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An Empirical Study of LLMs for Engineering Medical Consent.

Anastasia Terzi1, Panos Bonotis1, Christina Zoi1

  • 1Department of Electrical and Computer Engineering, University of Western Macedonia.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary

Large Language Models (LLMs) can assist clinicians in generating patient consent forms. However, a tailored approach is necessary, as a one-size-fits-all strategy is insufficient for complex healthcare needs.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Workflow Optimization

Background:

  • Healthcare providers face significant challenges in managing patient informed consent for health information and treatment.
  • Evolving regulatory landscapes and complex consent requirements strain existing clinical workflows.
  • Large Language Models (LLMs) present a potential solution for automating and improving the medical consent process.

Purpose of the Study:

  • To evaluate the capabilities of popular LLMs (LLaMa, Gemma, DeepSeek, Phi-3) in generating compliant and patient-centric medical consent forms.
  • To assess LLMs' effectiveness as collaborative tools for clinicians in consent engineering.
  • To identify key clinical challenges in consent management and how LLMs can address them.

Main Methods:

Keywords:
Consent EngineeringData TransparencyLarge Language Models (LLMs)Natural Language GeneratorPrivacy Enhancing Technologies

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  • LLMs were tasked with formalization, comprehensibility, and compliance checks for medical consent forms based on natural language inputs.
  • Evaluation involved qualitative expert feedback on LLM performance in addressing identified clinical challenges.
  • Comparative analysis of LLaMa, Gemma, DeepSeek, and Phi-3 capabilities in consent generation.

Main Results:

  • LLMs demonstrated effectiveness as supportive tools for clinicians in generating consent forms.
  • Expert feedback indicated that LLMs are valuable companions but a "one-size-fits-all" approach is inadequate.
  • Performance varied across LLMs, highlighting the need for context-specific selection.

Conclusions:

  • LLMs show promise in automating aspects of medical consent engineering, improving quality and compliance.
  • The selection of an appropriate LLM depends on the specific context and requirements of the consent process.
  • Future work should focus on developing tailored LLM applications for diverse healthcare consent needs.