Use of Large Language Models for Cataloging Medical Reports in Reconfigurable Digital Collections

This work proposes an approach based on Large Language Models (LLMs) for creating digital collections from free-text medical reports. The approach uses instruct LLMs to extract relevant clinical terms from these reports, as well as to catalog them using the extracted terms. The cataloged reports are then integrated into a digital collection management platform, enabling further curation by clinical experts. To confirm the feasibility of the approach, we used various models associated with DeepSeek, as well as a coding model developed by Alibaba, and the experimental Clavy reconfigurable collection management platform to handle the resulting collections. The preliminary evaluation results demonstrate the feasibility of the approach, even without relying on external services that could compromise data privacy in real-world scenarios, or on particularly expensive dedicated hardware.