Evidence map›Paper›PMID 39983118›Full record

ArticleJMIR human factors2025

Prioritizing Trust in Podiatrists' Preference for AI in Supportive Roles Over Diagnostic Roles in Health Care: Qualitative Interview and Focus Group Study.

Mohammed A Tahtali, Chris C P Snijders, Corné W G M Dirne, Pascale M Le Blanc

Abstract read
In one paragraph

Article in JMIR human factors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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

4 authors.

Mohammed A Tahtali *Department of Industrial Engineering & Management, Fontys University of Applied Sciences, Eindhoven, The Netherlands.ORCID 0009-0009-8080-0959
Chris C P Snijders *Department of Industrial Engineering & Innovation Sciences, Human Technology Interaction group, Eindhoven University of Technology, Eindhoven, The Netherlands.ORCID 0000-0001-6165-7645
Corné W G M Dirne *Department of Industrial Engineering & Management, Fontys University of Applied Sciences, Eindhoven, The Netherlands.ORCID 0009-0001-2508-6168
Pascale M Le Blanc *Department of Industrial Engineering & Innovation Sciences, Human Performance Management group, Eindhoven University of Technology, Eindhoven, The Netherlands.ORCID 0000-0003-4693-9980

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAs artificial intelligence (AI) evolves, its roles have expanded from helping out with routine tasks to making complex decisions, once the exclusive domain of human experts. This shift is pronounced in health care, where AI aids in tasks ranging from image recognition in radiology to personalized treatment plans, demonstrating the potential to, at times, surpass human accuracy and efficiency. Despite AI's accuracy in some critical tasks, the adoption of AI in health care is a challenge, in part because of skepticism about being able to rely on AI decisions.

objectiveThis study aimed to identify and delve into more effective and acceptable ways of integrating AI into a broader spectrum of health care tasks.

methodsWe included 2 qualitative phases to explore podiatrists' views on AI in health care. Initially, we interviewed 9 podiatrists (7 women and 2 men) with a mean age of 41 (SD 12) years and aimed to capture their sentiments regarding the use and role of AI in their work. Subsequently, a focus group with 5 podiatrists (4 women and 1 man) with a mean age of 54 (SD 10) years delved into AI's supportive and diagnostic roles on the basis of the interviews. All interviews were recorded, transcribed verbatim, and analyzed using Atlas.ti and QDA-Miner, using both thematic analysis for broad patterns and framework analysis for structured insights per established guidelines.

resultsOur research unveiled 9 themes and 3 subthemes, clarifying podiatrists' nuanced views on AI in health care. Key overlapping insights in the 2 phases included a preference for using AI in supportive roles, such as triage, because of its efficiency and process optimization capabilities. There is a discernible hesitancy toward leveraging AI for diagnostic purposes, driven by concerns regarding its accuracy and the essential nature of human expertise. The need for transparency and explainability in AI systems emerged as a critical factor for fostering trust in both phases.

conclusionsThe findings highlight a complex view from podiatrists on AI, showing openness to its application in supportive roles while exercising caution with diagnostic use. This result is consistent with a careful introduction of AI into health care in roles, such as triage, in which there is initial trust, as opposed to roles that ask the AI for a complete diagnosis. Such strategic adoption can mitigate initial resistance, gradually building the confidence to explore AI's capabilities in more nuanced tasks, including diagnostics, where skepticism is currently more pronounced. Adopting AI stepwise could thus enhance trust and acceptance across a broader range of health care tasks, aligning technology integration with professional comfort and patient care standards.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelPodiatryTrustAdultFemaleFocus GroupsHumansInterviews as TopicMaleMiddle AgedQualitative ResearchacceptanceadoptionAIAI’s role in health careartificial intelligenceattitudedecision-makingdiabetes and podiatristsexperiencefocus groupfootopinionperceptionpodiatryprofessionalqualitativethematictrust

Identifiers

PMID39983118
PMCPMC11890136

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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.