Clinical trials have a median cost of $19 million, with the average cost exceeding $41 million per trial. Considering the majority of trials face delays—and these can cost anywhere from hundreds of thousands to millions of dollars per day—there’s no shortage of incentive to identify opportunities to appropriately accelerate drug development.
Artificial intelligence represents a transformative shift in the way clinical trials are managed, dramatically reducing timelines and costs. From patient recruitment to patient retention, AI has the power to help address bottlenecks at critical junctures in clinical research and help trials cross the finish line on time.
How AI Can Support Clinical Trial Recruitment
Patient recruitment delays are one of the biggest hurdles in clinical trials, with nearly 80% of studies failing to meet their enrollment timelines. By analyzing vast datasets like electronic medical records (EMRs), including both structured and unstructured data, AI can identify patients that meet clinical trial eligibility criteria in far less time.
Algorithms can scan EMRs for specific markers and conditions, ensuring that no eligible patient goes overlooked. They can even convert unstructured eligibility criteria into searchable indices, helping researchers surface qualified candidates more efficiently. This can be especially helpful in rare disease clinical trials, where patients tend to be harder to identify with traditional recruitment methods.
At the same time, AI can assist in prescreening potential clinical trial participants by deploying intelligent bots on research sites' websites. These bots use decision trees and a series of automated questions to assess patient eligibility with a certain degree of probability. This provides more immediate answers for patients while saving administrative staff from having to spend time prescreening patients on the phone.
It's also worth noting that AI can support diversity in clinical trials. By analyzing demographic data, geographic trends, and disease prevalence across underrepresented communities, AI helps researchers proactively identify and engage more inclusive patient populations. If AI identifies that a particular zip code has a high prevalence of Type 2 diabetes among Hispanic adults — a group historically underrepresented in trials — outreach strategies can be tailored to that area, improving representation and meeting FDA diversity planning expectations.
Additional AI-driven recruitment examples include:
- Recommending optimal outreach channels (e.g., social media, primary care referrals, community events) based on how similar populations have historically enrolled
- Identifying eligibility bottlenecks in study criteria that disproportionately exclude otherwise qualified patients
- Forecasting enrollment slowdowns tied to seasonal trends, holidays, or local events
- Prioritizing follow-up with high-intent leads based on response timing and engagement patterns
How AI Can Support Better Patient Retention
While enrolling patients in clinical trials is challenging, keeping patients engaged is another feat. Per the Clinical Leader resource above, the average dropout rate across all clinical trials is about 30%.
AI can analyze patient data to identify early warning signs of patient frustration or noncompliance. By detecting these indicators early, such as changes in engagement behavior, communication patterns, or missed tasks, teams can proactively intervene to prevent participant attrition. In doing so, they help preserve data quality, study timelines, and budget — all while improving the patient experience.
For instance, AI may detect patterns of late medication logging in an eDiary, which can suggest the participant is struggling to keep up with dosing schedules. The trial team can use these insights to send customized reminders or provide a simplified medication schedule if trial protocols allow for it. AI can even personalize these messages using behavioral science-driven strategies to improve adherence.
Meanwhile, if AI systems find that participants from a specific region are frequently rescheduling appointments, they can arrange for virtual visits to reduce that burden. This type of real-time monitoring improves participant safety and makes it easier for sites to respond quickly to emerging issues before they escalate.
Additional AI-driven retention examples include:
- Identifying when engagement drops after study milestones (e.g., invasive procedures or long clinic visits) and triggering proactive check-ins
- Detecting increases in participant support requests that may indicate confusion or fatigue with study requirements
- Analyzing visit duration data to flag when on-site workflows may be contributing to participant dissatisfaction
- Recognizing patterns where participants disengage after adverse events and prompting enhanced follow-up or education
Additional AI Use Cases in Clinical Research
Alongside recruitment and retention efforts, AI may help accelerate trials in other respects too:
Streamlined Clinical Trial Design
Before a study even begins, AI can support protocol optimization by analyzing historical clinical trial data, including prior study designs, endpoints, patient populations, and outcomes. These insights can inform optimal protocol structures, dosing schedules, and visit frequency, lowering the risk of future amendments. In a field where protocol amendments can delay studies by weeks or even months, AI helps trial teams design smarter from day one.
AI can also simulate patient enrollment performance based on different protocol criteria, allowing teams to assess feasibility before launching the trial. This is especially helpful in therapeutic areas with hard-to-reach populations or where inclusion and exclusion criteria may unintentionally limit accrual.
Enhanced Data Collection and Analysis
AI-driven platforms can automate much of the data collection, cleaning, and analysis work that traditionally required manual oversight. For example, machine learning models can identify data anomalies in real-time, reducing the chance of protocol deviations or missed endpoints. Natural language processing (NLP) tools can extract relevant information from unstructured data, such as physician notes or lab reports, and integrate it into the study dataset.
