e.g. mhealth
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Skip search results from other journals and go to results- 194 Journal of Medical Internet Research
- 73 JMIR Medical Education
- 68 JMIR Formative Research
- 56 JMIR Medical Informatics
- 43 JMIR AI
- 31 JMIR Research Protocols
- 28 JMIR Mental Health
- 25 JMIR Human Factors
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- 9 Interactive Journal of Medical Research
- 8 JMIR Diabetes
- 7 JMIR mHealth and uHealth
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- 5 JMIR Infodemiology
- 4 JMIR Rehabilitation and Assistive Technologies
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- 3 JMIR Pediatrics and Parenting
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- 2 Asian/Pacific Island Nursing Journal
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The downstream task was prognostic AI and ML modeling, and performance was measured by AUROC when LGBM and MLP models were trained on synthetic and tested on real data (ie, TSTR).
In general, the median deviation of the AI and ML performance derived from the synthetic data (ie, TSTR) from the one derived from the real data (ie, train real test real) was low across all health care populations (Table 3).
J Med Internet Res 2025;27:e77893
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Use of Wearable Sensors to Assess Fall Risk in Neurological Disorders: Systematic Review
These methods prepare the raw data for more effective AI analysis by enhancing data quality and extracting meaningful features. Arpan et al [44] used unscented Kalman filter techniques to integrate data from multiple sensors, facilitating a more accurate AI-based prediction of fall events [44]. Some of the included studies (3/19, 16%) explored the implementation of AI models that operate in real time to provide immediate insights or predictive analytics.
JMIR Mhealth Uhealth 2025;13:e67265
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A final instruction was provided requesting Copilot AI to condense to 4 to 6 themes (Make it 4 to 6 themes). This instruction was not applicable to the other AI platforms, which already provided major themes alone. We assessed the overlap between the themes derived by humans and the AI platforms. The investigators repeated the AI-based analysis for each platform to verify findings.
JMIR Form Res 2025;9:e69892
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Quo Vadis, AI-Empowered Doctor?
In the event of an adverse outcome, physicians also risk penalization by juries whether or not an AI recommendation is accepted or overruled [48,49]. A rigorous discussion of the legal ramifications of using AI in clinical decision-making is beyond the scope of this viewpoint, but in light of the above considerations, the most prudent use of medical AI may be to confirm an existing medical decision, rather than as a means to augment care [50].
JMIR Med Educ 2025;11:e70079
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AI and Primary Care: Scoping Review
AI also shows promise for skin lesion assessment in primary care: an AI morphology classifier reached 68% on top-1 accuracy across 44 conditions [29], and a handheld elastic-scattering spectroscopy device boosted skin-cancer diagnostic sensitivity from 67% to 88% [30].
J Med Internet Res 2025;27:e65950
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This platform and app will provide support for health behavior change (eg, artificial intelligence [AI]–supported personalized exercise prescription [EP]) and facilitate disease-related self-management based on current clinical guidelines and in accordance with patients’ individual needs.
JMIR Res Protoc 2025;14:e66283
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How AI-Based Digital Rehabilitation Improves End-User Adherence: Rapid Review
Some of these technologies include AI-based digital and personalized rehabilitation mobile apps, AI-driven virtual reality and augmented reality rehabilitation, sensors, robotic devices, and AI-powered gamification and telerehabilitation [4,10].
Despite the key benefits of these technology-driven interventions—such as improved accessibility, affordability, and their availability from the convenience of homes—most of the AI-based rehabilitation applications are in the early stages of development [12,14-16].
JMIR Rehabil Assist Technol 2025;12:e69763
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First, given the rapid evolution of wearable artificial intelligence (AI) technology and our focus on modern methodologies, we restricted the search to articles published after 2015. Second, while changes in mental health states (eg, emotions, mood, and stress) may serve as potential indicators of disorders such as depression or anxiety [41], examining these factors in isolation fails to reflect clinical diagnoses of mental health conditions [42,43].
J Med Internet Res 2025;27:e77066
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Artificial Intelligence (AI) and Emergency Medicine: Balancing Opportunities and Challenges
Health care providers face liability concerns when AI-driven recommendations lead to misdiagnoses or suboptimal treatment. The unpredictable nature of AI systems is complex. As legal frameworks evolve, questions arise about responsibility when errors stem from “black-box” AI. Should providers be held accountable for following AI advice? Can liabilities extend to AI developers or institutions? Jurisdictions worldwide are addressing these issues [46,53].
JMIR Med Inform 2025;13:e70903
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However, over the last several years, technological advances in artificial intelligence (AI) have offered promising solutions [2,3]. AI, an interdisciplinary field, aims to develop systems capable of performing tasks that typically require human reasoning [4]. Machine learning, a subfield of AI, creates algorithms capable of learning and improving from experience without explicit programming [5,6].
Interact J Med Res 2025;14:e56240
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