Empowering PET Imaging Reporting with Retrieval-Augmented Large Language Models and Reading Reports Database: A Pilot Single Center Study

Abstract Introduction The potential of Large Language Models (LLMs) in enhancing a variety of natural language tasks in clinical fields includes medical imaging reporting. This pilot study examines the efficacy of a retrieval-augmented LLM system considering zero-shot learning capability of LLMs, integrated with a comprehensive PET reading reports database, in improving referring previous reports and decision-making. Methods We developed a custom LLM framework enhanced with retrieval capabilities, leveraging a database encompassing nine years of PET imaging reports from a single center. The system employs vector space embedding of the reports database to facilitate retrieval based on similarity metrics. Queries prompt the system to retrieve embedded vectors, generating context-based answers and identifying similar cases or differential diagnoses from the historical reports database. Results The system efficiently organized embedded vectors from PET reading reports, showing that imaging reports were accurately clustered within the embedded vector space according to the diagnosis or PET study type. Based on this system, a proof-of-concept chatbot was developed and showed the framework’s potential in referencing reports of previous similar cases and identifying exemplary cases for various purposes. Additionally, it demonstrated the capability to offer differential diagnoses, leveraging the vast database to enhance the completeness and precision of generated reports. Conclusions The integration of a retrieval-augmented LLM with a large database of PET imaging reports represents an advancement in medical reporting within nuclear medicine. By providing tailored, data-driven insights, the system not only improves the relevance of PET report generation but also supports enhanced decision-making and educational opportunities. This study underscores the potential of advanced AI tools in transforming medical imaging reporting practices.