AI in Clinical Trials: 10 Practical Ways a CRA Can Use AI Without Losing Human Oversight
Clinical trials generate an enormous amount of information. Site performance data, monitoring reports, protocol deviations, adverse events, queries, laboratory results, essential documents, recruitment numbers, action items and communications all need to be reviewed and managed throughout the study. For a Clinical Research Associate (CRA), the challenge is not simply having access to information it is identifying what requires attention, what represents a genuine risk, and what action should be taken. This is where Artificial Intelligence (AI) can become a practical co-pilot for the CRA. AI should not replace the CRA's clinical and operational judgment. Instead, it can help reduce repetitive work, organize information, identify patterns and support risk-focused monitoring.
9/29/20265 min read
Clinical trials generate an enormous amount of information.
Site performance data, monitoring reports, protocol deviations, adverse events, queries, laboratory results, essential documents, recruitment numbers, action items and communications all need to be reviewed and managed throughout the study.
For a Clinical Research Associate (CRA), the challenge is not simply having access to information it is identifying what requires attention, what represents a genuine risk, and what action should be taken.
This is where Artificial Intelligence (AI) can become a practical co-pilot for the CRA.
AI should not replace the CRA's clinical and operational judgment. Instead, it can help reduce repetitive work, organize information, identify patterns and support risk-focused monitoring.
The following are 10 practical ways CRAs can use AI in their daily work while maintaining appropriate human oversight.
1. Prepare for a Monitoring Visit Faster
Before a monitoring visit, a CRA may need to review previous monitoring reports, open action items, protocol deviations, queries, recruitment data, safety information and site communications.
AI can help summarize this information into a structured pre-visit briefing.
For example:
AI summary:
3 major action items remain open
Recruitment is below the planned rate
Query volume increased during the previous month
Two protocol deviations remain under assessment
One essential document requires follow-up
The CRA can then review the underlying records and decide what should actually be discussed with the investigator.
AI organizes the information. The CRA interprets it.
2. Identify Sites That May Need Additional Attention
Not every site requires the same level of monitoring attention.
A site with consistently good performance may require a different approach from a site experiencing repeated deviations, delayed data entry or recruitment problems.
AI can help identify patterns across multiple indicators, such as:
Recruitment performance
Protocol deviations
Query rates
Data-entry delays
Safety reporting timelines
Missing documents
Screen-failure patterns
Subject withdrawal rates
Outstanding action items
This supports the principles of risk-proportionate clinical trial oversight described in ICH E6(R3), where risks affecting participant protection and reliability of trial results should be identified, evaluated and managed appropriately.
However, an AI-generated "high-risk" flag should not automatically mean that a site is non-compliant.
The CRA still needs to investigate the reason behind the signal.
3. Spot Unusual Data Patterns
Humans are good at understanding context, but reviewing thousands of individual data points can make subtle patterns difficult to recognize.
AI can assist by identifying unusual trends.
For example:
A CRA may notice that one site has a significantly higher number of:
Missing assessments
Out-of-window visits
Repeated laboratory abnormalities
Data corrections
Same-day data entries
Protocol deviations
The AI does not determine whether misconduct or non-compliance has occurred.
Instead, it provides a signal for investigation.
The CRA then goes back to the source information and determines whether the pattern has a reasonable explanation.
4. Review Protocol Deviations More Efficiently
Protocol deviations can become difficult to manage in large multicentre studies.
AI can help categorize deviations by:
Type → Frequency → Site → Subject → Severity → Recurrence
For example:
"Five deviations related to visit-window compliance were identified across three sites, with four occurring at Site B."
This can help the CRA quickly identify whether the issue is isolated or recurring.
The important point is that AI should support trend identification, while the CRA and study team determine the clinical and operational significance.
5. Support Safety Review
Safety information requires particularly careful handling.
AI can assist with organizing information from adverse-event listings, narratives and safety-related communications.
For example, it may identify:
"Several adverse-event reports from one site contain similar terminology and occurred within a similar time period."
That observation may help the CRA identify an area requiring further review.
