# Radiology-Llama2: Best-in-Class Large Language Model for Radiology

arXiv: https://arxiv.org/abs/2309.06419 · HTML: https://mlsanity.com/paper/2309.06419/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 66 references that are in the map.
- First posted on arXiv 29 Aug 2023.

## Builds on (most cited first, 30 of 39)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [PaLM 2 Technical Report](https://mlsanity.com/paper/2305.10403/) (2023)
- [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://mlsanity.com/paper/2103.10360/) (2021)
- [A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT](https://mlsanity.com/paper/2302.09419/) (2023)
- [HuaTuo: Tuning LLaMA Model with Chinese Medical Knowledge](https://mlsanity.com/paper/2304.06975/) (2023)
- [DoctorGLM: Fine-tuning your Chinese Doctor is not a Herculean Task](https://mlsanity.com/paper/2304.01097/) (2023)
- [DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4](https://mlsanity.com/paper/2303.11032/) (2023)
- [Review of Large Vision Models and Visual Prompt Engineering](https://mlsanity.com/paper/2307.00855/) (2023)
- [Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence](https://mlsanity.com/paper/2209.02970/) (2022)
- [An Iterative Optimizing Framework for Radiology Report Summarization with ChatGPT](https://mlsanity.com/paper/2304.08448/) (2023)
- [When Brain-inspired AI Meets AGI](https://mlsanity.com/paper/2303.15935/) (2023)
- [Evaluating Large Language Models on a Highly-specialized Topic, Radiation Oncology Physics](https://mlsanity.com/paper/2304.01938/) (2023)
- [Radiology-GPT: A Large Language Model for Radiology](https://mlsanity.com/paper/2306.08666/) (2023)
- [BayLing: Bridging Cross-lingual Alignment and Instruction Following through Interactive Translation for Large Language Models](https://mlsanity.com/paper/2306.10968/) (2023)
- [SAMAug: Point Prompt Augmentation for Segment Anything Model](https://mlsanity.com/paper/2307.01187/) (2023)
- [AD-AutoGPT: An Autonomous GPT for Alzheimer's Disease Infodemiology](https://mlsanity.com/paper/2306.10095/) (2023)
- [Context Matters: A Strategy to Pre-train Language Model for Science Education](https://mlsanity.com/paper/2301.12031/) (2023)
- [Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task](https://mlsanity.com/paper/2304.09138/) (2023)
- [Artificial General Intelligence for Medical Imaging Analysis](https://mlsanity.com/paper/2306.05480/) (2023)
- [ChatABL: Abductive Learning via Natural Language Interaction with ChatGPT](https://mlsanity.com/paper/2304.11107/) (2023)
- [Differentiate ChatGPT-generated and Human-written Medical Texts](https://mlsanity.com/paper/2304.11567/) (2023)

## Built on by (most cited first, 30 of 42)

- [Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization](https://mlsanity.com/paper/2309.07430/) (2023)
- [Large language models in healthcare and medical domain: A review](https://mlsanity.com/paper/2401.06775/) (2023)
- [Revolutionizing Finance with LLMs: An Overview of Applications and Insights](https://mlsanity.com/paper/2401.11641/) (2024)
- [Large Language Models for Robotics: Opportunities, Challenges, and Perspectives](https://mlsanity.com/paper/2401.04334/) (2024)
- [From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery](https://mlsanity.com/paper/2508.14111/) (2025)
- [Qilin-Med-VL: Towards Chinese Large Vision-Language Model for General Healthcare](https://mlsanity.com/paper/2310.17956/) (2023)
- [Understanding LLMs: A Comprehensive Overview from Training to Inference](https://mlsanity.com/paper/2401.02038/) (2024)
- [A Comprehensive Survey of Large Language Models and Multimodal Large Language Models in Medicine](https://mlsanity.com/paper/2405.08603/) (2024)
- [A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers](https://mlsanity.com/paper/2508.21148/) (2025)
- [Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Research](https://mlsanity.com/paper/2412.04497/) (2024)
- [Large Language Models for Manufacturing](https://mlsanity.com/paper/2410.21418/) (2024)
- [Large language models in medicine: the potentials and pitfalls](https://mlsanity.com/paper/2309.00087/) (2023)
- [Holistic Evaluation of GPT-4V for Biomedical Imaging](https://mlsanity.com/paper/2312.05256/) (2023)
- [Benchmarking Pathology Foundation Models: Adaptation Strategies and Scenarios](https://mlsanity.com/paper/2410.16038/) (2024)
- [Trustworthy and Practical AI for Healthcare: A Guided Deferral System with Large Language Models](https://mlsanity.com/paper/2406.07212/) (2024)
- [Multimodal Large Language Models for Medicine: A Comprehensive Survey](https://mlsanity.com/paper/2504.21051/) (2025)
- [ChatGPT's One-year Anniversary: Are Open-Source Large Language Models Catching up?](https://mlsanity.com/paper/2311.16989/) (2023)
- [ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data](https://mlsanity.com/paper/2310.05242/) (2023)
- [Transcending Language Boundaries: Harnessing LLMs for Low-Resource Language Translation](https://mlsanity.com/paper/2411.11295/) (2024)
- [GP-GPT: Large Language Model for Gene-Phenotype Mapping](https://mlsanity.com/paper/2409.09825/) (2024)
- [The Mosaic Memory of Large Language Models](https://mlsanity.com/paper/2405.15523/) (2024)
- [Dynamic Stochastic Decoding Strategy for Open-Domain Dialogue Generation](https://mlsanity.com/paper/2406.07850/) (2024)
- [Towards Next-Generation Medical Agent: How o1 is Reshaping Decision-Making in Medical Scenarios](https://mlsanity.com/paper/2411.14461/) (2024)
- [How Well Can Modern LLMs Act as Agent Cores in Radiology Environments?](https://mlsanity.com/paper/2412.09529/) (2024)
- [Evaluating Mathematical Reasoning Across Large Language Models: A Fine-Grained Approach](https://mlsanity.com/paper/2503.10573/) (2025)
- [A Multimodal Multi-Agent Framework for Radiology Report Generation](https://mlsanity.com/paper/2505.09787/) (2025)
- [m3P: Towards Multimodal Multilingual Translation with Multimodal Prompt](https://mlsanity.com/paper/2403.17556/) (2024)
- [RAD-PHI2: Instruction Tuning PHI-2 for Radiology](https://mlsanity.com/paper/2403.09725/) (2024)
- [RadPhi-3: Small Language Models for Radiology](https://mlsanity.com/paper/2411.13604/) (2024)
- [Large Language Models for Bioinformatics](https://mlsanity.com/paper/2501.06271/) (2025)

## Abstract (arXiv)

This paper introduces Radiology-Llama2, a large language model specialized for radiology through a process known as instruction tuning. Radiology-Llama2 is based on the Llama2 architecture and further trained on a large dataset of radiology reports to generate coherent and clinically useful impressions from radiological findings. Quantitative evaluations using ROUGE metrics on the MIMIC-CXR and OpenI datasets demonstrate that Radiology-Llama2 achieves state-of-the-art performance compared to other generative language models, with a Rouge-1 score of 0.4834 on MIMIC-CXR and 0.4185 on OpenI. Additional assessments by radiology experts highlight the model's strengths in understandability, coherence, relevance, conciseness, and clinical utility. The work illustrates the potential of localized language models designed and tuned for specialized domains like radiology. When properly evaluated and deployed, such models can transform fields like radiology by automating rote tasks and enhancing human expertise.