Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma - Chanda, Tirtha; Hauser, Katja; Hobelsberger, Sarah; Bucher, Tabea-Clara; Garcia, Carina Nogueira; Wies, Christoph; Kittler, Harald; Tschandl, Philipp; Navarrete-Dechent, Cristian; Podlipnik, Sebastian; Chousakos, Emmanouil; Crnaric, Iva; et al. (Nature Communications, 2024)
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.