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Transforming Informed Consent Generation Using Large Language Models: Mixed Methods Study.

Qiming Shi¹, Katherine Luzuriaga¹, Jeroan J Allison²

¹Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, 55 N Lake Ave, Worcester, MA, 01655, United States, 1 508-856-1952.

JMIR Medical Informatics

|February 13, 2025

Summary

This summary is machine-generated.

Large language models (LLMs) can generate more readable and understandable informed consent forms (ICFs) for clinical trials. This study shows LLM-generated ICFs improved clarity and actionability without compromising accuracy.

Keywords:

AI AI in health care ICF LLMs artificial intelligence clinical trials health informatics informed consent form large language models readability

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Published on: June 13, 2025

Area of Science:

Clinical Trial Management
Artificial Intelligence in Healthcare
Health Informatics

Background:

Informed consent forms (ICFs) in clinical trials are often complex, hindering participant understanding and engagement.
Advances in large language models (LLMs) offer potential solutions for simplifying ICF creation and improving participant comprehension.

Purpose of the Study:

To evaluate the performance of the Mistral 8x22B LLM in generating ICFs with enhanced readability, understandability, and actionability.
To assess if LLM-generated ICFs maintain accuracy and completeness compared to human-generated versions.

Main Methods:

Four clinical trial protocols were processed using the Mistral 8x22B LLM to generate key ICF sections.
A multidisciplinary team of eight evaluators assessed LLM-generated ICFs against human-generated ICFs.
Evaluation criteria included completeness, accuracy, readability, understandability, and actionability using the Readability, Understandability, and Actionability of Key Information indicators.

Main Results:

LLM-generated ICFs showed comparable accuracy and completeness to human-generated ICFs (P>.10).
The LLM significantly improved readability (76.39% vs 66.67%) and understandability (90.63% vs 67.19%; P=.02).
LLM-generated ICFs achieved perfect actionability scores (100%) compared to human-generated versions (0%; P<.001), with high evaluator consistency (ICC=0.83).

Conclusions:

The Mistral 8x22B LLM effectively enhances ICF readability, understandability, and actionability without compromising accuracy.
LLMs provide a scalable and efficient method for ICF generation, potentially improving participant comprehension and consent in clinical trials.