# Researchers release ChestPheNoT for auditable radiology report analysis

Digest AI · Research · published 2026-09-29T04:00:00Z

Canonical: https://digestai.news/story/researchers-release-chestphenot-for-auditable-radiology-report-analysi

## Summary

Researchers introduced **ChestPheNoT**, a compact 0.5–3B language model designed to extract structured medical findings, status labels (present/absent/uncertain), and verbatim evidence spans from radiology reports. The model avoids external APIs by running locally, addressing concerns about data leaving institutional networks. It uses hybrid silver supervision (CheXbert + 72B models) and GRPO refinement to improve accuracy under distribution shifts, outperforming CheXbert on cross-institution detection by **+2.0 F1** and matching larger models on detection tasks.

ChestPheNoT achieves **99% evidence span locatability** and an **auditable-F1 of 47.5**, surpassing Qwen2.5-7B by **7.6 points**. The team claims it enables deployable, explainable radiology analytics without relying on cloud-based inference. Code and prompts will be open-sourced on GitHub.

## Key points

- ChestPheNoT extracts findings, status labels, and evidence spans from radiology reports locally, not via APIs
- Outperforms CheXbert on cross-institution detection by +2.0 F1 and matches larger models on detection tasks
- Achieves 99% evidence span locatability and 47.5 auditable-F1, surpassing Qwen2.5-7B by 7.6 points

## Why it matters

Locally deployable models like ChestPheNoT reduce reliance on cloud APIs for sensitive medical data, improving auditability and compliance while offering competitive performance to much larger models.

## Sources

1. [ChestPheNoT: Deployable, Auditable Label-Status-Evidence Extraction from Radiology Reports](https://arxiv.org/abs/2609.31629) (arXiv cs.CL, 2026-09-29, primary source)

## Cite

Digest AI, "Researchers release ChestPheNoT for auditable radiology report analysis", 29 September 2026, https://digestai.news/story/researchers-release-chestphenot-for-auditable-radiology-report-analysi

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