Evidence map›Paper›PMID 40138691›Full record

ArticleJMIR formative research2025

Perceived Trust and Professional Identity Threat in AI-Based Clinical Decision Support Systems: Scenario-Based Experimental Study on AI Process Design Features.

Sophia Ackerhans, Kai Wehkamp, Rainer Petzina, Daniel Dumitrescu, Carsten Schultz

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Article in JMIR formative research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
–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

17 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

5 authors.

Sophia AckerhansKiel Institute of Responsible Innovation, University of Kiel, Kiel, Germany.ORCID 0009-0005-9269-6854
Kai WehkampMedical School Hamburg, Hamburg, Germany.ORCID 0000-0003-3536-4958
Rainer PetzinaMedical School Hamburg, Hamburg, Germany.ORCID 0000-0002-0688-5482
Daniel DumitrescuClinic for General and Interventional Cardiology/Angiology, Heart and Diabetes Center North Rhine-Westphalia, Ruhr-University Bochum, Medical Faculty OWL (University Bielefeld), Bad Oeynhausen, Germany.ORCID 0009-0002-7340-5271
Carsten SchultzKiel Institute of Responsible Innovation, University of Kiel, Kiel, Germany.ORCID 0000-0002-5984-9872

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI)-based systems in medicine like clinical decision support systems (CDSSs) have shown promising results in health care, sometimes outperforming human specialists. However, the integration of AI may challenge medical professionals' identities and lead to limited trust in technology, resulting in health care professionals rejecting AI-based systems.

objectiveThis study aims to explore the impact of AI process design features on physicians' trust in the AI solution and on perceived threats to their professional identity. These design features involve the explainability of AI-based CDSS decision outcomes, the integration depth of the AI-generated advice into the clinical workflow, and the physician's accountability for the AI system-induced medical decisions.

methodsWe conducted a 3-factorial web-based between-subject scenario-based experiment with 292 medical students in their medical training and experienced physicians across different specialties. The participants were presented with an AI-based CDSS for sepsis prediction and prevention for use in a hospital. Each participant was given a scenario in which the 3 design features of the AI-based CDSS were manipulated in a 2×2×2 factorial design. SPSS PROCESS (IBM Corp) macro was used for hypothesis testing.

resultsThe results suggest that the explainability of the AI-based CDSS was positively associated with both trust in the AI system (β=.508; P<.001) and professional identity threat perceptions (β=.351; P=.02). Trust in the AI system was found to be negatively related to professional identity threat perceptions (β=-.138; P=.047), indicating a partially mediated effect on professional identity threat through trust. Deep integration of AI-generated advice into the clinical workflow was positively associated with trust in the system (β=.262; P=.009). The accountability of the AI-based decisions, that is, the system required a signature, was found to be positively associated with professional identity threat perceptions among the respondents (β=.339; P=.004).

conclusionsOur research highlights the role of process design features of AI systems used in medicine in shaping professional identity perceptions, mediated through increased trust in AI. An explainable AI-based CDSS and an AI-generated system advice, which is deeply integrated into the clinical workflow, reinforce trust, thereby mitigating perceived professional identity threats. However, explainable AI and individual accountability of the system directly exacerbate threat perceptions. Our findings illustrate the complex nature of the behavioral patterns of AI in health care and have broader implications for supporting the implementation of AI-based CDSSs in a context where AI systems may impact professional identity.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelDecision Support Systems, ClinicalPhysiciansStudents, MedicalTrustAdultFemaleHumansMaleYoung Adultartificial intelligenceclinical decision support systemsexplainable artificial intelligencehealth careperceptionsphysiciansprofessional identityprofessional identity threat

Identifiers

PMID40138691
PMCPMC11982750

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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.