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Related Experiment Videos

Ai-generated personalized informed consent for elective spinal surgery: a comparative study using retrieval-augmented

Ali Kaplan1, Begüm Aslantaş Kaplan2, Ali Aydilek3

  • 1Department of Neurosurgery, Tokat Erbaa State Hospital, Tokat, Turkey.

Journal of Clinical Neuroscience : Official Journal of the Neurosurgical Society of Australasia
|May 7, 2026
PubMed
Summary

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Psychosurgery01:30

Psychosurgery

Psychosurgery, the surgical alteration or permanent removal of brain tissue to alleviate severe psychological conditions, stands as one of the most radical and controversial treatments in the history of mental health care. Its development and application have evolved significantly, marked by dramatic shifts in scientific understanding and ethical perspectives.
Historical Development of Psychosurgery
In the 1930s, Portuguese neurologist Antonio Egas Moniz introduced a surgical procedure designed...

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This summary is machine-generated.

Retrieval-augmented generation (RAG) created superior informed consent documents for spine surgery. These AI-generated forms offer better quality, personalization, and readability than standard documents, improving patient understanding and potentially reducing litigation risks.

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Surgical Consent

Background:

  • Informed consent (IC) documents for spine surgery often lack crucial details like procedure-specific risks and complication rates.
  • These deficiencies hinder patient comprehension and are a frequent cause of malpractice litigation in spine surgery.
  • Existing large language models (LLMs) show potential for generating readable consent forms but may produce inaccuracies.

Purpose of the Study:

  • To generate personalized informed consent documents for elective spinal surgery using a retrieval-augmented generation (RAG) system.
  • To compare the quality of RAG-generated consent documents against standard consent forms.

Main Methods:

  • Generated consent documents using NotebookLM, a RAG platform, with a knowledge base combining NASS guidelines and PubMed literature.
Keywords:
Artificial intelligenceHealth literacyInformed consentPatient safetyRetrieval-augmented generationSpine surgery

Related Experiment Videos

  • Incorporated patient archetypes and targeted a sixth-grade reading level for personalization and readability.
  • Evaluated document quality using the Spatz instrument, personalization rubric, content accuracy verification, and Flesch readability indices.

    • Main Results:

    • AI-generated documents significantly outperformed standard forms on the Spatz instrument (14.75 vs. 10.17, p < 0.001), with 75% achieving maximum scores.
    • High personalization (93.1%) and readability (95.8% meeting grade level threshold) were achieved.
    • Factual claims demonstrated high accuracy (98.1% citation accuracy, 99.7% reporting accuracy) with a low fabrication rate (0.76%).

    Conclusions:

    • RAG-based systems can produce high-quality, personalized, and accurate informed consent documents for spinal surgery.
    • These AI-generated documents are superior to standard forms in content quality and readability.
    • This approach offers a potential solution to improve informed consent processes and reduce medicolegal risks in surgery.