Closing the AI Generalization Gap by Adjusting for Dermatology Condition Distribution Differences Across Clinical Settings

This study highlights the challenges AI algorithms face in classifying dermatological conditions across different clinical settings due to variations in skin condition distribution. The research identifies that these distribution differences, rather than demographic factors or image capture methods, are the primary cause of errors when AI is applied to new data sources. The study outlines steps to mitigate this generalization gap, including adjusting training data to better reflect the condition distribution of new sources. It also finds that both end-to-end fine-tuning and fine-tuning only the classification layer can achieve similar performance improvements. This approach can guide the adaptation of AI algorithms to diverse clinical environments.