A reinforcement learning model for AI-based decision support in skin cancer
Menée à partir de plus de 11 000 images dermatoscopiques de lésions cutanées (mélanomes, carcinomes basocellulaires, kératoses actiniques, carcinomes intra-épidermiques, naevi, dermatofibromes, lésions kératinocytaires bénignes et lésions vasculaires), cette étude évalue la performance d'un algorithme d'apprentissage automatique par renforcement pour établir un diagnostic et éclairer la décision thérapeutique
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.
Nature Medicine , article en libre accès, 2023