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Streamlining Ophthalmic Documentation With Anonymized, Fine-Tuned Language Models: Feasibility Study.

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Summary

Generative AI can automate medical report summaries, significantly reducing clinician workload and documentation time. While accuracy needs further refinement, this technology shows promise for improving healthcare efficiency and patient safety.

Keywords:
artificial intelligencedocumentation automationepicrisis generationlarge language modelsmedical reports

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Area of Science:

  • Artificial Intelligence in Medicine
  • Clinical Informatics
  • Natural Language Processing

Background:

  • Clinician administrative burden, especially in medical documentation, contributes to burnout and patient safety risks.
  • Generative artificial intelligence (AI) presents a potential solution for improving documentation and mitigating these challenges.

Purpose of the Study:

  • To evaluate the feasibility of using a fine-tuned OpenAI Curie model for automated medical report summary (epicrisis) generation in ophthalmology.
  • To assess AI model performance through human and automated evaluations for accuracy, usefulness, and regulatory compliance.
  • To determine the potential of AI in reducing clinician workload.

Main Methods:

  • A dataset of approximately 60,000 anonymized medical letters was created adhering to General Data Protection Regulation (GDPR) guidelines.
  • The OpenAI Curie model was fine-tuned on this dataset to generate epicrises from medical histories, diagnoses, and findings.
  • Performance was evaluated using human assessments and automated evaluations from two large language models (LLMs).

Main Results:

  • Nearly 50% of AI-generated epicrises were rated as helpful or excellent in a clinical context.
  • Human evaluation showed significantly high formal correctness (mean 3.59/4.0) and a significant reduction in correction time compared to manual writing (54.25s vs 109.52s).
  • AI-generated reports were significantly shorter, and automated LLM assessments showed consistency with human ratings, supporting the proof of concept.

Conclusions:

  • Fine-tuned commercial LLMs are technically and practically feasible for integration into clinical practice, demonstrating time-saving potential.
  • AI-generated epicrises showed formal and clinical correctness in many instances, indicating significant workload reduction potential.
  • The study successfully demonstrated an anonymization process for handling patient data with AI and outlined a pipeline for integrating LLMs into EU clinical practice, emphasizing safety and efficiency.