Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma
German Cancer Research Center · Heidelberg University · +87 more institutions
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…
Citation impact
- FWCI
- 42.93
- Percentile
- 100%
- References
- 59
Authors
124- TCTirtha ChandaCorresponding
German Cancer Research Center, Heidelberg University
- KHKatja Hauser
German Cancer Research Center, Heidelberg University
- SHSarah Hobelsberger
Technische Universität Dresden
- TBTabea-Clara Bucher
German Cancer Research Center, Heidelberg University
- CNCarina Nogueira Garcia
German Cancer Research Center, Heidelberg University
Topics & keywords
- Transparency (behavior)
- Medical diagnosis
- Confidence interval
- Computer science
- Domain (mathematical analysis)
- Medicine
- Pathology
- Mathematics