![]() ![]() This versatility represents a stark change from the previous generation of AI models, which were designed to solve specific tasks, one at a time.ĭriven by growing datasets, increases in model size and advances in model architectures, foundation models offer previously unseen abilities. Individual models can now achieve state-of-the-art performance on a wide variety of problems, ranging from answering questions about texts to describing images and playing video games 2, 3, 4. We expect that GMAI-enabled applications will challenge current strategies for regulating and validating AI devices for medicine and will shift practices associated with the collection of large medical datasets.įoundation models-the latest generation of AI models-are trained on massive, diverse datasets and can be applied to numerous downstream tasks 1. Here we identify a set of high-impact potential applications for GMAI and lay out specific technical capabilities and training datasets necessary to enable them. Models will in turn produce expressive outputs such as free-text explanations, spoken recommendations or image annotations that demonstrate advanced medical reasoning abilities. Built through self-supervision on large, diverse datasets, GMAI will flexibly interpret different combinations of medical modalities, including data from imaging, electronic health records, laboratory results, genomics, graphs or medical text. GMAI models will be capable of carrying out a diverse set of tasks using very little or no task-specific labelled data. We propose a new paradigm for medical AI, which we refer to as generalist medical AI (GMAI). The exceptionally rapid development of highly flexible, reusable artificial intelligence (AI) models is likely to usher in newfound capabilities in medicine. ![]()
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