Between map and maze
Reframing trust in healthcare AI
Bibliographic Data
| ID | 17675940 |
|---|---|
| Authors | Hannah S Piehl (Maastro Clinic, corresponding author), Ricky Janssen (0000-0002-4485-1152, Maastricht University), Bart Penders (0000-0002-2661-9181, Department of Health), Rianne Fijten (0000-0002-1964-6317, Maastro Clinic), Rianne R R Fijten |
| Year | 2026 |
| Volume | 41 |
| Issue | 7 |
| Pages | 6925-6935 |
| Publication date | 2026-03-26 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | AI & Society (JOURNAL) |
| Journal identifiers | ISSN: 0951-5666 • E-ISSN: 1435-5655 |
| Publisher | Springer Nature (PUBLISHER • SG) |
| DOI | 10.1007/s00146-026-02997-9 |
| PMID | 42482956 |
| OpenAlex | W7140737867 |
| Language | EN |
| References cited | 70 |
Artificial intelligence (AI) is often presented as a transformative technology for healthcare, promising to augment clinical decision-making, streamline workflows, and enhance diagnostic precision. Yet its integration into healthcare practice is shaped by the complex and often ambiguous notion of trust . While trust in AI has become a recurring theme across disciplines, there has been little systematic analysis of different conceptualizations of the term. This paper addresses this gap through an interdisciplinary scoping review that examines how trust, trustworthiness, distrust, and mistrust are articulated in literature on AI in healthcare. Drawing on 82 publications (2015–2025) retrieved from six databases (Web of Science, Scopus, PubMed, PhilPapers, SocINDEX, and ACM Digital Library), it maps how trust is defined, measured, or problematized, and identifies the conceptual, epistemological, and disciplinary boundaries that shape these approaches. The analysis puts forward five different conceptualizations: trust as a set of designable principles , trust as an attitude or belief , trust as a binary between cognition/affect or human/technology, trust as a structural mechanism within uncertainty, and trust as a relational process embedded in socio-technical systems. These framings illuminate how trust is treated as an attribute to be engineered, a behavior to be calibrated, or a relation to be cultivated—often removed from clinical practice. Yet, across literature, trust disruptions which manifest as distrust, mistrust, overtrust, and undertrust are undertheorized and are typically framed as obstacles to overcome rather than phenomena warranting analysis in their own right. By introducing these diverging conceptualizations, the paper argues that trust in healthcare AI is less a stable condition than a dynamic negotiation that reveals power relations, uncertainties, and institutional dependencies of AI. Recognizing distrust and mistrust as analytically productive allows for a more reflexive understanding of how trust is enacted, distributed, and contested within healthcare. This review contributes to an emerging body of work that reframes trust not as a prerequisite for technological adoption but as a contested and dynamic concept
Clinical Practice · Cognitive reframing · Discipline · Health care · Relation (database) · Set (abstract data type) · Theme (computing) · Transformative learning · Artificial Intelligence in Healthcare and Education · Ethics and Social Impacts of AI · Explainable Artificial Intelligence (XAI
Fairness and Abstraction in Sociotechnical Systems
Bias in computer systems
Scoping studies
Guidance for conducting systematic scoping reviews
Trust and Antitrust
Trust in medical artificial intelligence
Implementing Ethics in Healthcare AI-Based Applications
How the different explanation classes impact trust calibration
AI-Inclusivity in Healthcare
Misplaced Trust and Distrust
Healthy Mistrust
Trust criteria for artificial intelligence in health
Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI
Intentional machines
The influence of artificial intelligence within health-related risk work
Artificial intelligence and society
Sorting Things Out
Distrust
Seeing without knowing
Bringing the pregnancy test home from the hospital
Implicit trust in clinical decision-making by multidisciplinary teams
Navigating artificial intelligence in care homes
Why we should talk about institutional (dis)trustworthiness and medical machine learning
After Method
If You're Going to Trust the Machine, Then That Trust Has Got to Be Based on Something
Misunderstood misunderstanding
The trustification of AI. Disclosing the bridging pillars that tie trust and AI together
The Body Multiple
Situated Knowledges
Medical technologies
| Highly cited | No |
|---|