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 (Corresponding author-nrf), Chris C.P. Snijders, Corné W.G.M. Dirne, Pascale M. Le Blanc

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

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Samenvatting

BACKGROUND: As 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.

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

METHODS: We 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.

RESULTS: Our 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.

CONCLUSIONS: The 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.

Originele taal-2Engels
Artikelnummere59010
Aantal pagina's19
TijdschriftJMIR Human Factors
Volume12
DOI's
StatusGepubliceerd - 21 feb. 2025

Bibliografische nota

©Mohammed A Tahtali, Chris C P Snijders, Corné W G M Dirne, Pascale M Le Blanc. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 21.02.2025.

Financiering

The authors extend their heartfelt gratitude to all individuals who generously shared their insights and experiences, making this research possible. Special thanks are extended to Ruben Gloudemans for his invaluable assistance with conducting interviews, engaging in thorough analyses and participating in wide-ranging discussions that significantly enriched this work. The authors appreciate RondOm Podiatrists for providing access to the FootCheck app, an essential resource for our study. This work is part of the project Vertrouwen in Algoritmes (project number 023.017.109), under the auspices of the Promotiebeurs voor Leraren program, generously funded by the Dutch Research Council (NWO). Additionally, this study was part of the Technology Support for Diabetes project, which is subsidized by SIA RAAK EXZ.EXZ.01.004. The research efforts of the authors were greatly supported by these contributions, for which they are profoundly grateful.

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