Can Artificial Intelligence Revolutionize Recruitment for Clinical Trials?

Selecting the right doctors and locations to conduct clinical trials remains a major challenge. Traditional methods, often slow and limited, lead to costly delays and a lack of diversity among participants. Nearly four out of ten sites struggle to recruit enough volunteers, and one in ten finds none at all. These difficulties delay access to new treatments and increase budgets, with losses reaching several million per day.

A new approach uses artificial intelligence to optimize this process. By analyzing patient data, trial criteria, and participation histories, a computer model suggests the most suitable doctors. This system does more than just propose precise matches: it also improves participant diversity and reduces conflicts between simultaneous trials.

Tested on over 24,000 doctors and 5,000 trials in the United States, this model has demonstrated 58% greater effectiveness compared to existing methods. It considers various information, such as patients’ medical histories or trial descriptions, to identify the most relevant professionals. A complementary algorithm refines these suggestions by promoting better representation of different backgrounds and avoiding overburdening the same doctors.

The tool also helps estimate recruitment costs, enabling organizers to better plan their budgets. By making site selection faster, fairer, and more cost-effective, this technology could accelerate the development of new therapies.

Unlike current systems that assess patient eligibility one by one, this solution focuses on doctors and their ability to recruit suitable volunteers. It combines structured data, such as diagnoses, and free text, such as trial protocols, to provide a comprehensive view. Thanks to this approach, trials could become more accessible, more representative, and less expensive, while limiting the risks of geographic or demographic imbalances.

The use of artificial intelligence in this field paves the way for more inclusive and responsive medicine. By identifying doctors sometimes overlooked by traditional methods, it broadens the range of possibilities and reduces selection biases. An advancement that could transform medical research and facilitate access to therapeutic innovations for all.

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DOI: https://doi.org/10.1038/s44360-026-00073-6

Title: Matching clinicians with clinical trials using AI

Journal: Nature Health

Publisher: Springer Science and Business Media LLC

Authors: Junyi Gao; Cao Xiao; Lucas M. Glass; Ewen M. Harrison; Jimeng Sun