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Using a reinforcement learning model that includes human preferences, improves the diagnostic accuracy of artificial intelligence (AI) decision support systems for skin cancer, according to the findings of a recent study.
Published in the journal Nature Medicine, researchers from the Department of Dermatology at MedUni Vienna in Austria integrated human decision-making criteria in the form of ‘reward tables‘ into the AI diagnostic system.
This reinforcement learning – a subset of machine learning – allows the system to learn through trial and error, based on both positive and negative feedback from its actions. In other words, it learns from its mistakes and is designed to mimic natural intelligence as closely as possible.
The dermatologist-generated reward tables incorporated the positive and negative consequences of clinical assessments into the decision-making process, from both the physician‘s and the patient‘s perspective. Consequently, an AI diagnosis was not only rated as right or wrong, but rewarded or penalised with a certain number of plus or minus points depending on the impact of the diagnosis or the resulting decisions.
The researchers found greater accuracy in AI diagnostic results was achieved by incorporating this human decision-making criteria, which was designed to balance the benefits and harms of various diagnostic errors, using melanoma and other skin cancers as an example.
Reinforcement learning and diagnostic accuracy
When compared against supervised learning, the reinforcement learning model improved the sensitivity for melanoma diagnosis from 61.4% to 79.5% and for basal cell carcinoma from 79.4% to 87.1%. AI overconfidence was also reduced while simultaneously maintaining accuracy.
In addition, reinforcement learning increased the rate of correct diagnoses made by dermatologists by 12.0% and improved the rate of optimal management decisions from 57.4% to 65.3%.
Commenting on the importance of the results, study lead Harald Kittler, said: ‘In this way, the AI learned to take into account not only image-based features, but also consequences of misdiagnosis in the assessment of benign and malignant skin manifestations.‘
The improved performance of AI-based skin cancer diagnosis also occurs because reinforcement learning reduces the AI‘s overconfidence in its own predictions, making more nuanced and human-compatible suggestions.
‘This, in turn, helps physicians make more accurate decisions tailored to individual patients in complex medical scenarios,‘ Kittler added.
