ArticleJournal of medical ethics2022
Before and beyond trust: reliance in medical AI.
Article in Journal of medical ethics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 3 of them syntheses that pooled it.
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
39 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Diagnostic performance of AI-assisted endoscopy diagnosis of digestive system tumors: an umbrella review.Frontiers in oncology · 2025Pooled it
- A worldwide itinerary of research ethics in science for a better social responsibility and justice: a bibliometric analysis and review.Frontiers in research metrics and analytics · 2025Pooled it
- Non-empirical methods for ethics research on digital technologies in medicine, health care and public health: a systematic journal review.Medicine, health care, and philosophy · 2024Pooled it
- Automation dependence in AI-assisted colonoscopy: impact of system reliability and surgeon experience on navigation performance.Surgical endoscopy · 2026Article
- Responsibility Beyond the Black Box: A Medical Student's Reading of Giubilini's Paper "It Is Not About AI, It's About Humans".Journal of bioethical inquiry · 2026Article
- AI and Its Shifting Roles in the Therapeutic Relationship: Implications for Precision Medicine.Journal of personalized medicine · 2026Review
- Towards clinically interpretable machine learning in emergency surgery: feature importance and insights across clinical time points in abdominal pain cases.Langenbeck's archives of surgery · 2026Article
- The Mediation of AI Trust on AI Uncertainties and AI Competence Among Nurses: A Cross-Sectional Study.Journal of advanced nursing · 2026Article
- Clinical AI is Not (Yet) Trustworthy-But It Could Be.Journal of medical Internet research · 2026Article
- Artificial intelligence in retinal care: transforming the doctor-patient partnership.International journal of retina and vitreous · 2026Review
- Public perceptions of health data sharing for artificial intelligence research: a qualitative focus group study in the UK.BMJ digital health & AI · 2026Article
- The ethics of using virtual assistants to help people in vulnerable positions access care.Journal of medical ethics · 2025Article
- It Is Not About AI, It's About Humans. Responsibility Gaps and Medical AI.Journal of bioethical inquiry · 2025Article
- Public trust of AI in healthcare in South Africa: results of a survey.BMC medical ethics · 2025Article
- Trust in Medical AI: The Case of mHealth Diabetes Apps.Journal of evaluation in clinical practice · 2025Article
- German surgeons' perspective on the application of artificial intelligence in clinical decision-making.International journal of computer assisted radiology and surgery · 2025Article
- Increasing transparency of computer-aided detection impairs decision-making in visual search.Psychonomic bulletin & review · 2025Article
- Acceptability of wearable technology for the early detection of dementia-causing diseases: perspectives from the CODEC II cohort.BMC digital health · 2025Article
- Adaptable robots, ethics, and trust: a qualitative and philosophical exploration of the individual experience of trustworthy AI.AI & society · 2025Article
- Intersection of Performance, Interpretability, and Fairness in Neural Prototype Tree for Chest X-Ray Pathology Detection: Algorithm Development and Validation Study.JMIR formative research · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Artificial intelligence (AI) is changing healthcare and the practice of medicine as data-driven science and machine-learning technologies, in particular, are contributing to a variety of medical and clinical tasks. Such advancements have also raised many questions, especially about public trust. As a response to these concerns there has been a concentrated effort from public bodies, policy-makers and technology companies leading the way in AI to address what is identified as a "public trust deficit". This paper argues that a focus on trust as the basis upon which a relationship between this new technology and the public is built is, at best, ineffective, at worst, inappropriate or even dangerous, as it diverts attention from what is actually needed to actively warrant trust. Instead of agonising about how to facilitate trust, a type of relationship which can leave those trusting vulnerable and exposed, we argue that efforts should be focused on the difficult and dynamic process of ensuring reliance underwritten by strong legal and regulatory frameworks. From there, trust could emerge but not merely as a means to an end. Instead, as something to work in practice towards; that is, the deserved result of an ongoing ethical relationship where there is the appropriate, enforceable and reliable regulatory infrastructure in place for problems, challenges and power asymmetries to be continuously accounted for and appropriately redressed.
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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.