Improving Automatic Summarization of Radiology Reports through Mid-Training of Large Language Models

📰 ArXiv cs.AI

Mid-training of large language models improves automatic summarization of radiology reports

advanced Published 23 Mar 2026

Action Steps

Pre-training a large language model on a general domain
Mid-training the model on a clinical domain to adapt to radiology report summarization
Fine-tuning the model on a specific radiology report dataset
Evaluating the model's performance on a test dataset to measure improvement

Who Needs to Know This

Data scientists and AI engineers on a healthcare team can benefit from this research to develop more accurate summarization models, improving physician productivity and patient care

Key Insight

💡 Mid-training of LLMs on a clinical domain can improve summarization performance over traditional pre-training and fine-tuning methods

Key Takeaways

Mid-training of large language models improves automatic summarization of radiology reports

Full Article

Title: Improving Automatic Summarization of Radiology Reports through Mid-Training of Large Language Models

Abstract:
arXiv:2603.19275v1 Announce Type: cross Abstract: Automatic summarization of radiology reports is an essential application to reduce the burden on physicians. Previous studies have widely used the "pre-training, fine-tuning" strategy to adapt large language models (LLMs) for summarization. This study proposed a subdomain adaptation through a mid-training method to improve summarization. We explored three adaptation strategies: (1) general-domain pre-training, (2) clinical-domain pre-training, an

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