The impact of AI on clinical trial operations

Artificial Intelligence (AI) is now reaching into nearly every stage of that process, from finding the right patients to catching a data error before it becomes a finding in an audit. The question for clinical leaders is no longer whether AI belongs in trials. It is where AI is already proving itself, where it is still unproven, and what it actually changes about how a trial gets run.

The recruitment bottleneck, and how AI is changing it

Recruitment is where most trials lose the most time. A large share of studies fail to meet enrollment targets and timelines, and the cost of every additional delay day compounds across staff, sites, and lost market opportunity.

AI is being applied directly to this problem. Natural language processing and machine learning models can scan structured and unstructured data, including electronic health records, clinical notes, and registries, to flag potentially eligible patients in a fraction of the time manual screening takes.

A scoping review of AI applications in clinical trial recruitment, covering studies published between 2004 and 2023, found consistent gains in efficiency, cost, and recruitment accuracy across the 51 studies reviewed, with oncology as the most represented therapeutic area [1]. A separate review of AI across the clinical trial lifecycle found that AI-supported recruitment tools have been associated with enrollment improvements of up to 65% in some settings. The review also reported that AI implementation can contribute to faster trial execution and lower costs, with reported gains varying widely depending on factors such as therapeutic area, protocol complexity, and implementation approach [2].

The mechanism behind these gains is straightforward. AI models can process a volume of records no human team could review in the same timeframe, and they can do it continuously rather than in scheduled batches. For oncology trials specifically, this has meant identifying eligible patients across geographies and healthcare providers without requiring physical presence at a site, widening the pool of participants a trial can realistically reach [3].

From reactive to proactive: AI in monitoring and data quality

Recruitment gets the most attention, but monitoring is where AI is arguably changing daily operations the most.

Traditional monitoring relies on scheduled site visits and manual source data verification. Risk-based monitoring has already moved some oversight to a more centralized, data-driven approach, but it has often been limited by manual reviews and disconnected data sources.

AI removes much of that limitation. Machine learning models can establish expected data patterns across a trial and flag deviations as soon as they appear, sometimes within hours of data entry rather than during the next scheduled review [2]. These models pull from multiple systems at once, including EDC, CTMS, eTMF, and RTSM, so a signal like an unusually clean adverse event report from a high-enrolling site, or a cluster of protocol deviations tied to a specific data entry pattern, can surface before it becomes a bigger problem.

This is not a hypothetical shift. A landscape survey of risk-based monitoring practices across thousands of ongoing trials, conducted by the Association of Clinical Research Organizations, documented how centralized, analytics-driven monitoring is already replacing a meaningful share of on-site visits industry-wide [4]. More recent literature reviews of AI in clinical data review describe this as a closed loop between people, process, and technology, where clinically trained reviewers still determine priorities and interpret signals, but the detection work itself is increasingly automated [5].

The impact extends beyond operational efficiency because trial data quality is also a compliance requirement. Version mismatches, incomplete audit trails, and undetected errors are exactly what regulators look for during inspections, and catching them early is far less costly than catching them late.

Faster, more adaptive trial design

Beyond recruitment and monitoring, AI is starting to influence how trials are designed in the first place.

Predictive analytics models can now forecast trial outcomes and identify likely risks in study design before a protocol is finalized, with some models reaching accuracy levels around 85% in outcome forecasting [2]. This allows sponsors to catch design issues, such as an eligibility criterion that would exclude too much of the target population, before the trial is underway rather than after enrollment has stalled.

AI is also supporting more adaptive trial designs, where protocols can be adjusted based on accumulating real-world data rather than staying fixed for the full duration of the study. Digital biomarkers, drawn from wearables and remote monitoring tools, are enabling continuous safety monitoring with reported sensitivity as high as 90% for adverse event detection, a meaningful improvement over monitoring that only captures a patient's condition at scheduled visit intervals [2].

From Wemedoo's own research

Wemedoo's own research explored a key question for the future of AI in clinical trials: how can trial failure be better understood and prevented? In a study published in BMC Medical Research Methodology, Nikola Cihoric and colleagues reviewed the existing literature on predictors of trial failure and highlighted the need for more consistent approaches to defining and analyzing trial outcomes [6].

This work has important implications for the future use of AI in clinical research. Machine learning models depend on clear definitions and high-quality data to identify meaningful patterns. As the industry moves toward more standardized approaches, AI could play a greater role in identifying risk factors earlier, supporting study design decisions, and helping sponsors build more resilient trials from the outset [6].

Regulators are paying close attention

None of this is happening outside regulatory view. The FDA has seen a sharp rise in drug and biologic applications that include an AI component, and reports more than 500 such submissions since 2016 spanning nonclinical, clinical, postmarketing, and manufacturing phases [7].

In January 2025, the FDA issued draft guidance, Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products, outlining a risk-based framework tied to the specific context in which an AI tool is used, along with expectations around the data used to train it and the governance around its deployment [8]. The FDA and EMA have since aligned on a shared set of guiding principles for AI use in drug development, with both agencies signaling that further guidance will follow as use cases and scientific understanding evolve [9].

