Please use this identifier to cite or link to this item:
10.1038/s41467-023-43095-4
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DC Field | Value | Language |
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dc.contributor.author | Chanda, Tirtha | - |
dc.contributor.author | Hauser, Katja | - |
dc.contributor.author | Hobelsberger, Sarah | - |
dc.contributor.author | Brinker, Titus J | - |
dc.contributor.author | Reader Study Consortium | - |
dc.contributor.author | Kaļva, Artūrs | - |
dc.contributor.author | Bondare-Ansberga , Vanda | - |
dc.contributor.author | Balcere, Alise | - |
dc.date.accessioned | 2024-02-13T16:25:01Z | - |
dc.date.available | 2024-02-13T16:25:01Z | - |
dc.date.issued | 2024-12 | - |
dc.identifier.citation | Chanda , T , Hauser , K , Hobelsberger , S , Brinker , T J , Reader Study Consortium , Kaļva , A , Bondare-Ansberga , V & Balcere , A 2024 , ' Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma ' , Nature Communications , vol. 15 , no. 1 , 524 . https://doi.org/10.1038/s41467-023-43095-4 | - |
dc.identifier.issn | 2041-1723 | - |
dc.identifier.other | PubMedCentral: PMC10789736 | - |
dc.identifier.uri | https://dspace.rsu.lv/jspui/handle/123456789/15155 | - |
dc.description | Publisher Copyright: © 2024, The Author(s). | - |
dc.description.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. | en |
dc.format.extent | 17 | - |
dc.format.extent | 3311358 | - |
dc.language.iso | eng | - |
dc.relation.ispartof | Nature Communications | - |
dc.rights | info:eu-repo/semantics/openAccess | - |
dc.subject | Humans | - |
dc.subject | Trust | - |
dc.subject | Artificial Intelligence | - |
dc.subject | Dermatologists | - |
dc.subject | Melanoma/diagnosis | - |
dc.subject | Diagnosis, Differential | - |
dc.subject | 3.2 Clinical medicine | - |
dc.subject | 1.1. Scientific article indexed in Web of Science and/or Scopus database | - |
dc.title | Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma | en |
dc.type | /dk/atira/pure/researchoutput/researchoutputtypes/contributiontojournal/article | - |
dc.identifier.doi | 10.1038/s41467-023-43095-4 | - |
dc.contributor.institution | Department of Public Health and Epidemiology | - |
dc.contributor.institution | Department of Dermatology and Venereology | - |
dc.identifier.url | http://www.scopus.com/inward/record.url?scp=85182489709&partnerID=8YFLogxK | - |
dc.description.status | Peer reviewed | - |
Appears in Collections: | Research outputs from Pure / Zinātniskās darbības rezultāti no ZDIS Pure |
Files in This Item:
File | Size | Format | |
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Dermatologist-like_explainable.pdf | 3.23 MB | Adobe PDF | View/Open |
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