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; Majstorovic, Jovana; Alhajwan, Linda; Foreman, Tanya; Peternel, Sandra; Sarap, Sergei; Özdemir, İrem; Barnhill, Raymond L; Llamas-Velasco, Mar

doi:10.1038/s41467-023-43095-4

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

Journal

Nature communications

Date Issued

2024-01-15

Author(s)

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

Majstorovic, Jovana

Alhajwan, Linda

Foreman, Tanya

Peternel, Sandra

Sarap, Sergei

Özdemir, İrem

Barnhill, Raymond L

Llamas-Velasco, Mar

Poch, Gabriela

Korsing, Sören

Sondermann, Wiebke

Gellrich, Frank Friedrich

Heppt, Markus V

Erdmann, Michael

Haferkamp, Sebastian

Drexler, Konstantin

Goebeler, Matthias

Schilling, Bastian

Utikal, Jochen S

Ghoreschi, Kamran

Fröhling, Stefan

Krieghoff-Henning, Eva

Brinker, Titus J

Collaborators

Sotirovski, Tomica

Zafirovikj, Zorica

DOI

10.1038/s41467-023-43095-4

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.