Regulators have indicated a willingness to support appropriately governed AI applications in drug development, provided expectations around validation, governance, transparency, and traceability are met. That puts real weight behind data infrastructure. An AI model is only as trustworthy as the data pipeline feeding it, which is why the systems capturing and storing trial data are becoming as important a decision as the AI tools built on top of them.

Where the open questions remain

AI's role in trials is expanding, but it is not without friction. Reviews of AI adoption across the trial lifecycle consistently flag the same set of barriers: data interoperability challenges between systems, uneven data quality feeding into models, algorithmic bias risk when training data does not represent the full patient population, and limited trust among some clinical stakeholders in AI-generated outputs [2][10].

These are not reasons to avoid AI. They are reasons to be deliberate about the foundation it sits on. A predictive model built on data scattered across disconnected EDC, CTMS, and eTMF systems will inherit every inconsistency in that data. A recruitment tool pulling from incomplete or non-standardized records will miss eligible patients as easily as it finds them.

The foundation AI needs to work

This is the part of the AI conversation that gets skipped the most often. AI does not fix fragmented trial infrastructure. It exposes it faster.

A model that flags a data anomaly is only useful if the underlying data is unified enough to trust the signal. A recruitment tool is only as strong as the records it can actually access. This is why the conversation about AI in clinical trials keeps circling back to something less exciting than the AI itself: a single, connected data architecture across EDC, eTMF, CTMS, RTSM, and patient-facing tools like ePRO and eConsent.

oomnia was built with this in mind. Because oomnia runs on a single codebase and a single database rather than a patchwork of integrated modules, the data feeding AI-supported features, from error detection to protocol deviation tracking, is designed to promote data consistency across connected workflows, eliminating the need for manual reconciliation activities often associated with fragmented system environments.

Conclusion

AI is not replacing clinical judgment in trials, and the evidence so far does not suggest it should. What it is doing is compressing the time between a signal appearing in the data and a person acting on it, whether that signal is an eligible patient, a site at risk of a protocol deviation, or a safety trend worth a closer look.

The trials that will benefit most are the ones whose data infrastructure can actually support that speed.

References

Lu, X., Yang, C., Liang, L., Hu, G., Zhong, Z., & Jiang, Z. (2024). Artificial intelligence for optimizing recruitment and retention in clinical trials: A scoping review. Journal of the American Medical Informatics Association, 31*(11), 2749–2759. https://doi.org/10.1093/jamia/ocae243. View on Oxford Academic*

Olawade, D. B., Fidelis, S. C., Marinze, S., Egbon, E., Osunmakinde, A., & Osborne, A. (2026). Artificial intelligence in clinical trials: A comprehensive review of opportunities, challenges, and future directions. International Journal of Medical Informatics, 206*, Article 106141. https://doi.org/10.1016/j.ijmedinf.2025.106141. View on ScienceDirect*

Nashwan, A. J., & Bani Hani, S. (2023). Transforming cancer clinical trials: The integral role of artificial intelligence in electronic health records for efficient patient recruitment. Contemporary Clinical Trials Communications*. https://doi.org/10.1016/j.conctc.2023.101223. View on PMC*

Barnes, B., Stansbury, N., Brown, D., Garson, L., Gerard, G., Piccoli, N., Jendrasek, D., May, N., Castillo, V., Adelfio, A., Ramirez, N., McSweeney, A., Berlien, R., & Butler, P. J. (2021). Risk-based monitoring in clinical trials: Past, present, and future. Therapeutic Innovation & Regulatory Science, 55*(4), 899–906. https://doi.org/10.1007/s43441-021-00295-8. View on PMC*

Abbidi, S. R. (2026, June 16). Implementation of AI for future clinical data review: A literature review. Association of Clinical Research Professionals. View on ACRP

Jovanovic, A., Gavric, S., Dennstädt, F., & Cihoric, N. (2026). Approaches in analyzing predictors of trial failure: A scoping review and meta-epidemiological study. BMC Medical Research Methodology, 26*, Article 35. https://doi.org/10.1186/s12874-026-02774-8. View on Springer Nature Link*

U.S. Food and Drug Administration. (n.d.). Artificial intelligence for drug development. Center for Drug Evaluation and Research. Retrieved August 17, 2026, from FDA.gov

U.S. Food and Drug Administration. (2025, January). Considerations for the use of artificial intelligence to support regulatory decision-making for drug and biological products (Draft guidance). View on FDA.gov

European Medicines Agency. (2026). EMA and FDA set common principles for AI in medicine development. View on EMA.europa.eu

Teodoro, D., Naderi, N., Yazdani, A., Zhang, B., & Bornet, A. (2025). A scoping review of artificial intelligence applications in clinical trial risk assessment. npj Digital Medicine, 8*, Article 486. https://doi.org/10.1038/s41746-025-01886-7. View on PMC*