How Is Artificial Intelligence Used in Clinical Trials?

Clinical trials are expensive, time-consuming, and prone to failure. Your research coordinators spend weeks manually reviewing patient charts to find eligible candidates. Data arrives fragmented across systems in formats that make analysis nearly impossible. Patients drop out because adherence tracking relies on outdated methods like pill counts and diaries.

By the time you identify a problem with your trial, weeks or months have passed, and the damage is done.

Artificial intelligence (AI) is changing this. AI platforms scan thousands of electronic health records in minutes, monitor data quality in real time, and predict which patients are at risk of dropping out before it happens.

Organizations implementing these technologies are seeing recruitment timelines drop from months to days and trial success rates improve significantly.

This article discusses the specific AI applications impacting clinical research, the regulatory considerations you need to address, and the data infrastructure challenges most organizations face when implementing these solutions.

Key Takeaways

• Clinical trials face high failure rates, recruitment delays, fragmented data, and poor patient adherence, costing the pharmaceutical industry billions and slowing treatment development.
• Artificial intelligence accelerates recruitment by scanning electronic health records (EHRs) rapidly, reducing enrollment timelines from months to days with high accuracy.
• Core AI technologies in trials include machine learning, natural language processing (NLP), computer vision, deep learning, and predictive analytics, each addressing distinct trial challenges.
• AI improves trial design and execution through protocol simulation, adaptive trials, real-time data quality monitoring, and early detection of safety or dropout risks.
• Regulatory compliance remains critical, requiring explainable AI, strong data governance, encryption, audit trails, and alignment with frameworks like HIPAA, FDA guidance, and the EU Artificial Intelligence Act.
• Schedule a free assessment with Datavid to build a compliant, AI-ready data foundation that accelerates recruitment, improves trial success rates, and reduces regulatory risk.

Why Clinical Trials Need Artificial Intelligence

Clinical trials are the gold standard for evaluating new treatments, but they're riddled with inefficiencies that cost the pharmaceutical industry billions annually. Traditional methods struggle with recruitment delays, data management complexity, and patient retention challenges.

Here's why AI has become necessary:

• High Failure and Attrition Rates: Clinical trials typically progress through multiple phases, and participant attrition is substantial. This study by Wolters Kluwer Health, Inc notes that about 70% of participants move beyond Phase I, that around 33% go on to the last phase, and that only about a quarter to a third advance from Phase III to the next stage. These figures highlight how difficult it is to retain and progress participants across the clinical trial lifecycle.
• Patient Recruitment Bottlenecks: Traditional recruitment and screening are often manual and resource-intensive, requiring teams to verify eligibility across many medical-record criteria (e.g., bilirubin and haemoglobin levels, blood counts, and other measures). The study notes that nearly a third of Phase III studies fail due to enrolment issues, and estimates that 86% of trials do not meet recruitment schedules, contributing to delays and failure risk.
• Fragmented Data Management: Trials generate massive amounts of information from electronic health records, lab results, imaging studies, and wearable devices. Much of that data arrives in unusable formats. Hospitals still fax records that become PDFs or photos of handwritten notes. When spreadsheets get faxed, the structure that made data searchable disappears entirely.
• Patient Adherence Challenges: Approximately 40% of patients with various medical conditions fail to adhere to treatment recommendations, with nonadherence reaching up to 70% for complex regimens involving multiple medications or lifestyle modifications. Historically, adherence thresholds around 80% have been used to classify patients as adherent or non-adherent, but evidence suggests the ‘right’ threshold can vary by disease, medication, and patient factors.

Core AI Technologies Reshaping Healthcare

Several AI technologies are making their way into healthcare, each handling different pieces of the clinical trial puzzle.

Machine Learning Algorithms

Machine learning algorithms dig through historical data to predict what's coming next. They can tell you which patients are most likely to respond to a treatment, which participants might drop out, and where adverse events might occur before they actually happen.

Natural Language Processing (NLP)

Natural language processing lets computers make sense of unstructured text like physician notes, radiology reports, pathology results, and patient messages. In clinical trials, NLP systems pull relevant clinical information from these sources without anyone having to read through them manually.

Research shows certain tools can extract patient eligibility criteria from medical records with over 93% accuracy.

Computer Vision

Computer vision analyzes images, photos, and videos. These systems check whether images patients submit meet quality standards, spot specific features in diagnostic imaging, and monitor visual data for signs of treatment response or problems.

This means patients don't have to keep resubmitting poor-quality photos while your data stays reliable.

Deep Learning Neural Networks

Deep learning neural networks represent the most advanced AI, capable of processing enormous datasets with countless variables at once. They excel at biosimulation, which is modeling how drugs interact with human biology across different patient types, genetic profiles, and circumstances.

Companies like VeriSIM Life use deep learning to simulate drug effects on organs and body systems before compounds ever touch a human subject.

