How AI Accelerates Clinical Trial Processes
Designing and executing a clinical trial involves a sequence of high-stakes decisions, each with its own potential to delay timelines. Whether it’s recruiting the right patients, managing multi-source data inputs, or preparing audit-ready documentation, delays can compound quickly — often at a high cost.
In recent years, artificial intelligence has emerged not as a silver bullet, but as a resource for targeting specific bottlenecks in the clinical trial process. Its strength lies in accelerating repeatable, labor-intensive tasks and uncovering insights that may not be obvious through manual review, particularly when working across large datasets, disparate systems, or time-sensitive milestones.
Here's a closer look at where clinical trials tend to slow down in drug development, how AI is being used to accelerate core functions, and what challenges still remain from a broader adoption standpoint.
Where Clinical Trials Tend To Experience Slow Downs
From startup to closeout, clinical studies are vulnerable to breakdowns in communication, coordination, and data flow when timelines are tight and stakeholder expectations are high. Here's where delays tend to show up most:
Startup Phase: Protocol Complexity
Every trial begins with a protocol — and often, with overly ambitious expectations. In the rush to finalize study design, protocols can become bloated with inclusion/exclusion criteria, visit requirements, and data collection points that sound feasible on paper but strain operations in practice.
Once real-world recruitment begins, sites may discover that eligible patients are harder to find than expected. Screening logs start to fill with screen failures, and enrollment targets begin to slip. A protocol amendment may be the only fix, but that adds time, retraining, and regulatory hurdles.
Recruitment Phase: Sourcing Eligible Patients
With the protocol in place, patient recruitment efforts ramp up. For many clinical trials, this is where the friction is felt. Nearly 80% of trials struggle to meet their recruitment goals on time. Whether it’s due to narrow eligibility, lack of community awareness, or outdated outreach strategies, identifying and engaging trial participants remain persistent barriers.
This is particularly true in studies focused on rare diseases, comorbid conditions, or underrepresented populations. Coordinators spend weeks combing through electronic health records (EHRs), mailing flyers, and fielding calls without knowing whether their efforts will yield a single eligible participant.
Mid-Trial: Data Volume, Fragmentation, and Dropout Risk
As participants begin treatment, a new challenge emerges: keeping up with the data. Hybrid and decentralized clinical trials now gather vast amounts of patient data from wearables, ePROs, remote visits, lab reports, and patient portals. Each of these digital health technologies uses different formats and timelines. Study teams spend significant time cleaning and reconciling these inputs, slowing down data analysis and site reporting.
Meanwhile, participant engagement can fade. Missed reminders, scheduling conflicts, and confusing instructions can lead to protocol deviations or silent dropouts. Once a patient leaves a trial, replacing them adds cost and compromises data continuity.
Closeout and Submission: Documentation Pressure
As trials wrap up, the pressure turns to documentation. Pharmaceutical companies must ensure that every patient visit, consent form, data point, and deviation is audit-ready and regulator-approved. For clinical trials that have relied on manual processes or siloed data systems, this can mean weeks of backtracking, reconciling, and confirming what happened and when, while trying to stay on schedule for submission.
AI and Clinical Trials: Where Acceleration Is Most Felt
AI tools can support clinical trial acceleration at nearly every phase, from feasibility planning to patient engagement and beyond:
Recruitment and Prescreening
AI systems can analyze structured and unstructured data within medical records to streamline patient identification and match trial eligibility criteria more efficiently. They also support faster prescreening by powering intelligent bots that guide potential participants through automated eligibility checks on trial websites.
Consider this example: A sponsor is launching a study on a new GLP-1 receptor agonist for obesity-related Type 2 diabetes. AI tools access EMRs and identify patients who meet criteria like BMI over 30, elevated A1C, and medication-naïve status — including those who may not be actively seeing an endocrinologist. The sponsor is able to flag 70 potential candidates in the first week, a process that may otherwise take a coordinator several months manually.
Protocol Design and Optimization
AI can assist in protocol development by analyzing previous trial data to assess which eligibility criteria and schedules are most likely to support recruitment and retention. It can also simulate likely enrollment performance across different trial sites or demographics.
Consider this example: A sponsor designing a COPD study is unsure whether to include patients on home oxygen therapy. AI models simulate different protocol versions and show that including oxygen-dependent participants increases recruitment feasibility by 40% without compromising data quality. The protocol is adjusted before launch, avoiding a costly amendment later.
Predictive Retention Monitoring
AI can monitor participant behavior patterns, such as missed doses, skipped diary entries, or frequent cancellations, to predict dropout risk and improve medication adherence. Coordinators can proactively intervene and support patients before they disengage.
Consider this example: In a study for an injectable monoclonal antibody targeting psoriasis, AI tools notice that a subset of participants consistently log their eDiary entries late on weekends. This prompts the site to call and uncover that several patients are struggling with reminder fatigue. In response, the sponsor enables push notifications via text message, improving patient compliance and retention.
