Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma

Abstract

Artificial intelligence (AI) systems have been shown to help dermatologists diagnose melanoma more accurately, however they lack transparency, hindering user acceptance. Explainable AI (XAI) methods can help to increase transparency, yet often lack precise, domain-specific explanations. Moreover, the impact of XAI methods on dermatologists’ decisions has not yet been evaluated. Building upon previous research, we introduce an XAI system that provides precise and domain-specific explanations alongside its differential diagnoses of melanomas and nevi. Through a three-phase study, we assess its impact on dermatologists’ diagnostic accuracy, diagnostic confidence, and trust in the XAI-support. Our results show strong alignment between XAI and dermatologist explanations. We also show that dermatologists’ confidence in their diagnoses, and their trust in the support system significantly increase with XAI compared to conventional AI. This study highlights dermatologists’ willingness to adopt such XAI systems, promoting future use in the clinic.

Explainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review

“…Therefore, to provide trustworthiness, the reliability of the results through clinical evaluation is needed. Researchers have already used XAI domain-specific explanations to improve understanding, interpretation, trustworthiness, and reliability of the results in different medical domains for evaluating health interventions [125], disease causal pathway analysis [126], mental health surveillance and precision resource allocation [127], precision dermatology and disease diagnosis [128], immune response predictors [129], and investigating the links between socioenvironmental risk factors and Alzheimer's disease [130].…”