# MedJev study reports calibration gains for laptop-based medical predictions

_Published Wednesday, October 28, 2026 at 12:13 AM EDT · AI, Science · Latest · Tier 2 — Notable_

Researchers report in a preprint that MedJev, an open medical decision model that runs locally on a laptop, can adapt predictions to local patient populations using 50 to 200 labeled examples. Their two-parameter recalibration method reduced expected calibration error to 0.007.

MedJev answers structured questions in a single forward pass without generating text. Across ten medical benchmarks, its in-hospital mortality prediction achieved an AUROC of 0.926, exceeding XGBoost's 0.902. The researchers also released their code and evaluation harness.

Performance varied by task: MedJev underperformed supervised models on emergency-department triage admission, scoring 0.745 versus 0.809. Patient-level predictions also showed substantial miscalibration before adjustment, with in-hospital mortality expected calibration error of 0.286.

## Sources

- [cs.AI updates on arXiv.org](https://arxiv.org/abs/2610.36006)

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Canonical: https://techandbusiness.org/newswire/0-ECBqi78ih1XLAEf8t3MY
Published: 2026-10-28T04:13:53.556Z
Story chronology: 2026-10-28T04:00:00.000Z
Retrieved: 2026-10-28T11:03:55.381Z
Publisher: Tech & Business (techandbusiness.org)