Actors, artists, and musicians are rightly worried about the impact of AI on their incomes but doctors and scientists welcome the help. They know typewriters didn't make literature worse than writing in longhand and "AI" - LLMs - likewise removes the 'how' of information access so thinkers can get to the 'why.'

In modern government-controlled healthcare, doctors are more pressed for time per patient than ever. Often while relying on incomplete information. Electronic health records contain vast amounts of patient data but much of it remains difficult to interpret quickly, and that is even more challenging for patients with rare diseases or unusual symptoms.

The Icahn School of Medicine at Mount Sinai has developed an artificial intelligence system, called InfEHR, that links unconnected medical events over time and can reveal hidden patterns. In the study, InfEHR analyzed deidentified, privacy-protected electronic records from two hospital systems (Mount Sinai in New York and UC Irvine in California). The investigators turned each patient’s medical timeline—visits, lab tests, medications, vital signs—into a network that showed how events connected over time.

 The AI studied many of these networks to learn which combinations of clues tend to appear when a hidden condition is present.



With a small set of doctor-confirmed examples to calibrate it, the system checked whether it could correctly flag two real-world problems: newborns who develop sepsis despite negative blood cultures and patients who develop a kidney injury after surgery. Its performance in identifying patients with the diagnosis was compared with current clinical rules and validated across both hospitals. Notably, the system could also signal when the record lacked sufficient information, allowing it to respond “not sure” as a safety feature.

The study found that InfEHR can detect disease patterns that are invisible when examining isolated data. For neonatal sepsis without positive blood cultures—a rare, life-threatening condition—InfEHR was 12–16 times more likely to identify affected infants than current methods. For postoperative kidney injury, the system flagged at-risk patients 4–7 times more effectively. Importantly, InfEHR achieved this without needing large amounts of training data, learning directly from patient records and adapting across hospitals and populations.

Citation: Kauffman, J., Holmes, E., Vaid, A. et al. InfEHR: Clinical phenotype resolution through deep geometric learning on electronic health records. Nat Commun 16, 8475 (2025). https://doi.org/10.1038/s41467-025-63366-6