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Radiology-GPT: A Large Language Model for Radiology

Published 14 Jun 2023 in cs.CL and cs.AI | (2306.08666v2)

Abstract: We introduce Radiology-GPT, a LLM for radiology. Using an instruction tuning approach on an extensive dataset of radiology domain knowledge, Radiology-GPT demonstrates superior performance compared to general LLMs such as StableLM, Dolly and LLaMA. It exhibits significant versatility in radiological diagnosis, research, and communication. This work serves as a catalyst for future developments in clinical NLP. The successful implementation of Radiology-GPT is indicative of the potential of localizing generative LLMs, specifically tailored for distinctive medical specialties, while ensuring adherence to privacy standards such as HIPAA. The prospect of developing individualized, large-scale LLMs that cater to specific needs of various hospitals presents a promising direction. The fusion of conversational competence and domain-specific knowledge in these models is set to foster future development in healthcare AI. A demo of Radiology-GPT is available at https://huggingface.co/spaces/allen-eric/radiology-gpt.

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Citations (54)
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Summary

  • The paper introduces Radiology-GPT, a model fine-tuned with instruction tuning on MIMIC-CXR data to enhance radiological report generation.
  • The methodology involves systematic preprocessing of reports to extract key sections like 'Findings' and 'Impression' for diagnostic reasoning.
  • The model outperforms general LLMs in coherence, clinical utility, and relevance, while supporting HIPAA-compliant local deployment.

Radiology-GPT: A Domain-Specific LLM for Enhanced Radiological Practice

This paper introduces Radiology-GPT, an innovative application of LLMs within the medical domain of radiology. Leveraging the MIMIC-CXR dataset, the authors utilize an instruction tuning approach to specifically tailor the model for radiology. This development underscores the ongoing expansion of NLP capabilities within highly specialized medical fields, presenting Radiology-GPT as a model that surpasses the performance of more general models like StableLM, Dolly, and LLaMA.

Methodology and Development

The core of Radiology-GPT is anchored on instruction tuning, specifically modeled on the Alpaca framework, which was initially derived from Meta's LLaMA 7B model. Training is executed on rich radiological data, predominantly the MIMIC-CXR dataset, which comprises extensive textual data derived from chest X-ray reports. The systematic preprocessing of this data ensures the extraction of relevant sections such as "Findings" and "Impression," which are pivotal in developing understanding and interpretation capabilities within the model.

The model's local implementation is a strategic decision, responding to HIPAA regulations and the paramount need for patient data privacy, often challenged by large commercial LLMs that require data uploads to external platforms. This localization not only aligns with privacy protocols but also exemplifies an approach that can be generalized to other medical specialties, potentially enabling hospitals to deploy their proprietary LLMs.

Evaluation and Findings

Radiology-GPT's performance is evaluated across five critical metrics: understandability, coherence, relevance, conciseness, and clinical utility. The model demonstrates notable capabilities in generating concise and clinically applicable impressions, indicative of its proficiency in handling complex radiological language and tasks. It exhibits superior performance relative to several instruction-tuned models not specifically tailored for radiology, thereby validating the efficacy of domain-specific tuning.

Moreover, Radiology-GPT addresses a significant gap in clinical practice. By generating impressions from findings, it mirrors the diagnostic processes of radiologists, providing intelligent assistance. However, its impartiality and effectiveness radically depend on ongoing engagement with the medical community to ensure continuous alignment with clinical needs and practices.

Implications and Future Directions

The implications of this research are manifold, impacting both the practicalities of everyday clinical work and theoretical advancements in medical AI. Practically, Radiology-GPT offers a sophisticated tool for aiding radiologists in their diagnostic processes, potentially enhancing both the accuracy and efficiency of radiological assessments. The fusion of its conversational and domain-specific capabilities could facilitate improved patient communication and streamlined decision support in clinical settings.

Theoretically, this work contributes to the ongoing discourse on the development of DSLMs, emphasizing the critical importance of domain-specific training data and the resultant enhancements in model performance. Furthermore, it points toward broader future directions, including the integration of multimodal data to extend Radiology-GPT's capabilities beyond text to image interpretation, aligning more closely with the comprehensive evaluation performed by radiologists.

Overall, Radiology-GPT exemplifies a significant stride toward specialized, privacy-preserving AI tools in healthcare, heralding a future where AI can substantially contribute to individualized patient care while adhering to ethical and privacy standards.

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