mlsanityData to 7 Oct 2026

RadAdapt: Radiology Report Summarization via Lightweight Domain Adaptation of Large Language Models

arXiv 2305.01146 ↗First posted 2 May 2023cs.CLcited by 24

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Abstract (arXiv)

We systematically investigate lightweight strategies to adapt large language models (LLMs) for the task of radiology report summarization (RRS). Specifically, we focus on domain adaptation via pretraining (on natural language, biomedical text, or clinical text) and via discrete prompting or parameter-efficient fine-tuning. Our results consistently achieve best performance by maximally adapting to the task via pretraining on clinical text and fine-tuning on RRS examples. Importantly, this method fine-tunes a mere 0.32% of parameters throughout the model, in contrast to end-to-end fine-tuning (100% of parameters). Additionally, we study the effect of in-context examples and out-of-distribution (OOD) training before concluding with a radiologist reader study and qualitative analysis. Our findings highlight the importance of domain adaptation in RRS and provide valuable insights toward developing effective natural language processing solutions for clinical tasks.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 25 of its 45 references that are in the map. First posted on arXiv 2 May 2023.

Builds on

25 of 45 references.

References · 25

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13 more
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Citing papers per year: 2023: 6; 2024: 12; 2025: 5; 2026: 1.

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  1. 2023Towards Generalist Biomedical AIcited by 286
  2. 2023Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarizationcited by 244
  3. 2023BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Taskscited by 201
  4. 2023A Survey of Large Language Models in Medicine: Progress, Application, and Challengecited by 123
  5. 2024Merlin: A Computed Tomography Vision-Language Foundation Model and Datasetcited by 97
  6. 2024GSCo: Towards Generalizable AI in Medicine via Generalist-Specialist Collaborationcited by 33
  7. 2024MultiMedEval: A Benchmark and a Toolkit for Evaluating Medical Vision-Language Modelscited by 28
  8. 2024Exploring the Efficacy of Large Language Models in Summarizing Mental Health Counseling Sessions: A Benchmark Studycited by 13
  9. 2024A dataset and benchmark for hospital course summarization with adapted large language modelscited by 10
  10. 2023Retrieving Evidence from EHRs with LLMs: Possibilities and Challengescited by 8
  11. 2025Improving Performance, Robustness, and Fairness of Radiographic AI Models with Finely-Controllable Synthetic Datacited by 7
  12. 2024A Data-Centric Approach To Generate Faithful and High Quality Patient Summaries with Large Language Modelscited by 6
12 more
  1. 2025Scaling Up Biomedical Vision-Language Models: Fine-Tuning, Instruction Tuning, and Multi-Modal Learningcited by 6
  2. 2024SPEER: Sentence-Level Planning of Long Clinical Summaries via Embedded Entity Retrievalcited by 5
  3. 2024Best Practices for Large Language Models in Radiologycited by 5
  4. 2024Fine-tuning Large Language Models with Limited Data: A Survey and Practical Guidecited by 4
  5. 2025Evaluating and Mitigating Bias in AI-Based Medical Text Generationcited by 3
  6. 2025Adaptation of Foundation Models for Medical Image Analysis: Strategies, Challenges, and Future Directionscited by 3
  7. 2023SuryaKiran at MEDIQA-Sum 2023: Leveraging LoRA for Clinical Dialogue Summarizationcited by 2
  8. 2024Integrating ChatGPT into Secure Hospital Networks: A Case Study on Improving Radiology Report Analysiscited by 1
  9. 2025Synergy over Discrepancy: A Partition-Based Approach to Multi-Domain LLM Fine-Tuningcited by 1
  10. 2024Simplifying Multimodality: Unimodal Approach to Multimodal Challenges in Radiology with General-Domain Large Language Modelcited by 0
  11. 2024Query-Guided Self-Supervised Summarization of Nursing Notescited by 0
  12. 2026Performance of a domain-specific large language model in answering patient questions in psychiatrycited by 0