e.g. mhealth
Search Results (1 to 10 of 150 Results)
Download search results: CSV END BibTex RIS
Skip search results from other journals and go to results- 62 Journal of Medical Internet Research
- 18 JMIR Formative Research
- 18 JMIR Human Factors
- 13 JMIR mHealth and uHealth
- 9 JMIR Public Health and Surveillance
- 6 JMIR Medical Informatics
- 4 JMIR Mental Health
- 4 JMIR Research Protocols
- 3 Interactive Journal of Medical Research
- 3 JMIR Infodemiology
- 2 JMIR AI
- 2 JMIR Aging
- 1 JMIR Cancer
- 1 JMIR Medical Education
- 1 JMIR Pediatrics and Parenting
- 1 JMIR Perioperative Medicine
- 1 JMIR Rehabilitation and Assistive Technologies
- 1 Journal of Participatory Medicine
- 0 Medicine 2.0
- 0 iProceedings
- 0 JMIR Serious Games
- 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 Dermatology
- 0 JMIR Nursing
- 0 JMIRx Med
- 0 JMIRx Bio
- 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)
Trust emerged as the foundational element in the patient-provider relationship. Trust was defined as “the patient’s confidence that the physician will do what is best for the patient” [6]. Digital care could impact the other four themes (adoption factors, confidence in technology, connection, and empowerment), enhancing or diminishing trust.
The clinical context significantly influenced the adoption [61].
J Med Internet Res 2025;27:e70970
Download Citation: END BibTex RIS
Integrating AI technology into health care at scale requires general trust toward and acceptance of AI use in medical contexts. Consequently, a deeper understanding of potential concerns is necessary for an ethical integration of AI into health care. Thus far, empirical evidence on the factors of public trust toward AI in patient health care is limited.
J Med Internet Res 2025;27:e70179
Download Citation: END BibTex RIS
In addition, the framework addresses mediation pathways, where trust operates as an intermediary in 2 distinct relationships. First, the study investigates whether trust mediates the link between ease of use and intent to use, positing that user-friendly design can bolster trust, reinforcing the inclination to adopt Deep Seek.
JMIR Hum Factors 2025;12:e72867
Download Citation: END BibTex RIS
Users' Perceptions and Trust in AI in Direct-to-Consumer mHealth: Qualitative Interview Study
Empirical work assessing trust in health care provider-to-consumer conversational agents has shown that trust has interpersonal (eg, trust in companies and health care providers), social (eg, testimonies from loved ones and health care providers), and technological dimensions (eg, trust in the capabilities of the tool and design cues) [16].
JMIR Mhealth Uhealth 2025;13:e64715
Download Citation: END BibTex RIS
This focus on trust and explainability is essential for clinical use. Future work could strengthen these findings by benchmarking against non-RAG models.
A lack of benchmarking with non-RAG LLMs such as Chat GPT or web searches may be seen as a possible limitation of this study. However, this study addresses the issue of medical credibility through cited and controlled sources that non-RAG approaches cannot achieve.
J Med Internet Res 2025;27:e70131
Download Citation: END BibTex RIS
On a more abstract level, it can be argued that trust plays an essential role in health in general [46] and in data sharing in particular. Since the sharing of data is a socially highly indeterminate constellation for social action, trust is a crucial resource. The risks taken by data donors as well as the potential benefits depend strongly on other actors and can hardly be known in advance.
JMIR Hum Factors 2025;12:e65718
Download Citation: END BibTex RIS
Once recorded on a blockchain, transactions are immutable (cannot be changed or deleted), existing in a verifiable and auditable manner that fosters transparency and trust [1,3]. While blockchain is best known as the underlying technology of cryptocurrencies like Bitcoin, the application of its properties in the health research sector can transform the ways we share, access, and use data [1,4].
JMIR Mhealth Uhealth 2025;13:e65747
Download Citation: END BibTex RIS
However, it remains unknown whether users of these channels are significantly more likely to believe the misinformation and to trust specific geopolitical powers on a nationwide scale. Therefore, we examined associations between (2a) political trust and the 2 sets of misinformation, as well as associations between (2b) media use factors and the 2 sets of misinformation.
JMIR Infodemiology 2025;5:e62913
Download Citation: END BibTex RIS
The literature on trust in AI highlights concerted efforts to foster trust by exploring strategies to address trust issues between AI systems and users. For example, some studies suggest that redefining AI as a supportive adjunct to professionals rather than a standalone solution could promote acceptance [9,10]. In addition, showcasing instances where AI surpasses human experts in specific tasks may enhance humans’ trust in AI [5,9,11].
JMIR Hum Factors 2025;12:e59010
Download Citation: END BibTex RIS
In this paper, we focus on a central inhibitor of successful AI adoption in health care, namely, the low levels of user trust in AI systems [18]. Understanding and fostering trust in AI remains challenging, not only practically but also conceptually. Interpersonal trust constitutes a complex and contested construct in philosophy and social sciences [19-24]. Moreover, accounts diverge on what constitutes being worthy of trust, namely, on the definition of trustworthiness [19,20].
J Med Internet Res 2025;27:e56306
Download Citation: END BibTex RIS