Artificial intelligence (AI) and machine learning (ML) have become an integral part of clinical trial operations. While these technologies are sometimes lumped together, their roles and functions are distinct yet complementary.
This overview explores where we’re seeing the most impact from AI and ML on clinical research, how they work together, and what the road ahead looks like. It closes with an important reminder: even as automation advances, clinical trials remain fundamentally human experiences.
One of the clearest areas of value has been in protocol design optimization. AI can review thousands of past studies to suggest protocol elements that are more likely to lead to successful trial outcomes. This has helped sponsors:
AI plays a growing role in patient recruitment and matching, too. Rather than relying solely on manual chart review or advertising for clinical trial recruitment, study teams are using AI to scan electronic health records (EHRs) across institutions, flagging qualified patients who might otherwise be missed—especially in therapeutic areas like rare disease.
At the same time, AI is helping refine eSource integration by enabling connected devices to feed patient data directly into electronic case report forms (eCRFs). This minimizes manual transcription, improves data quality, and reduces the potential for human error. When systems are fully interoperable, sites eliminate hours of data reconciliation and ensure that the data generated during patient visits is immediately usable for monitoring and analysis.
Machine learning (ML) refers to systems that improve performance by learning from data. Where AI may be about structure and automation, ML focuses on prediction and refinement.
In clinical trials, ML delivers value in several impactful areas:
We’ve seen firsthand how this translates into meaningful intervention. For instance, when ML flags a subset of participants likely to disengage mid-study, sponsors can adjust outreach and transportation services—improving retention and preserving valuable data.
While AI and machine learning play distinct roles in clinical research, their combined application enables more agile, data-powered trial management. AI provides the structure while ML builds on that structure by identifying patterns, risks, and opportunities for refinement.
Here's a hypothetical example that brings those capabilities into clearer focus:
In a vaccine trial for older adults, AI is used upfront to analyze historical trial data and flag exclusion criteria, such as overly strict lab thresholds, that have previously led to high screen failure rates in this patient population. The protocol is adjusted to be more inclusive, helping accelerate enrollment.
As the study progresses, ML models analyze participant data in real time. They surface a pattern: individuals over 75 with cardiovascular conditions are more likely to experience fatigue and fever after the second dose—mild effects, but enough to deter follow-up in some cases. From these insights, the sponsor adjusts the subgroup's dosing interval and equips site staff with better anticipatory guidance.
Together, AI and ML help reduce recruitment barriers, improve tolerability, and support participant retention without sacrificing scientific rigor.
The next wave of AI and ML innovation in clinical trials will focus on personalization and real-world application. Key developments already taking shape include:
As these AI models and machine learning technologies mature, sponsors who adopt them early may see faster site activation, better enrollment performance, and more responsive clinical trial designs, all while collecting cleaner, more actionable data.
Despite all the technological progress, clinical research is still rooted in relationships: with clinical trial participants, investigators, and study teams.
Technology may streamline, but it can’t replace trust. It may predict behavior, but it can’t coach a patient through a tough side effect or make them feel seen when they’re worried about what comes next.
That’s why, at Remington-Davis, we balance innovation with empathy. We’ve built systems that help us operate more efficiently, while showing up, listening, and supporting participants every step of the way.
We’ve spent more than three decades at the intersection of innovation and patient care. Let’s talk about how we can bring that experience to your next study.
AI and ML play a major role in making decentralized clinical trials more scalable and effective. AI can automate tasks like scheduling, remote visit coordination, and real-time data collection from wearables or mobile apps. Machine learning helps interpret incoming patient data, flagging patterns that could indicate disengagement, patient safety concerns, or noncompliance.
Yes. AI enhances clinical trial data management by automatically structuring and cleaning incoming datasets from multiple sources, such as labs, ePRO tools, and connected medical devices. This reduces the manual burden on site staff and improves data integrity. When paired with ML, these systems can also surface trends or outliers in real time, helping sponsors act sooner on insights.
By analyzing variables like lab results, symptom reports, and treatment adherence, ML models can predict how different patient subgroups are likely to respond to a therapy. These insights allow sponsors to tailor interventions, adjust dosing strategies, or modify protocols in real time, improving the likelihood of positive patient outcomes while minimizing risks.
Like any tool, AI and ML must be used thoughtfully. Poorly trained models or biased input data can lead to inaccurate predictions or missed safety signals. That’s why it’s critical to pair advanced technologies with robust validation processes, human oversight, and a patient-centered trial design. When implemented correctly, these tools enhance the safeguards already built into ethical clinical research.