Artificial intelligence (AI) is transforming how medical research is conducted and how clinical trials are designed, run, and analyzed. From accelerating data analysis to improving participant recruitment, AI is helping researchers and clinicians bring treatments to patients faster and more effectively.
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.
What You’ll Learn in This Blog:
- How AI is accelerating insights in medical research, from protein modeling to literature reviews.
- How AI optimizes clinical trial design, recruitment, and participant engagement.
- The ethical challenges of AI in medical research and what they mean for future practice.
- Perspectives and real-world examples from the FDA, NIH, and leading research institutions applying AI in groundbreaking ways.
- Where medical writers fit in this evolving landscape—translating AI-driven discoveries into clear, actionable content.
Who It’s For:
- Medical writers and communicators looking to learn more about AI’s role in medical research and clinical trials.
- Health communicators who want to stay ahead of emerging trends in AI’s impact on medicine and science.
Accelerating Insights: AI in Medical Research
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
- Models like ProCyon that are advancing our understanding of protein structures, accelerating insights that once took years to uncover.
- Quick generation of accurate in-silico, or computer-generated, predictions for molecules and protein interactions that scientists can leverage for further scientific work.
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 in Clinical Trials
Optimizing Drug Development and Clinical Trials
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
- Data collection from electronic health records or digital health technologies embedded with AI
- Predictive computer modeling to support clinical trial design by testing different versions of clinical trials and identifying optimal dosing regimens
- Advance identification of potential adverse events in clinical trial participants
Streamlining Recruitment and Improving Equity
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.
Understanding Ethical Issues with AI in Medical Research
Despite its promise, AI in medical research comes with caveats. Some of the potential issues that experts have identified are
- The appropriateness of data sets used by AI. Are data sets that train AI models representative and of sufficient quality and size so they don’t introduce bias and raise questions about the reliability of results?
- The need to clearly understand AI models. Since the methodologies behind AI models can be quite complex, do scientists understand how they are developed and how they reach conclusions?
- AI model degradation over time. Can AI models become less accurate as new data are introduced, causing a mismatch between incoming data and data they were trained on?
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.
Final Thoughts: What AI in Medical Research Means for Medical Writers
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.