This functionality becomes particularly valuable in hybrid or decentralized clinical trials, where data may flow from multiple sources, including wearables, electronic diaries, and remote visits. AI systems help harmonize that data across platforms so researchers can act on it faster and with more confidence.
Regulatory Compliance Optimization
Keeping up with evolving global regulatory standards is no small task. AI can assist by analyzing regulatory requirements from the FDA, EMA, and other authorities and flagging any gaps or inconsistencies in trial design or documentation. By doing so, it ensures that study protocols, informed consent forms, and case report forms (CRFs) meet compliance standards from the outset.
In some cases, AI can also auto-generate submission-ready documentation or help tailor regulatory filings for different regions. This not only reduces human error but also compresses approval timelines.
Quality Assurance and Documentation Review
Maintaining data quality throughout a study is critical, especially when preparing for audits or regulatory submissions. AI can support quality assurance by reviewing source documents for completeness, accuracy, and consistency. These tools can identify missing data points, flag protocol deviations, and highlight outliers that may warrant additional investigation.
AI can also play a role in patient-facing materials. For instance, it can help generate informed consent forms that are compliant and easy for participants to understand. By using plain language algorithms and readability scoring, AI keeps key study information accessible, which can contribute to higher consent rates and better patient comprehension.
The Challenges of Artificial Intelligence in Clinical Trials
While AI’s potential is immense, its adoption in medical research is not without challenges.
The FDA has yet to issue comprehensive guidelines on the use of AI in clinical trials. Sponsors and research sites are treading carefully to stay compliant with privacy laws and data protection standards, knowing that regulatory dynamics may shift.
At the same time, implementing AI solutions is a significant technology and talent investment. Skilled professionals are needed to train AI systems, maintain data integrity, and engage with vendors. Costs extend beyond implementation, requiring ongoing maintenance and support.
The other layer to all this: the clinical research industry is inherently cautious when adopting new technologies. AI's integration into clinical trials involves technical hurdles, ethical dilemmas, and regulatory uncertainties. It requires careful validation and alignment with existing workflows, a process that can take a considerable amount of time.
The Road Ahead for AI in Drug Discovery
AI holds promise for accelerating drug development, with its use showcasing positive outcomes.
In one case study, AI-assisted recruitment was shown to reduce the time for identifying participants for relevant clinical trials by up to 50%.
The road ahead for AI in clinical trials may require collaboration, investment, and adaptation, but the promise of faster access to therapies, improved data analysis, and better patient outcomes makes it a journey worth taking.
At Remington-Davis, we’re committed to staying at the forefront of clinical trial innovation. We’ve been a leader in the use of other key study technologies like eSource, and we look forward to being part of the AI conversation and finding ways it can support future clinical trial success.
Frequently Asked Questions About AI in the Clinical Trial Landscape
Does AI change how clinical trial protocols are written or approved?
Yes, especially in the trial design phase. AI can help inform protocol development by identifying overly complex inclusion criteria or potential patient burden. However, if AI is used to influence design or decision-making, that needs to be transparently documented. Sponsors must ensure their protocol documents reflect how AI-derived insights were used and remain aligned with FDA guidance—even as that guidance evolves.
Can AI be used retroactively to analyze data from previous trials?
Absolutely. One of AI’s most promising applications in drug development is its ability to surface historical clinical trial data, especially unstructured data like investigator notes or adverse event narratives. Large language models (LLMs) are particularly well-suited for this task, enabling richer pattern recognition and contextual understanding across thousands of documents. This retrospective analysis can identify trends, inform future trial designs, and uncover insights about patient subpopulations that weren't initially apparent.
How do AI tools interact with electronic health records across multiple systems?
This is where integration becomes critical. AI needs standardized access to EHRs, but many systems don’t “talk” to each other. Some vendors are solving this with natural language processing (NLP) and data harmonization engines that can translate and align EHR outputs across trial sites. Sponsors should vet if a site has experience with multi-EHR environments or has partnered with EHR-integrated AI tools.
How can AI help with hybrid or decentralized clinical trials?
AI is particularly valuable in decentralized clinical trials where data is flowing in from wearables, ePROs, and remote visits. It can clean and consolidate these inputs into a unified dataset while identifying gaps or anomalies. AI also helps maintain engagement by flagging participants who may be struggling with adherence.
Will using AI impact data audits or regulatory inspections at clinical trial sites?
Yes, but not necessarily in a negative way. AI tools can enhance audit readiness by reducing transcription errors, flagging missing data in real-time, and offering traceability. That said, auditors will want clear documentation of how AI was used, what decisions it influenced, and whether the outputs were reviewed by qualified personnel. Building audit trails into the AI workflow is essential.