Predictive Analytics

Predictive analytics pulls multiple AI approaches together to forecast trial outcomes based on your protocol design, patient characteristics, site selection, and what's worked (or hasn't) in the past.

How AI Is Actually Being Used in Clinical Trials

The real value of AI becomes clear when you look at what's happening in clinical research right now. Organizations are deploying these tools and seeing measurable results.

Here's how AI is being implemented across different stages of the clinical trial process.

1. Finding and Enrolling Patients Faster

AI has completely changed how trials identify and enroll participants.

Where research coordinators used to manually review hundreds of charts per week to find a handful of eligible candidates, AI systems now scan thousands of electronic health records in minutes, matching patient characteristics against trial criteria with impressive precision.

For example, BEKHealth's platform uses natural language processing to analyze both structured data and those messy clinical notes buried in electronic health records. The system processes records, physician notes, and charts, identifying eligible patients three times faster than manual review.

At institutions using this technology, the time from spotting a potential participant to making contact has dropped from days or weeks to hours.

Another example here would be the results Dyania Health showed at the Cleveland Clinic. Their AI system delivered a 170-fold speed improvement in finding eligible candidates with 96% accuracy.

Instead of research staff spending hours combing through each patient's complete medical history, they get a prioritized list of people who meet the criteria, complete with supporting documentation.

2. Designing Better Trials from the Start

AI lets researchers test different trial designs virtually before committing real resources. Machine learning models simulate various protocol configurations and predict outcomes based on what has happened in similar trials.

These simulations evaluate optimal dosing schedules, appropriate sample sizes, treatment duration, and monitoring frequency. You can identify which protocol elements will likely yield meaningful results while keeping the burden on patients manageable.

Adaptive trials represent one of the biggest applications here. Traditional trials follow rigid protocols set at the outset, but adaptive trials use data collected during the study to modify the study in real time.

AI systems continuously analyze accumulating results and suggest adjustments to treatment arms, dosing levels, or patient population criteria. This flexibility makes trials more efficient and can lead to faster, more accurate conclusions.

3. Managing Data Without Drowning In It

Real-time AI monitoring has changed the way trials track participant data and maintain data quality. Traditional trials rely on periodic review cycles, which means problems might not surface until weeks or months after they occur.

AI platforms continuously monitor incoming data, flagging anomalies, inconsistencies, and potential issues in real time. You catch small problems before they turn into major data integrity disasters.

Automated collection reduces the burden on clinical staff and cuts down on human error. AI tools pull data from wearables, smartphone apps, electronic health records, and patient-reported outcomes into unified platforms.

Participants submit information through user-friendly interfaces while the system automatically validates entries, checks for missing pieces, and prompts patients when more information is needed.

AI manages clinical trial data through:

• Continuous quality monitoring that flags inconsistencies, outliers, and potential errors as data arrives rather than discovering problems during scheduled reviews.
• Automated extraction from unstructured sources like physician notes, converting free-text clinical documentation into structured, searchable data.
• Cross-source validation that checks whether information from different systems matches, identifying discrepancies between lab results, imaging reports, and clinical assessments.
• Real-time completeness checks that alert teams to missing data points immediately, allowing them to follow up with participants while the information is still fresh.

4. Keeping Patients Engaged and Compliant

Patient retention can make or break a trial, and AI helps keep participants engaged throughout the process. Algorithms analyze patient data to deliver personalized reminders, educational content, and support, keeping people informed and involved.

When someone's at risk of dropping out or not following the protocol, the system flags it early so you can intervene.

One way AI can help here is through AI-powered voice assistants. These assistants handle routine tasks such as appointment reminders, tracking daily activities, and facilitating communication between sites and sponsors.

Apps like Medisafe and MyTherapy are good examples of this and provide medication reminders, instructional material, dosage monitoring, and let physicians access patient-reported data.

5. Meeting Regulatory Requirements with the Right Partner

AI streamlines regulatory submissions by preparing documentation and checking compliance with local and international guidelines, but implementing AI in a HIPAA-compliant organization requires iron-clad safeguards.

The European Commission's Artificial Intelligence Act provides a framework for high-risk AI systems, which includes many clinical trial applications. The FDA is also developing guidance for AI-enabled healthcare solutions.

You'll need to validate and explain your AI models in ways that satisfy regulatory requirements, which generally means extensive testing, documentation of algorithms, and clear explanations of how systems make decisions.

This is where Datavid's expertise in regulated industries comes into play. We build data architecture with compliance built in from day one, rather than bolted on as an afterthought.

Our platforms implement FAIR principles (Findable, Accessible, Interoperable, Reusable) that regulators expect, while providing the data observability, governance, and lineage tracking that auditors demand. When you're asked to demonstrate how your AI made a decision, you'll have the documentation ready.

Organizations must encrypt protected health information both at rest and in transit. Access controls determine who can view or interact with sensitive patient data. Audit logs track every interaction with AI systems. Datavid's semantic data platforms handle these requirements as core features.

