AI’s Role in Clinical Trial Patient Selection

Eligibility criteria for a clinical trial can contain dozens of variables, from lab results and diagnostic codes to comorbidities, medication histories, and other forms of medical data. For coordinators and investigators, that means poring through electronic health records (EHRs), verifying documentation, and making nuanced decisions about whether a patient fits the protocol before enrollment can begin.

Artificial intelligence (AI) doesn’t change the science or the criteria behind patient eligibility; it supports how clinical trial sites, sponsors, and CROs operationalize those rules. AI helps identify and match patients who are more likely to qualify, reduces time spent on ineligible candidates, and enables better alignment between protocol design and real-world clinical practice while protecting patient outcomes.

Here’s how that process unfolds and where AI fits in.

The Layers of Patient Eligibility in Clinical Research

Eligibility criteria are a multi-layered clinical framework used to protect clinical trial patients and guide protocol compliance. These layers ensure that patient data is applied correctly to determine fit, safety, and potential benefit.

Let’s break it down:

• Inclusion criteria outline what a patient must have (e.g., a confirmed diagnosis, a specific biomarker, or age range).
• Exclusion criteria outline what a patient must not have (e.g., a comorbidity, medication contraindication, or certain lab values).
• Conditional criteria create “if-then” scenarios (e.g., if a patient is on X medication, then they must also meet Y lab threshold).
• Temporal criteria relate to timing, such as how recently a patient was diagnosed or treated.

Each layer is critical, and missing just one detail can mean enrolling the wrong patient. This can:

• Put the patient at risk
• Skew study data
• Lead to protocol deviations or early discontinuation
• Trigger costly regulatory issues

That’s why getting eligibility right the first time around is a must. It’s also why AI, when applied thoughtfully, can be such a powerful support system for busy clinical research teams.

How Artificial Intelligence Supports the Eligibility Review Process

Artificial intelligence doesn’t define patient eligibility criteria, but it can make applying those criteria significantly easier and faster for coordinators and investigators.

Here’s how AI-powered tools can support the process from start to finish:

Structured Data Review

AI can scan structured fields in the EHR (such as diagnosis codes, lab values, medications, and demographics) to quickly assess whether a patient appears to meet key inclusion and exclusion criteria. This helps filter out clearly ineligible patients early in the process.

Unstructured Data Extraction

Many critical data points live in unstructured notes, like radiology reports or clinician narratives. Natural Language Processing (NLP) models can extract mentions of qualifying terms (e.g., “Stage 3 CKD” or “history of myocardial infarction”) that may not be coded but are relevant to the clinical trial.

Real-Time Pre-Screening & Alerts

Once eligibility criteria are programmed into an AI system, it can continuously monitor EHR systems to flag patients who newly qualify based on lab results, diagnoses, or medication changes. This enables just-in-time patient recruitment.

Patient Eligibility Scoring Models

Advanced machine learning models can assign an eligibility likelihood score based on the completeness and alignment of a patient’s profile. From there, sites can then prioritize outreach to the most promising candidates, saving time and resources in clinical trial recruitment.

Risk Identification & Exclusion Support

AI can also flag potential exclusion risks that might be overlooked during manual review, particularly in complex protocols. Examples include prohibited medications or overlapping clinical trial participation.

The Benefits of AI in Patient Selection

By supporting eligibility assessments in these ways, AI brings tangible, measurable benefits to the patient selection process:

• Accelerated Screening: Sites spend less time sifting through charts and more time connecting with patients who are likely to qualify.
• Reduced Screen Failures: By filtering out ineligible patients earlier, AI helps avoid the time and cost associated with failed screenings, which can be high in complex or competitive trials.
• Cleaner Data & Fewer Protocol Deviations: With eligibility accurately applied from the start, studies benefit from higher data integrity and fewer mid-study exclusions due to criteria misalignment.
• Better Reach Across Populations: AI can help flag qualified patients from historically underrepresented groups, especially when trained to include variables related to social determinants of health, language, or geography, further supporting FDA diversity initiatives.

