Nature research paper: Health system-scale language models are all-purpose prediction engines
, which further emphasizes the necessity of accounting for the domain shift from general to medical text for LLM work in the medical sciences. Although we have not compared these approaches directly , we believe that this could be an interesting future direction for research and that, in the end, approaches combining these different approaches to language modelling may prove to be complementary depending on the use case.
The ultimate validation of our approach must come from randomized controlled trials of interventions tied to individual task predictions to assess their clinical impact and from user feedback as we continue to integrate NYUTron into health systems. As we plan this within our own health system, we recommend the consideration of different levels of intervention depending on the predicted risk of patients for each task.
It is a long-standing dream for physicians to have AI assistants observing care along with them and chiming in with predictions and advice. To take a step towards this futuristic vision, we trained an LLM, NYUTron, on the entire EHR of a large healthcare system to read physician notes and make several of these predictions across a wide range of clinical and operational tasks.
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