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

arXiv: https://arxiv.org/abs/2303.12806 · HTML: https://mlsanity.com/paper/2303.12806/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 80 references that are in the map.
- First posted on arXiv 17 Mar 2023.

## Builds on (most cited first, 27 of 27)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)](https://mlsanity.com/paper/1711.11279/) (2017)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1710.05006/) (2017)
- [ERASER: A Benchmark to Evaluate Rationalized NLP Models](https://mlsanity.com/paper/1911.03429/) (2019)
- [European Union regulations on algorithmic decision-making and a "right to explanation"](https://mlsanity.com/paper/1606.08813/) (2016)
- [BCN20000: Dermoscopic Lesions in the Wild](https://mlsanity.com/paper/1908.02288/) (2019)
- [Concept Whitening for Interpretable Image Recognition](https://mlsanity.com/paper/2002.01650/) (2020)
- [Tell Me Where to Look: Guided Attention Inference Network](https://mlsanity.com/paper/1802.10171/) (2018)
- [What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use](https://mlsanity.com/paper/1905.05134/) (2019)
- [Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives](https://mlsanity.com/paper/2208.07422/) (2022)
- [On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors](https://mlsanity.com/paper/2005.02000/) (2020)
- [Resolving challenges in deep learning-based analyses of histopathological images using explanation methods](https://mlsanity.com/paper/1908.06943/) (2019)
- [Identifying Melanoma Images using EfficientNet Ensemble: Winning Solution to the SIIM-ISIC Melanoma Classification Challenge](https://mlsanity.com/paper/2010.05351/) (2020)
- [ExAID: A Multimodal Explanation Framework for Computer-Aided Diagnosis of Skin Lesions](https://mlsanity.com/paper/2201.01249/) (2022)
- [Unsupervised Domain Adaptation with Adversarial Residual Transform Networks](https://mlsanity.com/paper/1804.09578/) (2018)
- [Diagnostic Accuracy of Content Based Dermatoscopic Image Retrieval with Deep Classification Features](https://mlsanity.com/paper/1810.09487/) (2018)
- [DermX: an end-to-end framework for explainable automated dermatological diagnosis](https://mlsanity.com/paper/2202.06956/) (2022)
- [Global Saliency: Aggregating Saliency Maps to Assess Dataset Artefact Bias](https://mlsanity.com/paper/1910.07604/) (2019)
- [Taking Advice from (Dis)Similar Machines: The Impact of Human-Machine Similarity on Machine-Assisted Decision-Making](https://mlsanity.com/paper/2209.03821/) (2022)

## Built on by (most cited first, 18 of 18)

- [MedPAIR: Measuring Physicians and AI Relevance Alignment in Medical Question Answering](https://mlsanity.com/paper/2505.24040/) (2025)
- [A Survey on Human-Centered Evaluation of Explainable AI Methods in Clinical Decision Support Systems](https://mlsanity.com/paper/2502.09849/) (2025)
- [Evaluating Deep Learning-based Melanoma Classification using Immunohistochemistry and Routine Histology: A Three Center Study](https://mlsanity.com/paper/2309.03494/) (2023)
- [Clinical Melanoma Diagnosis with Artificial Intelligence: Insights from a Prospective Multicenter Study](https://mlsanity.com/paper/2401.14193/) (2024)
- [Superhuman performance in urology board questions by an explainable large language model enabled for context integration of the European Association of Urology guidelines: the UroBot study](https://mlsanity.com/paper/2406.01428/) (2024)
- [Explainable AI Enhances Glaucoma Referrals, Yet the Human-AI Team Still Falls Short of the AI Alone](https://mlsanity.com/paper/2407.11974/) (2024)
- [MultiTask Learning AI system to assist BCC diagnosis with dual explanation](https://mlsanity.com/paper/2407.00104/) (2024)
- [Methodological Explainability Evaluation of an Interpretable Deep Learning Model for Post-Hepatectomy Liver Failure Prediction Incorporating Counterfactual Explanations and Layerwise Relevance Propagation: A Prospective In Silico Trial](https://mlsanity.com/paper/2408.03771/) (2024)
- [Image class translation: visual inspection of class-specific hypotheticals and classification based on translation distance](https://mlsanity.com/paper/2408.08973/) (2024)
- [Safer Skin Lesion Classification with Global Class Activation Probability Map Evaluation and SafeML](https://mlsanity.com/paper/2508.20776/) (2025)
- [Explainable Melanoma Diagnosis with Contrastive Learning and LLM-based Report Generation](https://mlsanity.com/paper/2512.06105/) (2025)
- [When Life Gives You AI, Will You Turn It Into A Market for Lemons? Understanding How Information Asymmetries About AI System Capabilities Affect Market Outcomes and Adoption](https://mlsanity.com/paper/2601.21650/) (2026)
- [Does Explanation Correctness Matter? Linking Computational XAI Evaluation to Human Understanding](https://mlsanity.com/paper/2603.25251/) (2026)
- [Beyond Forgetting in Continual Medical Image Segmentation: A Comprehensive Benchmark Study](https://mlsanity.com/paper/2605.06160/) (2026)
- [Towards Fine-Grained and Verifiable Concept Bottleneck Models](https://mlsanity.com/paper/2605.14210/) (2026)
- [Measuring What Matters: Synthetic Benchmarks for Concept Bottleneck Models](https://mlsanity.com/paper/2606.04326/) (2026)
- [SketchXplain: Intuitive Visual Explanations of Image Classifiers with Sketches](https://mlsanity.com/paper/2606.17646/) (2026)
- [Beyond "AI Helps Humans": Decision-Targeted Evaluation Design for Human-Agent Teams in the Agentic Era](https://mlsanity.com/paper/2609.05527/) (2026)

## Abstract (arXiv)

Although artificial intelligence (AI) systems have been shown to improve the accuracy of initial melanoma diagnosis, the lack of transparency in how these systems identify melanoma poses severe obstacles to user acceptance. Explainable artificial intelligence (XAI) methods can help to increase transparency, but most XAI methods are unable to produce precisely located domain-specific explanations, making the explanations difficult to interpret. Moreover, the impact of XAI methods on dermatologists has not yet been evaluated. Extending on two existing classifiers, we developed an XAI system that produces text and region based explanations that are easily interpretable by dermatologists alongside its differential diagnoses of melanomas and nevi. To evaluate this system, we conducted a three-part reader study to assess its impact on clinicians' diagnostic accuracy, confidence, and trust in the XAI-support. We showed that our XAI's explanations were highly aligned with clinicians' explanations and that both the clinicians' trust in the support system and their confidence in their diagnoses were significantly increased when using our XAI compared to using a conventional AI system. The clinicians' diagnostic accuracy was numerically, albeit not significantly, increased. This work demonstrates that clinicians are willing to adopt such an XAI system, motivating their future use in the clinic.