# FOLTMed reaches 85.4% accuracy across 42 medical image benchmarks

_Published Monday, September 21, 2026 at 3:06 AM EDT · AI, Science · Latest · Tier 2 — Notable_

Researchers introduced FOLTMed, a medical image model trained on more than one million visual question-answer pairs assembled from de-identified images and clinician commentary. In preprint tests, the model achieved 85.4% macro accuracy across 42 medical visual-question-answering benchmarks.

The researchers used clinician-in-the-loop verification to build the ThoughtMed-1M dataset and reported that FOLTMed exceeded other state-of-the-art models by 3% to 5% on factuality and similarity measures for its test set. The results remain benchmark findings from a preprint, not evidence of clinical deployment or patient outcomes.

## Sources

- [cs.AI updates on arXiv.org](https://arxiv.org/abs/2609.06914)

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Canonical: https://techandbusiness.org/newswire/zvncSj1hLOhrQJ2c3JSfbz
Published: 2026-09-21T07:06:19.746Z
Story chronology: 2026-09-21T04:00:00.000Z
Retrieved: 2026-09-22T12:18:37.003Z
Publisher: Tech & Business (techandbusiness.org)