e.g. mhealth
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Skip search results from other journals and go to results- 46 Journal of Medical Internet Research
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The AI Reviewer: Evaluating AI’s Role in Citation Screening for Streamlined Systematic Reviews
adoption
JMIR Form Res 2025;9:e58366
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Studying the Potential Effects of Artificial Intelligence on Physician Autonomy: Scoping Review
Physician autonomy has been found to play a role in physician acceptance and adoption of medical technologies [3], and in particular, AI [1]. Although physician autonomy has become an increasingly important concept in recent decades [4-7], there is still no consensus definition in the literature. However, physician autonomy is generally seen as including both clinical freedoms, as well as social and economic freedoms [6,7].
JMIR AI 2025;4:e59295
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Especially in the early adoption phases, users should be supported in familiarizing themselves with the tools and integrating them into their work routines. Potential solutions could include more built-in guidance functions in the tool or establishing additional structures (eg, service centers) that can provide direct user support.
Furthermore, our findings emphasize that self-monitoring demands a lot of clients, and can be difficult and burdensome for people with mental health problems.
JMIR Hum Factors 2025;12:e60096
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Thus, we aim to understand these professionals’ perspectives on AI adoption, emphasizing the development of trust and integration strategies and identifying approaches that position AI as a valuable tool complementing human expertise, not replacing it [10]. Consequently, the central research question we propose is as follows: How can AI be effectively integrated into practice in a way that ensures health care professionals’ acceptance and trust in its advice?
JMIR Hum Factors 2025;12:e59010
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Previous studies have highlighted substantial barriers to the successful adoption of AI in health care, including issues related to trust; potential risks of harm; accuracy and perceived usefulness; reproducibility; evidentiary standards; and ethical, legal, and societal concerns [7,8]. In addition, uncertainty surrounding postadoption outcomes further complicates the implementation process [7].
J Med Internet Res 2025;27:e67485
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Existing literature suggests that design quality can significantly influence staff adoption and implementation of the technologies [21]. The traditional TAM component of “Perceived Ease of Use” was not fully applicable in this context, as our primary aim was to compare the perceptions of patients and staff regarding their user experiences and perceptions of the PDIS.
JMIR Aging 2025;8:e60506
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The aim of the study was to perform a user-centered evaluation of the Go-Beyond program using user engagement data routinely collected and assess the reach, adoption, engagement, and completion rates. Furthermore, the study aimed to gather user feedback to enhance the concept version of the program’s modules, identify opportunities for further improvement, and generate future recommendations.
JMIR Form Res 2025;9:e60868
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The ever-evolving mobile health (m Health) technology ecosystem and the widespread adoption of mobile devices present a unique opportunity to tackle the aforementioned challenges hindering youth access to quality mental health care. With nearly 6.4 billion smartphone mobile network subscriptions in 2022, and projections to reach 7.7 billion by 2028 [4], m Health stands as a powerful platform for delivering on-demand mental health support and interventions to adolescents.
JMIR Res Protoc 2024;13:e59372
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Current State of Community-Driven Radiological AI Deployment in Medical Imaging
By focusing on key aspects of AI research and deployment in the health care domain [9], this paper aims to facilitate the adoption of AI technologies in real-world scenarios, empowering radiologists and health care professionals to leverage AI’s potential for improved efficiency and patient care.
By highlighting the barriers [10,11] associated with deploying AI models and providing practical ways to overcome them, we aim to offer a comprehensive understanding of the imaging informatics landscape.
JMIR AI 2024;3:e55833
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Clinician adoption of smart home technology requires clinicians to use and understand a new form of evidence. Accordingly, using the findings of this systematic review, we developed a theoretical model to support clinician readiness for and adoption of HAS technology, which will be discussed at the end of the Results section.
JMIR Aging 2024;7:e64367
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