Psychological Factors Influencing Appropriate Reliance on AI-enabled Clinical Decision Support Systems: Experimental Web-Based Study Among Dermatologists
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| Publicado en: | Journal of Medical Internet Research vol. 27 (2025), p. e58660 |
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| Autor principal: | |
| Otros Autores: | , , , |
| Publicado: |
Gunther Eysenbach MD MPH, Associate Professor
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| Resumen: | Background:Artificial intelligence (AI)–enabled decision support systems are critical tools in medical practice; however, their reliability is not absolute, necessitating human oversight for final decision-making. Human reliance on such systems can vary, influenced by factors such as individual psychological factors and physician experience.Objective:This study aimed to explore the psychological factors influencing subjective trust and reliance on medical AI’s advice, specifically examining relative AI reliance and relative self-reliance to assess the appropriateness of reliance.Methods:A survey was conducted with 223 dermatologists, which included lesion image classification tasks and validated questionnaires assessing subjective trust, propensity to trust technology, affinity for technology interaction, control beliefs, need for cognition, as well as queries on medical experience and decision confidence.Results:A 2-tailed t test revealed that participants’ accuracy improved significantly with AI support (t222=−3.3; P<.001; Cohen d=4.5), but only by an average of 1% (1/100). Reliance on AI was stronger for correct advice than for incorrect advice (t222=4.2; P<.001; Cohen d=0.1). Notably, participants demonstrated a mean relative AI reliance of 10.04% (139/1384) and a relative self-reliance of 85.6% (487/569), indicating a high level of self-reliance but a low level of AI reliance. Propensity to trust technology influenced AI reliance, mediated by trust (indirect effect=0.024, 95% CI 0.008-0.042; P<.001), and medical experience negatively predicted AI reliance (indirect effect=–0.001, 95% CI –0.002 to −0.001; P<.001).Conclusions:The findings highlight the need to design AI support systems in a way that assists less experienced users with a high propensity to trust technology to identify potential AI errors, while encouraging experienced physicians to actively engage with system recommendations and potentially reassess initial decisions. |
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| ISSN: | 1438-8871 |
| DOI: | 10.2196/58660 |
| Fuente: | Library Science Database |