How AI is Being Adopted Across the Clinical Trials Lifecycle

AI is already being integrated across the clinical trials lifecycle at nearly every stage. It can sift through billions of data points to identify new drug targets and candidate molecules, helping pharma and biotech companies decide what to develop next. It can mine electronic health records across massive healthcare systems to identify eligible patients for trials—and agentic AI can then administer rating scales and questionnaires to evaluate eligibility. AI can also generate trial methodology, streamlining protocol design.

During trials, AI can monitor patient responses, for example by analyzing voice and video in real time to assess medication response, capturing signals that traditional assessments might miss. In non-CNS studies, AI processes medical images, including X-rays, MRIs, and other scans, to achieve faster, more consistent outcome evaluation. And AI-powered platforms can also manage the hundreds of thousands of documents a single trial generates, keeping records organized and audit-ready.

AI-powered Artificial Twins: A Promise for Rare Disease Patients

Whenever a new technology emerges, there is inevitable excitement. However, amidst that buzz, it’s important to remember that cutting-edge tools should ultimately serve a purpose—and in the case of clinical trials, the ultimate purpose is helping patients. AI-powered artificial twins are a good example of what that might look like.

Using AI, researchers can model how a patient’s illness is likely to progress without treatment. That predicted trajectory becomes the placebo arm, while the patient’s actual treatment response becomes the experimental data—in essence, the patient becomes their own control, supercharging both methodology and timelines. For patients with rare and ultra-rare diseases, this could be a genuine lifeline. Traditional placebo-controlled recruitment has long been one of the greatest barriers to advancing development programs in these areas. With the FDA reportedly considering single-trial efficacy data for drug approval in rare diseases, artificial twin methodology may prove to be exactly the tool that makes that viable, helping sponsors identify the right patients and dose before running one pivotal study.

Realizing this promise requires researchers who understand how to implement these models responsibly—ensuring AI in clinical trials is working toward better patient outcomes, not simply generating impressive data.

AI in Clinical Trials Only Works as Well as the People Behind It

AI holds great promise for drug and medical technology development. However, these tools must be properly implemented—integrated into protocols, explained to participants, supported by trained staff, and backed by rigorous data analysis. Excitement around technology must be tempered with careful questioning: How will users implement it? What processes will it actually improve? Of course, the most important question is whether the application of an AI tool genuinely improves patient outcomes. It is up to the people in research and development to ask and answer these questions.

Adopting technology without investing equally in the people behind it risks introducing new errors rather than eliminating old ones. The organizations that will lead the next revolution are those that build teams capable of deploying these tools thoughtfully and questioning them critically when needed.

Amidst all the new technology, the ultimate goal remains the same: to help patients with new and improved treatments and cures. We should not lose sight of this as AI in clinical trials becomes more prevalent—and we should measure every technology advancement by the same standard we apply to the drugs themselves: Does it ultimately help the patient?