What is it about?
Clinical trials often struggle to find and recruit suitable participants. This can delay research and make it harder for new treatments to reach patients. This article looks at how large language models, such as advanced AI chatbots, could support clinical trial recruitment. The paper explains that these tools should not be treated simply as standalone technologies. Instead, they need to be built into real clinical workflows, with clear roles for patients, clinicians, trial staff, and regulators. The article proposes a framework called LECRA to show how AI could help with patient matching, communication, human oversight, governance, and cost-related decisions.
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Why is it important?
AI is increasingly being discussed in health care, but many debates focus only on technical performance. In clinical trial recruitment, the bigger question is how AI can be used safely, fairly, and practically in real-world settings. This work is important because it connects AI design with human decision-making, trust, accountability, and economic feasibility. It highlights that successful use of large language models in recruitment depends not only on accurate matching, but also on transparent governance, responsible oversight, and workflow integration. The framework may help researchers, clinical teams, trial sponsors, and health technology developers think more clearly about how to deploy AI in ways that support both efficiency and patient protection.
Perspectives
I wrote this article because clinical trial recruitment is both a scientific and human challenge. Many promising studies are delayed or limited because the right patients are difficult to identify, reach, and support through the recruitment process. Large language models create new possibilities, but they also raise important questions about trust, explainability, responsibility, and fairness. My aim was to move the discussion beyond “Can AI do matching?” toward “How should AI be responsibly embedded in the recruitment system?” I hope this framework can support further discussion among researchers, clinicians, trial sponsors, and digital health practitioners who are working to make clinical research more accessible, efficient, and accountable.
Mr. Qian Qian
Hong Kong Polytechnic University
Read the Original
This page is a summary of: Large Language Models in Clinical Trial Recruitment: Sociotechnical and Economic Framework Development Study, JMIR AI, May 2026, JMIR Publications Inc.,
DOI: 10.2196/95899.
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