e.g. mhealth
Search Results (1 to 10 of 114 Results)
Download search results: CSV END BibTex RIS
Skip search results from other journals and go to results- 29 Journal of Medical Internet Research
- 18 JMIR Medical Informatics
- 16 JMIR Medical Education
- 15 JMIR Formative Research
- 13 JMIR Mental Health
- 9 JMIR AI
- 4 JMIRx Med
- 2 JMIR Cancer
- 2 JMIR Research Protocols
- 1 Interactive Journal of Medical Research
- 1 JMIR Aging
- 1 JMIR Dermatology
- 1 JMIR Public Health and Surveillance
- 1 JMIR mHealth and uHealth
- 1 Journal of Participatory Medicine
- 0 Medicine 2.0
- 0 iProceedings
- 0 JMIR Human Factors
- 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 JMIR Pediatrics and Parenting
- 0 JMIR Perioperative Medicine
- 0 JMIR Nursing
- 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)
These findings are further analyzed by the LLM to extract disease-specific features and rank their importance. Then the summarized features are used by the LLM to generates diagnostic questions to construct a logical reasoning pathway. During the prediction phase, the workflow retrieves similar imaging reports via a hybrid RAG framework to refine the LLM’s understanding of disease patterns, ultimately generating comprehensive and precise results for disease prediction.
J Med Internet Res 2025;27:e72638
Download Citation: END BibTex RIS
Indeed, creating a hybrid chatbot with both rule-based and LLM components can offer a versatile and streamlined user experience by ensuring that key information is covered in the rule-based components of the chatbot and allowing for the LLM component to support complex, open-ended queries that are not covered in the scripted content.
JMIR Cancer 2025;11:e65848
Download Citation: END BibTex RIS
Integrating Nurse Preferences Into AI-Based Scheduling Systems: Qualitative Study
llm
JMIR Form Res 2025;9:e67747
Download Citation: END BibTex RIS
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
Download Citation: END BibTex RIS
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
Download Citation: END BibTex RIS
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
Download Citation: END BibTex RIS
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
Download Citation: END BibTex RIS
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
Download Citation: END BibTex RIS
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
Download Citation: END BibTex RIS
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
Download Citation: END BibTex RIS