Prioritizing Trust in Podiatrists’ Preference for AI in Supportive Roles Over Diagnostic Roles in Health Care: Qualitative Interview and Focus Group Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".