---
title: 'Software Daily Summary: What Happened on October 13, 2026'
headline: Neonatal X-ray AI improves across 30 hospitals with local retraining
description: A study found that sharing model updates, rather than patient images,
improved tube-position assessment at most participating sites.
url: https://thebench.bio/news/software/2026-10-13
topic: software
date: '2026-10-13'
covers:
- '2026-10-13'
date_published: '2026-10-13T12:03:13Z'
date_modified: '2026-10-13T12:03:13Z'
tags:
- Software
sources: 1
publisher: TheBench.Bio
---
# Software Daily Summary: What Happened on October 13, 2026
**Neonatal X-ray AI improves across 30 hospitals with local retraining**
A study found that sharing model updates, rather than patient images, improved tube-position assessment at most participating sites.
## Today in brief
A neonatal X-ray model adapted through local retraining had lower landmark-localization error than other approaches at 29 of 30 hospitals in an international study. [1] Hospitals used 50 radiographs each, sharing model parameters rather than patient data. [1]
## The day in numbers
- **0.67 cm**: Landmark-localization error with continual learning, compared with 1.29 cm for hospital-specific fine-tuning and 4.30 cm for the adult-only model. [1]
## Also published
- [Icms2yield AI-Predicted Full-Spectrum UV Correction Factors: An Analyte-Standard-Free Framework for Reaction Yield Quantification](), Mac in Chem
## Sources
1. [Repurposing adult AI radiograph models for neonatal care through continual learning: an international multicentre study](), Nature Biomedical Engineering, 2026-10-13
---
TheBench.Bio wrote this from the reporting cited above, with a language model drafting and editing.
[1]: