This article focuses on AI’s influence on the research and clinical trial process. While generative AI tools that assist with drafting and editing content are impacting medical communication work more directly, this piece highlights the innovations reshaping research, trial design, and outcomes.
One way that AI is streamlining medical research is through new tools like OpenEvidence, which can summarize medical literature within seconds. Although AI has had a role in medicine for many years, today’s large language models like GPT-5 have the capacity to make a significant impact on the field. For example, they can offer instant assistance with second opinions and suggest next steps in patient care.
In biomedical research at Harvard University, AI use includes
Future possibilities include using AI as an assistant that can tap into an entire body of scientific literature, contribute to results, and propose next steps.
AI is playing an increasingly large role in aspects of drug development, including the use of AI in clinical research, drug discovery, and clinical trial design. The US Food and Drug Administration (FDA) has noted an increase in submissions that reference AI, citing some 300 from 2016 through 2024.
By analyzing large data sets, AI is helping to modernize and accelerate clinical trials, offering insights into the safety and effectiveness of drugs under evaluation. In addition, AI can be used to monitor clinical trial participants’ medication adherence and attendance at clinical visits, improve participants' access to trial information, and support participant retention.
The FDA sees potential for AI to inform other aspects of clinical trials through
One of the largest hurdles in clinical trials is recruiting participants. The AI algorithm TrialGPT developed by the National Institutes of Health (NIH) has demonstrated how AI can match patients with relevant clinical trials by analyzing trial eligibility criteria and producing information describing how people match. The NIH found that clinicians using TrialGPT spent 40 percent less time screening participants, maintaining the same accuracy as traditional methods.
This approach not only expedites recruitment but also increases trial accessibility—especially for populations underrepresented in clinical research. The NIH’s pilot study shows that AI can reduce barriers to participation, addressing disparities rooted in the traditional recruitment process.
Despite its promise, AI in medical research comes with caveats. Some of the potential issues that experts have identified are
As AI tools continue to evolve, the medical research community must stay engaged, building both technical knowledge and ethical frameworks to ensure these technologies truly advance clinical research.
Not only is AI transforming medicine and medical research, but it is reshaping the work of medical writers. From drafting protocols and trial summaries to developing recruitment materials, medical writers play a critical role in translating AI-driven innovations into clear, actionable content.
Whether you’re documenting how AI predicted an adverse event or explaining an algorithm’s logic for an institutional review board (IRB), your ability to distill complex topics into accessible language is crucial. As the role of AI in medical research and clinical trials expands, staying informed will ensure your medical writing career keeps pace with medical and scientific advancements.
AMWA acknowledges Wayne Beazley for providing peer review in the development of this AMWA resource.