Clinical Scenarios Where AI Has the Biggest Impact

AI is helpful across all clinical trials, but its value is magnified in certain scenarios:

Infectious Disease Clinical Trials

For acute infections like RSV, influenza, or COVID-19, enrollment windows are short. AI can monitor EHRs and lab feeds in real time to flag newly diagnosed patients who meet eligibility, allowing site staff to act quickly before the window closes.

Rare Disease Clinical Studies

With narrow criteria and low-prevalence populations, rare disease trials require finding the right patient out of thousands. AI can efficiently scan large datasets to identify subtle diagnostic patterns, genetic markers, or treatment histories that indicate eligibility.

Vaccine and Preventive Studies

These studies often require enrolling healthy volunteers who meet strict baseline criteria, including no prior exposure, normal lab values, and a clean medical history. AI can help pre-screen large patient databases to rule out common disqualifiers and surface high-probability candidates faster.

Human Judgment Remains Essential in Patient Selection

Even with AI at your side, clinical judgment can’t be outsourced.

There’s still a need to:

• Interpret ambiguous or borderline cases
• Evaluate nuanced patient histories and contraindications
• Have conversations that assess willingness, consent readiness, and adherence potential
• Adapt when eligibility criteria evolve mid-study

And before any of that can happen, eligibility criteria must be defined. AI can help scale the process, but it can’t determine what’s clinically appropriate, ethically sound, or operationally realistic.

At Remington-Davis, we take a consultative role with sponsors and CROs, helping shape eligibility criteria that are both scientifically rigorous and feasible in the real world. Once defined, we use technology to support our efforts — but always with a human lens.

For example, we use EHR-based pre-screening to identify high-probability candidates faster; then coordinators validate eligibility manually and document criteria in eSource/eReg for clean audit trails. Throughout the study, CTMS-based workflows and automated reminders support visit adherence and reduce preventable screen failures and discontinuations.

The art of patient selection lives in that balance. Technology helps us move faster. Our experience helps us move smarter. And our people make sure patients feel understood, supported, and confident.

If you're looking for a partner to manage your next clinical trial, let's talk.

Frequently Asked Questions

What’s the difference between patient selection and patient recruitment in clinical trials?

Patient selection refers to determining whether an individual is eligible to participate in a trial based on inclusion/exclusion criteria. Patient recruitment is the broader process of attracting and engaging potential participants — through advertising, outreach, referrals, and more — before they are screened for eligibility.

How does AI-driven patient selection influence trial outcomes?

By helping sites enroll patients who more precisely match a study’s inclusion and exclusion criteria, AI improves the consistency and reliability of trial data. This leads to cleaner endpoints, fewer outliers, and more meaningful results. AI also reduces the likelihood of enrolling borderline or ineligible patients who may drop out or require early discontinuation. In short, better patient matching from the start improves patient retention and chances of producing valid, actionable trial results.

Besides patient selection, how else do AI-driven tools support trial efficiency?

AI can help predict recruitment timelines by analyzing population-level data, identify high-performing sites based on historical trial performance, and even flag potential protocol deviations before they occur. Additionally, AI plays a growing role in remote monitoring and data cleaning, streamlining data management and reducing site burden.

What are the biggest challenges with integrating AI into clinical trial design?

One of the main challenges is ensuring that AI tools align with real-world clinical workflows. Many platforms aren’t fully integrated with EHR systems or require data that isn’t consistently structured across records. Another challenge is ensuring that AI respects regulatory and ethical standards.

Can AI improve patient safety in clinical trials?

Yes, indirectly. By ensuring patients meet eligibility criteria related to pre-existing conditions, lab values, or contraindications, AI reduces the risk of enrolling individuals who may be harmed by study participation. It also helps catch exclusion flags early in the process, before an ineligible participant progresses through screening.