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Large Language Models are currently transforming many aspects of healthcare topics as they have demonstrated a remarkable capability to encode and extract a wide range of clinical knowledge. The role of these language models in medical education has been thoroughly reviewed. The current work focuses on supporting the tagging of clinical records and related narratives for instructional purposes. The main goal consists in building educational scenarios that can take advantage of these clinical narratives by using Clavy as a digital medical collection management tool and prompting engineering techniques to process them. Preliminary experiences demonstrate the potential of this approach.

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Building Healthcare Educational Scenarios by Tagging Clinical Narratives Using Large Language Models

  • 会议论文

  • 发表日期：

    2025年

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  • Félix Buendía-García,
  • Joaquín Gayoso-Cabada,
  • José-Luis Sierra-Rodríguez

摘要

Large Language Models are currently transforming many aspects of healthcare topics as they have demonstrated a remarkable capability to encode and extract a wide range of clinical knowledge. The role of these language models in medical education has been thoroughly reviewed. The current work focuses on supporting the tagging of clinical records and related narratives for instructional purposes. The main goal consists in building educational scenarios that can take advantage of these clinical narratives by using Clavy as a digital medical collection management tool and prompting engineering techniques to process them. Preliminary experiences demonstrate the potential of this approach.