e.g. mhealth
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Skip search results from other journals and go to results- 13 Journal of Medical Internet Research
- 8 JMIR Medical Informatics
- 6 JMIR AI
- 5 JMIR Formative Research
- 5 JMIR Human Factors
- 4 JMIR Dermatology
- 4 JMIRx Med
- 3 JMIR Cancer
- 3 JMIR Research Protocols
- 2 JMIR Aging
- 2 JMIR Medical Education
- 2 JMIR Mental Health
- 2 JMIR Nursing
- 1 Asian/Pacific Island Nursing Journal
- 1 JMIR Bioinformatics and Biotechnology
- 1 JMIR Cardio
- 1 JMIR Diabetes
- 1 JMIR Public Health and Surveillance
- 0 Medicine 2.0
- 0 Interactive Journal of Medical Research
- 0 iProceedings
- 0 JMIR mHealth and uHealth
- 0 JMIR Serious Games
- 0 JMIR Rehabilitation and Assistive Technologies
- 0 JMIR Preprints
- 0 JMIR Challenges
- 0 JMIR Biomedical Engineering
- 0 JMIR Data
- 0 Journal of Participatory Medicine
- 0 JMIR Pediatrics and Parenting
- 0 JMIR Perioperative Medicine
- 0 JMIRx Bio
- 0 JMIR Infodemiology
- 0 Transfer Hub (manuscript eXchange)
- 0 JMIR Neurotechnology
- 0 Online Journal of Public Health Informatics
- 0 JMIR XR and Spatial Computing (JMXR)
While AI plays a crucial role, particularly through the use of LLMs and machine learning (ML), it is used selectively within the broader software framework to enhance specific tasks.
LLMs are used in generating related search terms, expanding upon human-generated queries to enhance the comprehensiveness of literature searches. Any LLM can be adapted to TU software, up to date we have used Chat GPT 4 [18].
JMIR Res Protoc 2025;14:e67248
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Large Language Models in Biochemistry Education: Comparative Evaluation of Performance
ml
JMIR Med Educ 2025;11:e67244
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Applications of AI in Predicting Drug Responses for Type 2 Diabetes
AI includes a range of methods, among which ML and deep learning (DL) stand out as 2 prominent subsets [8]. ML is involved in building systems that are capable of learning from data, identifying patterns, and making decisions. On the other hand, DL, is a special form of ML inspired by the structure and function of the brain, especially neural networks. These models learn from data autonomously and are adaptable to various features.
JMIR Diabetes 2025;10:e66831
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A prior study by Beverin et al [7] examined the prediction of total lung capacity from spirometry using three tree-based machine learning (ML) models, achieving a mean squared error of 560.1 m L. They further developed models to classify restrictive ventilatory impairment, achieving a sensitivity and specificity of 83% and 92%, respectively. However, they did not explore prediction of the complete lung volume assessments.
JMIR AI 2025;4:e65456
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The number of studies on artificial intelligence/machine learning (AI/ML) has surged in recent years, exceeding prior expectations [1]. The growth of AI/ML research in health care continues to gain momentum, driven by its potential to enable early detection of serious conditions in resource-constrained settings or facilitate timely identification of patient deterioration that might otherwise go unnoticed, to name a few.
J Med Internet Res 2025;27:e60148
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