Lexomics, or why to extract relevant information from radiology reports through LLMs.

Abstract

Purpose: The application of large language models (LLMs) to radiology reports aims to enhance the extraction of meaningful textual data, improving clinical decision-making and patient management. Similar to radiomics in image analysis, lexomics seeks to reveal hidden patterns in radiology reports to support diagnosis, classification, and structured reporting.

Methods: LLMs and natural language processing (NLP) algorithms analyze radiology reports to extract relevant information, refine differential diagnoses, and integrate clinical data. These models process structured and unstructured text, identifying patterns and correlations that may otherwise go unnoticed. Applications include automated structured reporting, quality control, and enhanced communication of incidental and urgent findings.

Results: LLMs have demonstrated the ability to assist radiologists in real-time, standardizing classifications, improving report clarity, and enhancing the integration of radiology reports into electronic health records (EHRs). They support radiologists by reducing redundancies, structuring free-text reports, and detecting clinically relevant insights. Unlike radiomics, lexomics requires minimal computational power, making it more accessible in clinical settings.

Conclusion: Lexomics represents a significant advancement in AI-driven radiology, optimizing report utilization and communication. Future research should focus on addressing challenges such as data privacy, bias mitigation, and validation in diverse clinical scenarios to ensure ethical and effective implementation in radiological practice.