mlsanityData to 7 Oct 2026

A deep learning system for differential diagnosis of skin diseases

arXiv 1909.05382 ↗First posted 11 Sep 2019eess.IV cs.CVcited by 118

Not on the map. Why?

Abstract (arXiv)

Skin conditions affect an estimated 1.9 billion people worldwide. A shortage of dermatologists causes long wait times and leads patients to seek dermatologic care from general practitioners. However, the diagnostic accuracy of general practitioners has been reported to be only 0.24-0.70 (compared to 0.77-0.96 for dermatologists), resulting in referral errors, delays in care, and errors in diagnosis and treatment. In this paper, we developed a deep learning system (DLS) to provide a differential diagnosis of skin conditions for clinical cases (skin photographs and associated medical histories). The DLS distinguishes between 26 skin conditions that represent roughly 80% of the volume of skin conditions seen in primary care. The DLS was developed and validated using de-identified cases from a teledermatology practice serving 17 clinical sites via a temporal split: the first 14,021 cases for development and the last 3,756 cases for validation. On the validation set, where a panel of three board-certified dermatologists defined the reference standard for every case, the DLS achieved 0.71 and 0.93 top-1 and top-3 accuracies respectively. For a random subset of the validation set (n=963 cases), 18 clinicians reviewed the cases for comparison. On this subset, the DLS achieved a 0.67 top-1 accuracy, non-inferior to board-certified dermatologists (0.63, p<0.001), and higher than primary care physicians (PCPs, 0.45) and nurse practitioners (NPs, 0.41). The top-3 accuracy showed a similar trend: 0.90 DLS, 0.75 dermatologists, 0.60 PCPs, and 0.55 NPs. These results highlight the potential of the DLS to augment general practitioners to accurately diagnose skin conditions by suggesting differential diagnoses that may not have been considered. Future work will be needed to prospectively assess the clinical impact of using this tool in actual clinical workflows.

Key facts. Cited by 118 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Sep 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 19; 2022: 23; 2023: 29; 2024: 19; 2025: 16; 2026: 12.

118 papers.

Sort:

By area:

  1. 2021Big Self-Supervised Models Advance Medical Image Classificationcited by 320
  2. 2021Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Datasetcited by 184
  3. 2021Disparities in Dermatology AI: Assessments Using Diverse Clinical Imagescited by 132
  4. 2023Towards Accurate Differential Diagnosis with Large Language Modelscited by 115
  5. 2021Learning Optimal Conformal Classifierscited by 108
  6. 2023Generative models improve fairness of medical classifiers under distribution shiftscited by 90
  7. 2022Masked Image Modeling Advances 3D Medical Image Analysiscited by 71
  8. 2022Integrated multimodal artificial intelligence framework for healthcare applicationscited by 69
  9. 2021Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditionscited by 68
  10. 2022Detecting Shortcut Learning for Fair Medical AI using Shortcut Testingcited by 52
  11. 2022Diagnosing failures of fairness transfer across distribution shift in real-world medical settingscited by 45
  12. 2022Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Studycited by 45
38 more
  1. 2026MedGemma 1.5 Technical Reportcited by 43
  2. 2022Robust and Efficient Medical Imaging with Self-Supervisioncited by 40
  3. 2021Supervised Transfer Learning at Scale for Medical Imagingcited by 38
  4. 2022Towards Transparency in Dermatology Image Datasets with Skin Tone Annotations by Experts, Crowds, and an Algorithmcited by 35
  5. 2023Conformal prediction under ambiguous ground truthcited by 31
  6. 2025Derm1M: A Million-scale Vision-Language Dataset Aligned with Clinical Ontology Knowledge for Dermatologycited by 28
  7. 2023Coherent Concept-based Explanations in Medical Image and Its Application to Skin Lesion Diagnosiscited by 26
  8. 2022Improving dermatology classifiers across populations using images generated by large diffusion modelscited by 25
  9. 2021Single Model Deep Learning on Imbalanced Small Datasets for Skin Lesion Classificationcited by 23
  10. 2025MEDDxAgent: A Unified Modular Agent Framework for Explainable Automatic Differential Diagnosiscited by 23
  11. 2023Augmenting medical image classifiers with synthetic data from latent diffusion modelscited by 21
  12. 2024Conformalized Credal Regions for Classification with Ambiguous Ground Truthcited by 19
  13. 2023SkinGPT-4: An Interactive Dermatology Diagnostic System with Visual Large Language Modelcited by 18
  14. 2022CIRCLe: Color Invariant Representation Learning for Unbiased Classification of Skin Lesionscited by 17
  15. 2021Exponentiated Gradient Reweighting for Robust Training Under Label Noise and Beyondcited by 16
  16. 2022Distributed Contrastive Learning for Medical Image Segmentationcited by 16
  17. 2023Devil is in the Queries: Advancing Mask Transformers for Real-world Medical Image Segmentation and Out-of-Distribution Localizationcited by 16
  18. 2023CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scanscited by 15
  19. 2025MAKE: Multi-Aspect Knowledge-Enhanced Vision-Language Pretraining for Zero-shot Dermatological Assessmentcited by 12
  20. 2021Annotation-efficient cancer detection with report-guided lesion annotation for deep learning-based prostate cancer detection in bpMRIcited by 11
  21. 2023Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directionscited by 10
  22. 2022A Reduction to Binary Approach for Debiasing Multiclass Datasetscited by 9
  23. 2023Achieving Reliable and Fair Skin Lesion Diagnosis via Unsupervised Domain Adaptationcited by 9
  24. 2024From Majority to Minority: A Diffusion-based Augmentation for Underrepresented Groups in Skin Lesion Analysiscited by 9
  25. 2025Doctor Approved: Generating Medically Accurate Skin Disease Images through AI-Expert Feedbackcited by 9
  26. 2021Can self-training identify suspicious ugly duckling lesions?cited by 7
  27. 2022An Accurate and Explainable Deep Learning System Improves Interobserver Agreement in the Interpretation of Chest Radiographcited by 7
  28. 2023Evaluating AI systems under uncertain ground truth: a case study in dermatologycited by 7
  29. 2024Investigating the Quality of DermaMNIST and Fitzpatrick17k Dermatological Image Datasetscited by 7
  30. 2024Tissue Concepts: supervised foundation models in computational pathologycited by 7
  31. 2023A Novel Multi-Task Model Imitating Dermatologists for Accurate Differential Diagnosis of Skin Diseases in Clinical Imagescited by 6
  32. 2021On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathycited by 5
  33. 2022AFFIRM: Affinity Fusion-based Framework for Iteratively Random Motion correction of multi-slice fetal brain MRIcited by 5
  34. 2022Identifying the Context Shift between Test Benchmarks and Production Datacited by 5
  35. 2022Medical Diagnosis with Large Scale Multimodal Transformers: Leveraging Diverse Data for More Accurate Diagnosiscited by 5
  36. 2021Semi-weakly Supervised Contrastive Representation Learning for Retinal Fundus Imagescited by 4
  37. 2024Interpretable Differential Diagnosis with Dual-Inference Large Language Modelscited by 4
  38. 2024Enhancing Skin Disease Diagnosis: Interpretable Visual Concept Discovery with SAMcited by 4

50 most cited of 118 All 118 (JSON)