ArticleNature communications2024
Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
61 citing papers in PubMed, 1 synthesis or guideline pooled it, 126 citations in OpenAlex.
- Facilitators and Barriers to Implementing AI in Routine Medical Imaging: Systematic Review and Qualitative Analysis.Journal of medical Internet research · 2025Pooled it
- Artificial intelligence in oncology: empowering clinicians for responsible integration.ESMO real world data and digital oncology · 2026Review
- An interpretable multi-instance learning method for accurate differentiation of malignant and benign laryngeal lesions in laryngoscopy.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Beyond black-box AI: Comparing ChatGPT-4 interpretability and accuracy to CNNs in melanocytic lesions diagnosis.Journal der Deutschen Dermatologischen Gesellschaft = Journal of the German Society of Dermatology : JDDG · 2026Article
- Divergent impacts of explainable AI for dermatological diagnosis on clinicians versus lay people.Nature medicine · 2026Article
- Automated Skin Lesion and Cancer Detection Using Computer Vision: A Comprehensive Review.Bioengineering (Basel, Switzerland) · 2026Review
- Review
- Digital Dermatology.Journal der Deutschen Dermatologischen Gesellschaft = Journal of the German Society of Dermatology : JDDG · 2026Review
- Review
- DataXflowGen for GenAI-driven model generation.Scientific reports · 2026Article
- The Evolving Utility of Artificial Intelligence-Based Tools for the Detection of Heart Failure and Cardiomyopathies: From Potential to Implementation.Current heart failure reports · 2026Review
- MultiTask learning AI system to assist BCC diagnosis with dual explanation.Scientific reports · 2026Article
- DermaScanAI an explainable hybrid deep learning framework for automated skin lesion classification using dual attention and metadata fusion.Scientific reports · 2026Article
- Explainable deep learning framework incorporating medical knowledge for insulin titration in diabetes.Communications medicine · 2026Article
- Multimodal artificial intelligence for enhanced skin cancer diagnosis and prognosis.Discover oncology · 2026Review
- Explainability in AI-enabled medical neurotechnology: a scoping review.Journal of neuroengineering and rehabilitation · 2026Article
- Emerging Artificial Intelligence Models for Estimating Breslow Thickness from Dermoscopic Images.Biomedicines · 2026Review
- Between map and maze: reframing trust in healthcare AI.AI & society · 2026Review
- Virtual medicine: medical AI in human health and diseases.Military Medical Research · 2026Review
- A reliable and explainable deep learning framework for clinical-grade endocrine disorder risk prediction and decision support.Frontiers in artificial intelligence · 2026Article
1 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
36 authors at 20 institutions in 31 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.