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

Simple item page
dc.contributor.authorChanda, Tirtha
dc.contributor.authorHauser, Katja
dc.contributor.authorHobelsberger, Sarah
dc.contributor.authorBucher, Tabea-Clara
dc.contributor.authorGarcia, Carina Nogueira
dc.contributor.authorWies, Christoph
dc.contributor.authorKittler, Harald
dc.contributor.authorTschandl, Philipp
dc.contributor.authorNavarrete-Dechent, Cristian
dc.contributor.authorPodlipnik, Sebastian
dc.contributor.authorChousakos, Emmanouil
dc.contributor.authorCrnaric, Iva
dc.contributor.authorMajstorovic, Jovana
dc.contributor.authorAlhajwan, Linda
dc.contributor.authorForeman, Tanya
dc.contributor.authorPeternel, Sandra
dc.contributor.authorSarap, Sergei
dc.contributor.authorOzdemir, Irem
dc.contributor.authorBarnhill, Raymond L.
dc.contributor.authorLlamas-Velasco, Mar
dc.contributor.authorPoch, Gabriela
dc.contributor.authorKorsing, Soeren
dc.contributor.authorSondermann, Wiebke
dc.contributor.authorGellrich, Frank Friedrich
dc.contributor.authorHeppt, Markus V.
dc.contributor.authorErdmann, Michael
dc.contributor.authorHaferkamp, Sebastian
dc.contributor.authorDrexler, Konstantin
dc.contributor.authorGoebeler, Matthias
dc.contributor.authorSchilling, Bastian
dc.contributor.authorUtikal, Jochen S.
dc.contributor.authorGhoreschi, Kamran
dc.contributor.authorFroehling, Stefan
dc.contributor.authorKrieghoff-Henning, Eva
dc.contributor.authorBrinker, Titus J.
dc.date.accessioned2025-01-20T17:09:37Z
dc.date.available2025-01-20T17:09:37Z
dc.date.issued2024
dc.description.abstractArtificial 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.
dc.description.abstractArtificial intelligence has become popular as a cancer classification tool, but there is distrust of such systems due to their lack of transparency. Here, the authors develop an explainable AI system which produces text- and region-based explanations alongside its classifications which was assessed using clinicians' diagnostic accuracy, diagnostic confidence, and their trust in the system.
dc.description.funderFederal Ministry of Health, Berlin, Germany (Grant Number: 2520DAT801) and Ministry of Social Affairs, Health and Integration of the Federal State Baden-Wrttemberg, Germany (Grant Number: 53 - 5400.1-007/5).
dc.fuente.origenWOS
dc.identifier.doi10.1038/s41467-023-43095-4
dc.identifier.eissn2041-1723
dc.identifier.urihttps://doi.org/10.1038/s41467-023-43095-4
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/91058
dc.identifier.wosidWOS:001143918100017
dc.issue.numero1
dc.language.isoen
dc.revistaNature communications
dc.rightsacceso restringido
dc.titleDermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma
dc.typeartículo
dc.volumen15
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Dermatologist.pdf
Size:
3.16 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Artículos de revistas