Radiology Report Analysis Using Deep Learning and NLP

Abstract

Radiology reports contain important diagnostic information but are commonly written in unstructured textual form. This makes automated processing and analysis difficult. This paper presents a framework for Radiology Report Analysis Using Deep Learning and Natural Language Processing (NLP) to extract relevant medical entities and generate concise summaries from radiology reports. The proposed system applies biomedical Named Entity Recognition for identifying anatomical structures and pathological conditions, followed by ontology-based mapping for term standardization. A transformer-based summarization model is used to generate brief clinical summaries. To address privacy concerns, a de-identification module is included to remove sensitive patient information. In addition, a basic consistency check is performed between extracted findings and generated impressions. Experimental observations indicate that the framework can assist in organizing and summarizing radiology report content, supporting clinical review processes.

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