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Eric Lefkofsky notes predictive models need detailed patient data for healthcare accuracy
Artificial intelligence models are increasingly being designed to predict patient health outcomes, according to commentary from Eric Lefkofsky. Drawing a parallel with large language models that predict following words in a sentence, Lefkofsky points out the need for comprehensive longitudinal datasets and molecular information to accurately anticipate a patient's medical trajectory. Access to such outcome responses and real-world data is crucial for advancing predictive analytics in healthcare.
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