But AI should not independently determine causality, seriousness, expectedness or regulatory reporting requirements unless the system has been specifically designed, validated and authorized for that intended purpose.
Safety decisions require appropriate qualified human oversight.
6. Reduce Monitoring Report Preparation Time
Monitoring reports contain substantial documentation.
A CRA may need to summarize:
Activities performed
Subjects reviewed
Source-data activities
Investigational product accountability
Safety review
Protocol deviations
Essential documents
Site issues
Action items
Follow-up requirements
AI can help create a structured first draft from information already documented by the CRA.
But there is an important rule:
AI-generated text should never be treated as automatically accurate.
The CRA should verify every important statement against the actual monitoring records before finalizing the report.
This approach can reduce administrative workload without transferring accountability to the AI system.
7. Track Action Items and Follow-Ups
A common operational problem is not identifying an issue—it is ensuring that the issue is followed through to closure.
AI can help organize action items according to:
Issue → Responsible Person → Due Date → Status → Escalation
8. Help With Document Review
Clinical trials involve large numbers of documents.
AI can assist in identifying potentially missing or inconsistent information across documents.
Examples include:
Missing signatures
Missing dates
Inconsistent investigator information
Expired documents
Duplicate documents
Missing training records
Inconsistent version numbers
This can be particularly useful during site activation, ongoing document maintenance and close-out activities.
However, document-review AI should be treated as a screening and support mechanism, not as the final authority.
9. Turn Large Trial Data Into a Simple Story
One of the biggest advantages of AI is its ability to summarize large quantities of information.
Instead of reviewing multiple spreadsheets individually, a CRA could use an appropriately controlled system to generate a summary such as:
10. Use AI as a Second Pair of Eyes—not the Final Decision-Maker
Perhaps the most useful role for AI is as a second pair of eyes.
Imagine a CRA reviewing a site and AI highlights:
Potential concern: Increase in protocol deviations during the last two months.
The CRA investigates and discovers that the site recently added two new study coordinators.
Further review shows that the deviations are related primarily to scheduling errors.
The CRA can then determine whether additional training, process correction or other action is appropriate.
The AI identified the pattern.
The CRA understood the reason behind the pattern.
That distinction is critical.
What AI Should Not Replace
There are certain responsibilities where human oversight remains essential.
AI should not independently replace the CRA's responsibility for:
Assessing clinical and operational context
Communicating with investigators
Evaluating significant findings
Making appropriate escalation decisions
Assessing participant-safety implications
Confirming source information
Determining whether an issue represents non-compliance
Approving final monitoring documentation
Maintaining appropriate accountability
ICH E6(R3) continues to place emphasis on qualified individuals, appropriate oversight, quality management and processes proportionate to risk.
The Biggest Risk: Trusting AI Too Much
AI can produce a confident answer that is incomplete, inaccurate or based on an incorrect interpretation of the available information.
Therefore, a practical CRA workflow should look like this:
AI identifies → CRA verifies → CRA interprets → CRA decides → CRA documents
Not:
AI identifies → AI decides → CRA accepts
That difference is fundamental.
Data Privacy and Data Integrity Must Come First
Before introducing AI into clinical trial operations, organizations should consider how trial information is handled.
Important areas include:
Patient confidentiality
Access controls
Data privacy
System validation
Intended use
Audit trails
Data integrity
Cybersecurity
Change control
Version control
User training
Record retention
FDA guidance on electronic systems used in clinical investigations emphasizes trustworthy and reliable electronic records, security, documentation and auditability. FDA's current guidance also describes audit trails as an important mechanism for reconstructing changes to electronic records.
Therefore, do not simply copy confidential trial data into a public AI tool because it is convenient.
The AI environment, data flow, access permissions, contractual arrangements and intended use should be appropriately assessed before deployment.
References
ICH E6(R3) – Good Clinical Practice, International Council for Harmonisation.
FDA – Electronic Systems, Electronic Records, and Electronic Signatures in Clinical Investigations: Questions and Answers, October 2024.
FDA – Electronic Source Data in Clinical Investigations, Guidance for Industry.
FDA – Guidance for Industry: Computerized Systems Used in Clinical Trials.