Informed Consent Forms (ICFs) are critical documents in clinical trials. They are the first, and often most crucial, touchpoint between a patient and a clinical trial study. Yet the process of developing them is laborious, high-stakes, and heavily regulated. Each form must be tailored to jurisdictional requirements and local ethics boards, reviewed by cross-functional teams, and written in plain language that patients can understand. Producing them at scale across countries and disease areas demands manual effort and creates major operational bottlenecks. We used a combination of traditional AI and large language models to autodraft the ICF across clinical trial types, across countries and across disease areas at scale. The build, test, iteration and deployment offers both technical and non technical lessons learned for generative AI applications for complex documents at scale and for meaningful impact.
Informed Consent Forms are highly complex documents that require high precision and quality. A phase 2 / 3 clinical trial can have almost 1000 different forms that takes considerable time to complete.We identified this challenge that directly impacts trial timelines and patient engagement. The automated AI solution: the “ICF Autodrafter”, a custom LLM-powered application that automates the drafting of ICFs. This tool ingests a clinical trial protocol and ICF template and outputs a complete draft in minutes, cutting document preparation time by 90%.
This solution is not generic automation. The backend logic parses highly structured protocol documents, segments them, and feeds the relevant content into a carefully fine-tuned LLM that maps text to specific ICF fields. The front-end is designed for usability by clinical trial managers, with human-in-the-loop reviews. This system has already supported ICF creation for more than ten trials and has achieved near-perfect consistency (97%) with human-generated content, underscoring the speed, quality, and robustness of the solution.
We rigorously test version with A/B comparisons, iterated with feedback from end-users, and anchored all development within regulatory and ethical guardrails. The impact extends beyond efficiency. By standardizing and accelerating ICF production, we can reduce delays in trial start-up and potentially get medicines to patients faster, without compromising safety, compliance, or clarity. Furthermore, it also lays down a scalable model for future AI-driven document workflows across other parts of life sciences and healthcare.
Lily Xu
Aman Bhandari
Outline
Reflections and Lessons Learned
Some interesting insights about handling critical documents in regulated environments using AI. It can speed up the process significantly while maintaining high quality.
Perhaps the main point is that LLM are used to reduce work load but a human is kept in the loop to ensure quality and compliance. The real question is if all AI errors and biases can be mitigated by this human.
Citation
@online{bochman2025,
author = {Bochman, Oren},
title = {Using {Traditional} {AI} and {LLMs} to {Automate} {Complex}
and {Critical} {Documents} in {Healthcare}},
date = {2025-12-09},
url = {https://orenbochman.github.io/posts/2025/2025-12-09-pydata-automate-complex-and-critical-docs-in-healthcare/},
langid = {en}
}