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Area of Science:

  • Artificial Intelligence
  • Clinical Informatics
  • Natural Language Processing

Background:

  • Large language models (LLMs) automate clinical document summarization.
  • Current LLM outputs lack individual clinician writing styles, requiring extensive post-editing.
  • A significant stylistic gap exists between AI-generated and clinician-authored summaries.

Purpose of the Study:

  • To address the stylistic gap in LLM-generated clinical summaries.
  • To develop a style-informed generation framework for personalized clinical documentation.
  • To evaluate the effectiveness of style-informed generation in matching clinician writing styles.

Main Methods:

  • Utilized a multi-author corpus of de-identified clinical summaries.
  • Developed a framework extracting clinician-specific stylistic features via LLM feedback.
  • Employed a Train→Generate paradigm for personalized summary production.
  • Evaluated metrics including ROUGE, BERTScore, cosine similarity, Jaro-Winkler, and BLEU.
  • Conducted blinded A/B testing with clinicians comparing AI-generated and clinician-authored summaries.

Main Results:

  • Conventional metrics showed limited ability to differentiate writing styles.
  • LLM-guided feature extraction improved authorship classification accuracy to 73%.
  • Gemini 2.5 Pro pipeline drafts were preferred at rates comparable to or exceeding clinician-authored summaries.
  • GPT-4 drafts were preferred less often than original notes.
  • Hallucination risks were mitigated through prompt engineering and source-only data constraints.

Conclusions:

  • Style-informed generation significantly reduces the stylistic gap in clinical summaries.
  • The proposed framework produces clinically acceptable summaries that align with clinician voice.
  • Personalized AI-generated summaries show potential to improve efficiency and user satisfaction in clinical documentation.