While healthcare buzzes with talk of EHR tools that can forecast a patient’s mortality or disease progression, the industry hasn’t reached its “MedGPT moment,” according to Stanford (Calif.) University researchers.
Current EHR generative AI models are roughly where ChatGPT was between GPT-2 and GPT-3 (the AI chatbot is now at GPT-5.2), the experts wrote in a Jan. 7 Nature Medicine commentary. The EHR tools learn patterns from historical data to “generate plausible patient timelines — sequences of diagnoses, procedures, medication codes, lab values, and their timing,” said the authors, including Nigam Shah, MD, PhD, chief data scientist of Palo Alto, Calif.-based Stanford Health Care.
“If 60 out of 100 simulated timelines show a readmission, the model reports a 60% risk,” they wrote. “However, these frequencies are derived from simulated patterns, not necessarily real-world probabilities. Treating a simulation as an ‘oracle’ prediction can lead to unsafe clinical decisions, such as overtreating low-risk patients or missing high-risk ones.”
To reach their potential, these applications need five evaluation criteria: Performance by frequency, calibration, timeline completion, shortcut audits, and out-of-distribution Validation, the researchers wrote. Better calibration, for example, would ensure a “30% predicted risk actually corresponds to 30% of patients experiencing that outcome.”
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