Data Harmonization and Analysis
Clinical trials often rely on data from multiple systems and devices. AI-powered data harmonization tools can clean, format, and align these sources into a unified dataset for faster, more reliable analysis. LLMs can also summarize clinician notes and extract relevant trial information from unstructured text.
Consider this example: A trial for a new topical JAK inhibitor involves photo documentation, patient-reported outcomes, and biometric data from skin sensors. AI tools automatically align these inputs across formats and time points, flagging a discrepancy between a patient’s visual rash improvement and unchanged PRO scores. The site follows up and identifies a misunderstanding in how the patient was scoring symptoms: a data quality issue caught early, thanks to integrated AI monitoring.
Quality Assurance and Documentation Review
AI systems can review case report forms (CRFs), source documents, and regulatory submissions for completeness and consistency. They can also help generate readable, regulation-compliant informed consent forms using plain language and layout optimization.
Consider this example: A multi-site trial is preparing for a pre-approval audit. AI tools flag that 12% of informed consent documents are missing signatures on one specific version. This is identified and corrected before the audit, avoiding a potential delay in trial reporting and regulatory submission.
What Are Hurdles to Integrating AI Into Clinical Trial Design?
Despite its potential to streamline operations and accelerate timelines, integrating AI into clinical trial workflows has its challenges — ones that can hinder adoption:
Regulatory Ambiguity
While the FDA has made strides in offering guidance around AI in diagnostics and medical devices, its position on AI in clinical trial operations — especially for tasks like patient matching, real-time monitoring, or protocol optimization — remains less defined. This lack of formal guidance makes sponsors cautious. Without clear parameters, many choose to treat AI outputs as decision support tools rather than primary decision-makers, limiting how aggressively these tools are applied.
Data System Incompatibility
Even the most powerful AI systems are only as good as the data they can access. Unfortunately, many EHRs, clinical trial management systems (CTMS), and laboratory data sources are siloed or formatted inconsistently. Without robust integration frameworks, AI tools can struggle to reconcile fragmented or conflicting data inputs.
Oversight and Validation Requirements
AI tools must be validated like any other clinical system, and that includes documenting how they're trained, what data they're using, and what decisions they influence. If an AI system flags a patient as eligible, suggests a protocol change, or detects a data discrepancy, someone on the trial team still needs to verify and log that decision.
Resource and Talent Constraints
Implementing and maintaining AI-driven workflows requires cross-functional talent: individuals who understand clinical protocols, regulatory requirements, and data science underpinning the technology. There are also infrastructure costs: cloud computing, software licensing, vendor contracts, and time spent on training.
Clinical Research Sites and Using AI Strategically
At Remington-Davis, we act not only as a high-performing clinical trial site partner but also as a strategic consultant to help sponsors and CROs use technology in ways that truly support clinical trial success. Whether it’s applying AI to streamline recruitment, simplify data workflows, or anticipate patient needs, we ensure these tools are used with purpose.
We also know that in clinical research, there are moments where the human element can’t be replaced. Our team helps identify those points, from patient communication to data interpretation, and uses technology as a form of support, not a substitute. With decades of operational experience and a forward-thinking approach, we help sponsors strike the right balance between innovation and execution.
Because in the end, after all, it’s about using AI wisely.
Frequently Asked Questions About How AI and Clinical Trials
What makes a clinical trial site “AI-ready”?
An AI-ready site is one that has the infrastructure, workflows, and experience to support tech-enabled studies. This includes interoperable data systems, comfort with eSource and ePRO platforms, digital patient engagement strategies, and the operational maturity to document and respond to AI-generated outputs. Just as importantly, an AI-ready site knows how to balance technology with human oversight.
How does artificial intelligence impact the patient experience in a clinical trial?
AI can improve the patient experience by reducing wait times for eligibility feedback, simplifying communication, and personalizing reminders. However, if overused or poorly implemented, AI can create confusion or disengagement. Trials that blend automation with human connection tend to see better retention and satisfaction.
Are all AI tools in clinical trials based on machine learning?
Not necessarily. While many use machine learning to improve over time, others are rules-based systems designed to follow structured decision trees or automate defined tasks. Both approaches have value, but machine learning tools require additional validation and oversight due to their evolving nature.
What are some examples of AI improving decision-making during a trial?
AI helps study teams detect patterns in participant data that may not be visible through manual review, such as subtle shifts in adherence or early patient safety signals. These insights support more proactive clinical decisions, which can enhance trial efficiency and lead to better patient outcomes.
How is AI changing data management at the site level?
AI can automate much of the manual data entry and reconciliation that sites are responsible for, improving the accuracy of case report forms and reducing delays in data management. It can also flag anomalies or incomplete fields in real time, helping sites maintain cleaner datasets for analysis.