Evidence map›Paper›PMID 41155069›Full record

ReviewBioengineering (Basel, Switzerland)2025

From Trust in Automation to Trust in AI in Healthcare: A 30-Year Longitudinal Review and an Interdisciplinary Framework.

Kelvin K L Wong, Yong Han, Yifeng Cai, Wumin Ouyang, Hemin Du, Chao Liu

Abstract readReview
In one paragraph

Review in Bioengineering (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Kelvin K L WongAI Creativity Laboratory, Academy of Fine Arts, Hunan Normal University, Changsha 410081, China.ORCID 0000-0002-8600-1105
Yong HanFaculty of Innovation and Design, City University of Macau, Macau 999078, China.ORCID 0009-0009-7079-2019
Yifeng CaiGraduate School of Global Convergence, Kangwon National University, Chuncheon 24341, Republic of Korea.
Wumin OuyangFaculty of Innovation and Design, City University of Macau, Macau 999078, China.
Hemin DuFaculty of Innovation and Design, City University of Macau, Macau 999078, China.
Chao LiuYangtze Delta Region Institute, Tsinghua University, Jiaxing 314006, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Human-machine trust has shifted over the past three decades from trust in automation to trust in AI, while research paradigms, disciplines, and problem spaces have expanded. Centered on AI in healthcare, this narrative review offers a longitudinal synthesis that traces and compares phase-specific changes in theory and method, providing design guidance for human-AI systems at different stages of maturity. From a cross-disciplinary view, we introduce an Interdisciplinary Human-AI Trust Research (I-HATR) framework that aligns explainable AI (XAI) with human-computer interaction/human factors engineering (HCI/HFE). We distill three core categories of determinants of human-AI trust in healthcare, user characteristics, AI system attributes, and contextual factors, and summarize the main measurement families and their evolution from self-report to behavioral and psychophysiological approaches, with growing use of multimodal and dynamic evaluation. Finally, we outline key trends, opportunities, and practical challenges to support the development of human-centered, trustworthy AI in healthcare, emphasizing the need to bridge actual trustworthiness and perceived trust through shared metrics, uncertainty communication, and trust calibration.

Indexed as

human-AI in healthcarehuman-centered and trustworthy AII-HATR frameworkXAI-HCI alignmentXAI in healthcare

Identifiers

PMID41155069
PMCPMC12562135

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.