ArticleHumanities & social sciences communications2025
How effectively can ChatGPT-4 draft data transfer agreements for health research?
Article in Humanities & social sciences communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- AI‑driven framework for contract risk automation and compliance in oracle CPQ.Scientific reports · 2026Article
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Authors and funding
1 author.
Funding
Abstract
The rapid advancement of generative artificial intelligence (AI), such as ChatGPT-4, is reshaping legal drafting, offering significant potential for streamlining the creation of complex legal documents. However, there is limited scholarly research on how effectively AI can draft specialised contracts, such as data transfer agreements (DTAs) for health research. Unlike common consumer contracts, DTAs are highly specialised and less prevalent in the public data used to train generative AI models, making them a more challenging test of AI's drafting capabilities. This article fills this gap by critically assessing ChatGPT-4's ability to draft DTAs for health research. The study uses a two-stage methodology: first, an iterative process to develop a comprehensive outline, and second, a detailed refinement of each clause. This methodology produced a comprehensive DTA of 6847 words. While this DTA includes all the standard headings, the quality of the clause content varies in terms of clarity and legal precision. Additionally, its alignment with data protection best practices requires further refinement. The findings suggest that although generative AI is a valuable tool for legal drafting, it cannot yet replace the essential role of human legal expertise.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.