ArticleJournal of evaluation in clinical practice2025
Trust in Medical AI: The Case of mHealth Diabetes Apps.
Article in Journal of evaluation in clinical practice, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- AI-Based Learning Experiences of Nursing Students: A Qualitative Study.Journal of evaluation in clinical practice · 2026Article
- Patient Perceptions of Artificial Intelligence in Diabetes Self-Management: Cross-Sectional Survey Study.JMIR formative research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Over the past few years, mobile health applications (mHealth apps) have gained popularity regarding the management of chronic conditions, such as diabetes, as they are considered to enhance follow-up and treatment. Indeed, these applications are powerful tools that support individualised pharmaceutical and non-pharmaceutical care by remotely monitoring the patient's health status in real-time. Nevertheless, concerns have been raised regarding trust and trustworthiness towards their use, in particular, when they are powered by Artificial Intelligence (AI). Trust in AI is a multifaceted notion that encompasses aspects such as reliance, risk, as well as ethical principles (e.g., respect for human autonomy and prevention of harm). In this paper, we provide an analysis of trust and trustworthiness in AI-powered mHealth apps for diabetes through the lens of philosophy and risk analysis to promote the patient's well-being.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.