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MGH Radiology Llama: A Llama 3 70B Model for Radiology

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arxiv 2408.11848 v2 pith:6FK2EJLA submitted 2024-08-13 cs.CL cs.AI

classification cs.CLcs.AI
keywords radiologyllamamodellanguagelargellmsmodelspatient
verification ladder T0 review T1 audit T2 compute T3 formal

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In recent years, the field of radiology has increasingly harnessed the power of artificial intelligence (AI) to enhance diagnostic accuracy, streamline workflows, and improve patient care. Large language models (LLMs) have emerged as particularly promising tools, offering significant potential in assisting radiologists with report generation, clinical decision support, and patient communication. This paper presents an advanced radiology-focused large language model: MGH Radiology Llama. It is developed using the Llama 3 70B model, building upon previous domain-specific models like Radiology-GPT and Radiology-Llama2. Leveraging a unique and comprehensive dataset from Massachusetts General Hospital, comprising over 6.5 million de-identified medical reports across various imaging modalities, the model demonstrates significant improvements in generating accurate and clinically relevant radiology impressions given the corresponding findings. Our evaluation, incorporating both traditional metrics and a GPT-4-based assessment, highlights the enhanced performance of this work over general-purpose LLMs.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Leveraging Geolocation in Clinical Records to Improve Alzheimer's Disease Diagnosis Using DMV Framework

    cs.LG 2025-02 reject novelty 3.0 of 10

    Adding geolocation to LLM embeddings is claimed to improve Alzheimer's risk prediction, but the target is a CDC aggregate value and the near-zero error suggests label memorization.

  2. Legal Evalutions and Challenges of Large Language Models

    cs.CL 2024-11 reject novelty 3.0 of 10

    In a small human-scored evaluation of 10 LLMs on 26 legal cases, o1-preview received the highest overall human score (3.96/5), while ROUGE and BLEU scores did not track human preference.

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