e.g. mhealth
Search Results (1 to 10 of 52 Results)
Download search results: CSV END BibTex RIS
Skip search results from other journals and go to results- 19 Journal of Medical Internet Research
- 9 JMIR Medical Education
- 8 JMIR Medical Informatics
- 4 JMIR Formative Research
- 4 JMIR Mental Health
- 3 JMIR AI
- 2 JMIR Dermatology
- 1 JMIR Cancer
- 1 JMIR Nursing
- 1 JMIR mHealth and uHealth
- 0 Medicine 2.0
- 0 Interactive Journal of Medical Research
- 0 iProceedings
- 0 JMIR Research Protocols
- 0 JMIR Human Factors
- 0 JMIR Public Health and Surveillance
- 0 JMIR Serious Games
- 0 JMIR Rehabilitation and Assistive Technologies
- 0 JMIR Preprints
- 0 JMIR Bioinformatics and Biotechnology
- 0 JMIR Challenges
- 0 JMIR Diabetes
- 0 JMIR Biomedical Engineering
- 0 JMIR Data
- 0 JMIR Cardio
- 0 Journal of Participatory Medicine
- 0 JMIR Pediatrics and Parenting
- 0 JMIR Aging
- 0 JMIR Perioperative Medicine
- 0 JMIRx Med
- 0 JMIRx Bio
- 0 JMIR Infodemiology
- 0 Transfer Hub (manuscript eXchange)
- 0 JMIR Neurotechnology
- 0 Asian/Pacific Island Nursing Journal
- 0 Online Journal of Public Health Informatics
- 0 JMIR XR and Spatial Computing (JMXR)
Go back to the top of the page Skip and go to footer section

In particular, large language models (LLMs) have caught the popular imagination, because unlike advanced machine learning or natural language processing (NLP) tools, proprietary LLMs such as Chat GPT do not require technical knowledge - their user-friendly interfaces and capacity for accepting plain language as inputs democratizing access to the AI revolution for the average person.
J Med Internet Res 2025;27:e75666
Download Citation: END BibTex RIS
Go back to the top of the page Skip and go to footer section

To address this, this study applies large language models (LLMs), which excel at interpreting nuanced, unstructured textual data. Unlike traditional machine learning models—which require extensive feature engineering and often miss deeper linguistic or conceptual structures—LLMs can process entire sentences or paragraphs as coherent units, capturing context, tone, and latent psychological meaning [12].
JMIR Nursing 2025;8:e73672
Download Citation: END BibTex RIS

One low-cost potential solution that could assist health care workers in lower income countries is online clinical assistants powered by artificial intelligence (AI) large language models (LLMs). These clinical assistants could help clinicians to triage patients and identify the causes of their conditions in settings where secondary or tertiary specialist care is unavailable.
The advent of chatbots and AI within the field of medicine is not a new occurrence.
JMIR Form Res 2025;9:e64986
Download Citation: END BibTex RIS

We appreciate the their thoughtful input, which strengthens our discussion on the role of large language models (LLMs) in health care.
Our article aimed to provide a forward-looking perspective on LLMs’ potential in medicine, prioritizing conceptual insights over granular technical details. The reviewers’ points regarding multimodal data integration, image analysis, and resource allocation align with emerging research and underscore LLMs’ transformative capabilities.
J Med Internet Res 2025;27:e73144
Download Citation: END BibTex RIS

The authors synthesized all the possible applications of large language models (LLMs) very well, not only detailing applications related to clinical medicine, but also offering some examples of LLMs’ potential in a broader hospital environment and in public health policies.
J Med Internet Res 2025;27:e71618
Download Citation: END BibTex RIS

It is positioned to benefit from advances in an even wider array of disciplines, including bioinformatics, data science, machine learning, artificial intelligence (AI), natural language processing, large language models (LLMs), systems pharmacology, pharmacogenomics, pharmacometabolomics, and health informatics [2-7].
JMIR AI 2025;4:e65481
Download Citation: END BibTex RIS

The recent rapid innovation of large language models (LLMs) has led to the emergence of Chat GPT, which is the first LLM to provide the data basis and performance to support or carry out medical research and clinical decisions. Nevertheless, the clinical application is currently viewed with a degree of skepticism, as Chat GPT, especially in the earlier versions 2 and 3, demonstrated a marked tendency to “confabulate,” to fabricate statements and even references [2].
JMIR Form Res 2025;9:e63857
Download Citation: END BibTex RIS

In recent years, large language models (LLMs) based on transformer architectures, such as Chat GPT (Open AI), Gemini (Google Deep Mind), and Claude (Anthropic), have emerged as promising tools in the medical domain [13].
J Med Internet Res 2025;27:e67830
Download Citation: END BibTex RIS