Leveraging Large Language Models for Accurate Retrieval of Patient Information From Medical Reports: Systematic Evaluation Study

The findings highlight notable differences in precision and recall between models, particularly in extracting names and age-related information. There were challenges in processing unstructured medical text, including variability in model performance across data types. Our findings demonstrate the feasibility of integrating LLMs into health care workflows; LLMs offer substantial improvements in data accessibility and support clinical decision-making processes. In addition, the paper describes the role of retrieval-augmented generation techniques in enhancing information retrieval accuracy, addressing issues such as hallucinations and outdated data in LLM outputs. Future work should explore the need for optimization through larger and more diverse training datasets, advanced prompting strategies, and the integration of domain-specific knowledge to improve model generalizability and precision.