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000301729 245__ $$aDermatologist-like explainable AI enhances melanoma diagnosis accuracy: eye-tracking study.
000301729 260__ $$a[London]$$bSpringer Nature$$c2025
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000301729 520__ $$aArtificial intelligence (AI) systems substantially improve dermatologists' diagnostic accuracy for melanoma, with explainable AI (XAI) systems further enhancing their confidence and trust in AI-driven decisions. Despite these advancements, there remains a critical need for objective evaluation of how dermatologists engage with both AI and XAI tools. In this study, 76 dermatologists participate in a reader study, diagnosing 16 dermoscopic images of melanomas and nevi using an XAI system that provides detailed, domain-specific explanations, while eye-tracking technology assesses their interactions. Diagnostic performance is compared with that of a standard AI system lacking explanatory features. Here we show that XAI significantly improves dermatologists' diagnostic balanced accuracy by 2.8 percentage points compared to standard AI. Moreover, diagnostic disagreements with AI/XAI systems and complex lesions are associated with elevated cognitive load, as evidenced by increased ocular fixations. These insights have significant implications for the design of AI/XAI tools for visual tasks in dermatology and the broader development of XAI in medical diagnostics.
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000301729 650_2 $$2MeSH$$aMelanoma: diagnosis
000301729 650_2 $$2MeSH$$aMelanoma: diagnostic imaging
000301729 650_2 $$2MeSH$$aHumans
000301729 650_2 $$2MeSH$$aArtificial Intelligence
000301729 650_2 $$2MeSH$$aEye-Tracking Technology
000301729 650_2 $$2MeSH$$aDermatologists
000301729 650_2 $$2MeSH$$aSkin Neoplasms: diagnosis
000301729 650_2 $$2MeSH$$aSkin Neoplasms: diagnostic imaging
000301729 650_2 $$2MeSH$$aFemale
000301729 650_2 $$2MeSH$$aDermoscopy: methods
000301729 650_2 $$2MeSH$$aMale
000301729 650_2 $$2MeSH$$aNevus: diagnosis
000301729 650_2 $$2MeSH$$aNevus: diagnostic imaging
000301729 650_2 $$2MeSH$$aAdult
000301729 650_2 $$2MeSH$$aMiddle Aged
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