We've helped pharmaceutical companies, healthcare providers, and research organizations stay compliant with HIPAA, FDA, and European regulatory requirements through our work with clients like Roche and Syngenta.

Ready to build AI capabilities on a compliant foundation? Book a free assessment and learn how Datavid accelerates your path to AI-ready, regulation-proof data infrastructure.

What AI Cannot Replace in Clinical Trials

AI brings tremendous capabilities, but certain aspects of clinical research require human judgment that no algorithm can match.

Here's what remains firmly in human territory:

• Clinical Judgment and Expertise: Physicians develop pattern recognition over years of practice that goes beyond what datasets can capture. They consider patient history, comorbidities, and confounding variables, requiring intuition and experience. When a patient presents with unusual symptoms or conflicting data, clinical expertise navigates complexities that algorithms can't handle.
• Creativity and Innovation in Research: Design AI works with existing information, and it can't generate truly novel hypotheses or think beyond what's in its training data. Researchers bring creativity to developing research questions, imagining new approaches, and connecting disparate ideas in ways that lead to breakthroughs. Exploring the unknown and developing innovative solutions requires human oversight that AI simply doesn't possess.
• Patient Relationships and Trust: Building trust with patients, addressing their individual concerns, and maintaining long-term relationships that keep people engaged throughout lengthy trials all depend on human connection. Patients come to physicians for more than just technical expertise. They want understanding, empathy, and guidance tailored to their unique situation. That relationship-building is what keeps people in trials long-term, and it's something algorithms can't replicate.

Implementation Challenges Your Organization Will Face

The benefits of AI in clinical trials are clear, but implementation rarely goes smoothly without proper planning.

Organizations typically encounter several obstacles that can derail or significantly delay AI adoption. Knowing these challenges upfront helps you prepare for them and build solutions into your implementation strategy from the beginning.

Data Quality and Integration Obstacles

Getting AI to work properly depends entirely on the quality of data you feed it. Many organizations find out that their data isn't AI-ready when they start implementation.

Medical research databases have historically overrepresented European and Caucasian populations, which means AI trained on these datasets lacks knowledge about other demographic groups. This leads to biased findings that may not apply to diverse patient populations.

Unstructured data is another major hurdle. Faxed records, PDFs, and photographs of handwritten notes are all difficult for AI systems to process effectively.

When clinical data arrives in inconsistent formats across multiple sources, integrating it into a unified system that AI can analyze becomes a significant technical challenge.

Cost and Expertise Requirements

Implementing AI isn't cheap. The initial investment in technology and infrastructure can be substantial, and you need specialized expertise in both clinical research and machine learning to do it right.

Many organizations find they lack the internal talent to develop, validate, and maintain AI systems effectively. Partnering with vendors helps, but you still need people who understand both the clinical context and the technical capabilities well enough to make sure the AI is actually solving the right problems.

Building Trust and Transparency

The "black box" problem makes many clinicians hesitant to adopt AI. Deep learning models can make accurate predictions without being able to explain how they arrived at those conclusions.

In clinical trials, where you need to justify every decision to regulators, sponsors, and ethics boards, this lack of interpretability creates serious concerns.

You need to be able to explain why the AI recommended including or excluding a patient, why it flagged certain data as problematic, or how it arrived at a predicted outcome.

How Datavid Is the First Step in AI Implementation for Clinical Trials

Before your organization can effectively deploy AI in clinical trials, you need a data infrastructure that's actually ready for it.

Most AI implementation failures trace back to data problems such as fragmentation, poor quality, inadequate governance, or systems that weren't designed with AI in mind. Fixing these issues after the fact is expensive and time-consuming.

Getting them right from the start makes AI implementation dramatically smoother.

Datavid's approach focuses on building that foundation properly. Our data integration services consolidate information from electronic health records, laboratory systems, imaging platforms, patient-reported outcomes, and wearable devices into unified views.

This consolidation is what allows AI to see the complete picture rather than fragments. We implement semantic enrichment through knowledge graphs and ontology management, which gives your data the structure and context AI systems require.

This semantic layer is what allows AI to understand that "cardiovascular event" connects to dozens of specific conditions, that patient populations can be stratified in multiple meaningful ways, and that treatment outcomes depend on complex interrelationships between factors.

Without this semantic foundation, AI struggles to extract meaningful insights from clinical data.

The pharmaceutical and biotech companies we work with use our platforms to accelerate their path to AI readiness. We deliver semantic pipelines and data foundations in weeks rather than years through our Datavid Rover accelerator framework. This speed matters when you're trying to deploy AI capabilities that can cut months off recruitment timelines or improve trial success rates.

Looking to avoid data-related problems before they come up? Datavid helps you create a data foundation that makes everything else possible. Schedule a free assessment to learn more today.