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Found 3 papers, 1 papers with code
Accuracy of a Vision-Language Model on Challenging Medical Cases
Conclusions: GPT-4V outperformed human respondents on challenging medical cases and was able to synthesize information from both images and text, but performance deteriorated when images were added to highly informative text.
Augmenting medical image classifiers with synthetic data from latent diffusion models
While hundreds of artificial intelligence (AI) algorithms are now approved or cleared by the US Food and Drugs Administration (FDA), many studies have shown inconsistent generalization or latent bias, particularly for underrepresented populations.
Improving dermatology classifiers across populations using images generated by large diffusion models
Dermatological classification algorithms developed without sufficiently diverse training data may generalize poorly across populations.
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