Atilla P. Kiraly

Abstract Artificial intelligence has high potential for impact in healthcare applications, but its training and deployment are challenging due to diverse data, a complex spectrum of possible tasks and important privacy needs. High-performing foundation models that enable data-efficient fine-tuning for diverse downstream tasks can meaningfully accelerate development in this domain. We introduce MedGemma, a collection of medical vision-language foundation models based on Gemma 3.

Abstract Predictive artificial intelligence (AI) systems based on deep learning have been shown to achieve expert-level identification of diseases in multiple medical imaging settings, but can make errors in cases accurately diagnosed by clinicians and vice versa. We developed Complementarity-Driven Deferral to Clinical Workflow (CoDoC), a system that can learn to decide between the opinion of a predictive AI model and a clinical workflow.

Access provided by Correction to: Nature Medicine https://doi.org/10.1038/s41591-019-0447-x, published online 20 May 2019. In the version of this article originally published, there was an error in the phrase “This dataset contained 1,739 cases (27 cancer-positives)” in the main text. The number 1,739 should have been 1,139. There was also an error in the Fig. 4c legend. In the phrase “comprising n = 1,739 cases”, the number 1,739 again should have been 1,139. Additionally, in the Extended Data Fig.