ArticleNature medicine2023
A reinforcement learning model for AI-based decision support in skin cancer.
Article in Nature medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.
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Who cites it
39 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluating the Performance of Artificial Intelligence in Accurately Detecting Skin Cancer: An Umbrella Review of Systematic Reviews and Meta-analyses.American journal of clinical dermatology · 2026Pooled it
- Artificial intelligence in assisted localization of fetal ultrasound standard planes: a narrative review.Quantitative imaging in medicine and surgery · 2026Review
- Artificial Intelligence in Dermatology: New Regulatory Aspects After the European AI Act.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2026Article
- Optimizing hepatitis C diagnosis through reinforcement learning feature selection and multi-model machine learning evaluation.Scientific reports · 2026Article
- A brain-inspired computational framework for image-based risk assessment.Scientific reports · 2026Article
- Liver transplant donor-recipient matching with offline reinforcement learning.NPJ digital medicine · 2026Article
- Precision medicine in cutaneous melanoma-A comprehensive review.Journal of the European Academy of Dermatology and Venereology : JEADV · 2026Review
- Auxiliary diagnosis of hyperpigmented skin diseases using multimodal deep learning.Chinese medical journal · 2026Article
- Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data.IEEE reviews in biomedical engineering · 2026Review
- Novel nomogram and decision curve analysis for predicting head and neck skin cancer risk.Scientific reports · 2025Article
- The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence.Nature medicine · 2025Review
- Optimizing long term disease prevention with reinforcement learning: a framework for precision lipid control.NPJ digital medicine · 2025Article
- A multimodal vision foundation model for clinical dermatology.Nature medicine · 2025Article
- Effect of patient-contextual skin images in human- and artificial intelligence-based diagnosis of melanoma: Results from the 2020 SIIM-ISIC melanoma classification challenge.Journal of the European Academy of Dermatology and Venereology : JEADV · 2025Article
- Article
- Deep learning quantifies pathologists' visual patterns for whole slide image diagnosis.Nature communications · 2025Article
- Closing the AI generalisation gap by adjusting for dermatology condition distribution differences across clinical settings.EBioMedicine · 2025Article
- Reinforcement learning for healthcare operations management: methodological framework, recent developments, and future research directions.Health care management science · 2025Review
- Cutaneous melanoma.Nature reviews. Disease primers · 2025Review
- Multimodal large language models address clinical queries in laryngeal cancer surgery: a comparative evaluation of image interpretation across different models.International journal of surgery (London, England) · 2025Article
Corrections and comments
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Authors and funding
17 authors.
Funding
Abstract
We investigated whether human preferences hold the potential to improve diagnostic artificial intelligence (AI)-based decision support using skin cancer diagnosis as a use case. We utilized nonuniform rewards and penalties based on expert-generated tables, balancing the benefits and harms of various diagnostic errors, which were applied using reinforcement learning. Compared with supervised learning, the reinforcement learning model improved the sensitivity for melanoma from 61.4% to 79.5% (95% confidence interval (CI): 73.5-85.6%) and for basal cell carcinoma from 79.4% to 87.1% (95% CI: 80.3-93.9%). AI overconfidence was also reduced while simultaneously maintaining accuracy. Reinforcement learning increased the rate of correct diagnoses made by dermatologists by 12.0% (95% CI: 8.8-15.1%) and improved the rate of optimal management decisions from 57.4% to 65.3% (95% CI: 61.7-68.9%). We further demonstrated that the reward-adjusted reinforcement learning model and a threshold-based model outperformed naïve supervised learning in various clinical scenarios. Our findings suggest the potential for incorporating human preferences into image-based diagnostic algorithms.
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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.