A reinforcement learning model developed to adapt Artificial Intelligence (AI) predictions to human preferences showed better performance for skin cancer diagnoses and optimal management decisions compared to a supervised learning model HaraldKittler
, lowering the threshold results in a high number of patients with >3 excised benign lesions and with an increase of excised melanomas . The RL model would remove 61.8% and monitor 20% of melanomas, outperforming all other models in terms of acceptable management decisions for these melanomas . At the same time, 23 patients would have >3 benign lesions removed. A distinctive feature of the RL model would be the high number of benign lesions .
Here, we demonstrate that the integration of human preferences, represented as reward tables created by experts, enhances the performance of a pretrained AI decision-support system. Improvement is evident in both the system’s standalone performance and its ability to collaborate effectively with dermatologists. Dermatologists’ improvement may be due to the RL model reducing AI overconfidence by considering consequences of management decisions.
Based on our results, we suggest that RL, among other techniques, could be a suitable tool for this purpose, although it is not necessarily the best solution. A limitation of the RL method is that the model must be retrained, whereas other simpler approaches, such as thresholding, can be applied without retraining.
In conclusion, our study shows that incorporating human preferences can improve AI-based diagnostic decision support and that such preferences could be considered when developing AI tools for clinical practice. RL could be a potential alternative to threshold-based methods for creating tailored approaches in complex clinical scenarios. However, additional research, including evaluating patient and provider satisfaction, is necessary to fully uncover the potential of RL in this context.
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