AI Model Uses CRRT Pressure Waveforms to Predict Daily Mortality in AKI Patients

Researchers developed a machine learning approach that predicts day-by-day mortality risk for critically ill patients with acute kidney injury receiving continuous renal replacement therapy. Unlike existing tools that rely mainly on electronic health record data, this method incorporates minute-level machine pressure waveform signals. The work suggests that treatment device data can add predictive value beyond standard clinical parameters.

arXiv Paper Revisits Correctness Measures for Uncertainty Estimation in Clinical VLMs

A new preprint examines how correctness is defined when vision-language models are used to make clinical predictions from medical images and electronic health records. The authors argue that current uncertainty estimation methods may be evaluated in ways that do not reflect whether a prediction is actually reliable. The work targets safer deployment by improving how unreliable outputs are detected.

Structured EHR Features Used to Predict 30-Day Readmission on MIMIC-IV

A new arXiv paper proposes knowledge-enriched structured features drawn from electronic health records to predict whether a patient will be readmitted within 30 days. The authors position the approach as an alternative to models that depend on discharge summaries and pretrained language models, which require clinical notes and carry heavy computational costs. Evaluation is conducted on the MIMIC-IV dataset.

LongAgent: History-Guided Agentic Search for Longitudinal Outcome Prediction

A new arXiv paper introduces LongAgent, an agentic search method that uses patient history to predict future medical outcomes from longitudinal data. The authors note that such datasets are heterogeneous, with many variables collected across different sources and time points. The approach aims to extract representations from this messy, multi-source data that improve downstream outcome prediction.

OpenAI lets healthcare organizations link EHR data to ChatGPT

OpenAI announced that healthcare organizations can now connect electronic health records and other industry data sources to ChatGPT. The company says this gives clinicians a secure way to bring in patient context and medical research while using the tool. The feature is aimed at clinical and health-sector users rather than general consumers.

WHY IT MATTERS ↘Connecting EHR data to ChatGPT shifts competition from model quality to who controls the compliant data pipeline, letting OpenAI and its integration partners capture clinical workflows that EHR incumbents like Epic and Oracle have treated as their own. It also raises the governance stakes, since patient-context inference in a general-purpose LLM puts HIPAA alignment, auditability, and de-identification practices under direct enterprise scrutiny.