of transparency regarding training data and fine-tuning methodologies.
JMIR Med Inform 2025;13:e59309
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Emerging evidence suggests that LLM-based chatbots as social companions can offer positive support and contribute to general psychological wellness [3,4]. There have been numerous studies, especially focused on the AI companion app Replika, that found positive mental health outcomes of using chatbots, such as increased confidence and improved relationships with friends [4,5].
JMIR Ment Health 2025;12:e69709
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Assessing ChatGPT’s Capability as a New Age Standardized Patient: Qualitative Study
Since its introduction in November 2022, sectors spanning from history to entertainment have rapidly adopted the LLM [10].
This advancement in AI has led to the development of virtual SP chatbots. A number of major educational material suppliers and specialized companies are offering chatbot SPs, based on LLMs capable of natural language interactions, for students to practice clinical skills.
JMIR Med Educ 2025;11:e63353
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Among LLM users, half reported personal learning as their goal, and 39% sought information about physical or mental health [3]. Patients burdened with life-changing or rare conditions commonly search for the resources that they need to solve problems. As consumer costs of care keep rising and health care is relentlessly hard to navigate, patients and caregivers are gaining skills and intelligence using LLMs across a breadth of topics.
J Particip Med 2025;17:e75794
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This raises questions about the underlying mechanisms that prompt an LLM to label certain statements as “more factual.” For example, one possible explanation could be that data-rich or frequently discussed topics in training sets may be perceived as more certain [18], even if this does not translate into clinical accuracy. Additionally, retrieval-augmented generation (RAG) has been proposed to ground LLM outputs in external data, which potentially mitigates hallucinations [19].
JMIR Med Inform 2025;13:e66917
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Evaluating Generative AI in Mental Health: Systematic Review of Capabilities and Limitations
LLMs such as Chat GPT, Claude, and Bard hold great promise in mitigating this stark situation to reduce clinicians’ burden and increase clinician efficiency through LLM-assisted clinical notes writing, formulating differential diagnoses, drafting personalized treatment plans, drawing insights from patient chart data, providing on-demand coaching and companionship, and, ultimately, providing therapy [10,11].
JMIR Ment Health 2025;12:e70014
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Reference 22: What’s the most popular LLM? (https://www.thisisdefinition.com/insights/most-popular-llm)llm
JMIR AI 2025;4:e66796
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Large language model (LLM) refers to advanced artificial intelligence (AI) models designed for natural language processing tasks. LLMs are trained on vast amounts of text data and use deep learning techniques to understand and generate human-like language. They helped transform various fields, including medicine [1]. Some examples of most popular LLMs are Lla MA by Meta, Orca and Phi-1 by Microsoft, BLOOM, Pa LM2 by Google, and GPT by Open AI.
JMIR Med Educ 2025;11:e58801
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