AI in Clinical Trials: Governance Risks Pharma and Life Sciences Teams Should Address Now
- Jun 8
- 6 min read
Artificial intelligence is becoming increasingly relevant across clinical research and drug development. Pharma, biotech, CROs, and life sciences organizations are exploring AI to support protocol design, trial feasibility, site selection, patient recruitment, data review, safety monitoring, documentation, and operational efficiency.
The opportunity is significant.
But AI in clinical trials also introduces governance risks that cannot be ignored.
When AI influences who is identified for a study, how trial sites are selected, how data are reviewed, or how operational decisions are made, organizations need clear oversight. Without governance, AI can create risks related to bias, transparency, data integrity, patient safety, regulatory readiness, and accountability.
For life sciences leaders, the question is not simply whether AI can improve clinical trial efficiency.
The question is whether AI is being used in a way that is responsible, explainable, validated, and fit for purpose.
Why AI Governance Matters in Clinical Trials
Clinical trials depend on trust, data quality, participant safety, scientific integrity, and regulatory credibility. AI can support these goals, but only if it is implemented with the right safeguards.
AI may be used to:
identify eligible participants
optimize site selection
support protocol design
analyze real-world data
monitor safety signals
review clinical trial documentation
support trial operations
improve patient engagement
assist with data cleaning or anomaly detection
generate summaries or operational insights
Each of these use cases carries a different level of risk.
An AI tool used for internal administrative support may require basic controls. An AI system used to support eligibility screening, safety review, or trial decision-making requires much stronger governance.
Regulators are also paying closer attention to AI in drug development. FDA has published resources on AI in drug development, including a discussion paper on AI and machine learning in drug and biological product development and considerations for the use of AI to support regulatory decision-making. EMA has also issued a reflection paper addressing the use of AI across the medicinal product lifecycle, from drug discovery to post-authorization activities.
Start With Intended Use
The first governance question should always be:
What is the AI tool intended to do?
Intended use determines risk. It also determines the level of validation, documentation, human oversight, and monitoring required.
Life sciences teams should ask:
What workflow will this AI tool support?
Will it influence trial design, recruitment, enrollment, monitoring, safety, or data interpretation?
Is the tool administrative, operational, clinical, regulatory, or patient-facing?
Who will use the output?
Will humans review the output before action is taken?
What could happen if the output is wrong?
What documentation is needed to support use of the tool?
If intended use is unclear, governance will be weak from the start.
Address Bias in Recruitment and Eligibility
One of the most important AI risks in clinical trials is bias.
AI tools used for recruitment, eligibility screening, or patient identification may reflect the limitations of the data used to build them. If the underlying data are incomplete, unrepresentative, or shaped by unequal access to care, the AI system may fail to identify certain populations or may over-prioritize others.
This matters because clinical trial diversity remains a major challenge.
Organizations should ask:
What data were used to develop the recruitment or screening tool?
Are underrepresented populations adequately represented?
Does the tool perform differently by race, ethnicity, age, sex, geography, language, socioeconomic status, or site type?
Could the tool exclude patients because of documentation gaps or access barriers?
Are outputs reviewed by clinical or research staff?
How will recruitment patterns be monitored after deployment?
AI should not make trial access less equitable. Governance should ensure that AI-supported recruitment expands opportunity rather than narrowing it.
Evaluate Data Quality and Provenance
AI systems are only as reliable as the data used to develop and operate them.
In clinical trials, data may come from electronic health records, claims, registries, imaging, genomics, labs, wearables, patient-reported outcomes, real-world data sources, or prior studies. Each data source has limitations.
Governance should address:
data provenance
completeness and missingness
data quality
consent and permissible use
privacy and confidentiality
source system limitations
representativeness
documentation practices
data transfer and storage
auditability
Teams should be especially cautious when AI tools rely on real-world data that may reflect uneven access to care, inconsistent coding, or differences in documentation across sites.
Data governance is AI governance.
Validate AI Tools for the Trial Context
AI tools should be validated for the context in which they will be used.
A tool that performs well in one therapeutic area, geography, population, or health system may not perform the same way in another. This is especially important for global trials, decentralized trials, rare disease studies, oncology trials, and studies involving complex eligibility criteria.
Validation should consider:
intended use
therapeutic area
patient population
trial phase
site setting
geography
available data sources
workflow integration
subgroup performance
human review process
limitations and failure modes
For higher-risk use cases, organizations should document the evidence supporting use of the AI system and define what level of human oversight is required.
Maintain Human Oversight
AI should support clinical research decision-making, not replace accountable human judgment.
Human oversight is especially important when AI may influence eligibility, enrollment, safety review, endpoint assessment, protocol deviations, or regulatory documentation.
Organizations should define:
who reviews AI outputs
what expertise is required
when outputs must be verified
when AI outputs cannot be used alone
how disagreements are resolved
how errors are reported
how decisions are documented
EMA’s reflection paper emphasizes that AI use in the medicinal product lifecycle should follow a human-centric approach and comply with existing legal, ethical, and fundamental rights requirements.
Monitor AI Performance After Deployment
AI governance does not end when a tool is approved for trial use.
Performance can change as workflows, sites, populations, data sources, or vendor models change. AI tools used across multiple sites or countries may also perform differently across settings.
Post-deployment monitoring should include:
tool performance
subgroup performance
error patterns
user adoption
workflow impact
bias or equity signals
safety concerns
data drift
vendor updates
audit findings
documented incidents or deviations
Monitoring should be tied to action. If concerns arise, organizations should have a process to pause, modify, restrict, or retire the tool.
Clarify Vendor Accountability
Many AI tools used in clinical trials are provided by vendors. That makes vendor governance essential.
Before adopting a vendor AI tool, life sciences teams should ask:
What exactly does the tool do?
What data were used to develop and validate it?
Has it been tested in similar trial contexts?
What are the known limitations?
Does the vendor provide documentation suitable for audit or regulatory review?
How are model updates handled?
Can the sponsor review performance data?
Are bias and subgroup performance assessed?
What happens if the tool produces an error?
How are data privacy and secondary data use managed?
Vendor claims should not replace organizational accountability. Sponsors and research organizations still need their own governance process.
Prepare for Regulatory Readiness
AI use in clinical trials should be documented in a way that supports inspection readiness and regulatory confidence.
Depending on the use case, organizations may need to document:
intended use
rationale for AI use
validation evidence
data sources
human oversight
risk assessment
vendor review
monitoring plan
version control
change management
incident response
limitations
impact on trial conduct or data integrity
FDA’s discussion paper on AI and machine learning in drug development was intended to promote stakeholder discussion and mutual learning; it is not itself guidance or policy. Still, the regulatory direction is clear: organizations using AI in drug development should expect growing attention to transparency, reliability, fit-for-purpose use, and documentation.
What Life Sciences Leaders Should Do Now
Pharma, biotech, CRO, and life sciences leaders should begin by asking:
Where are we currently using AI across clinical trial workflows?
Which AI tools influence participant identification, enrollment, safety, data quality, or trial decisions?
Do we have a governance process for reviewing AI use cases?
Are AI tools validated for the intended trial context?
Do we monitor bias, performance, and workflow impact after deployment?
Do vendor agreements define documentation, accountability, and model update expectations?
Are we prepared to explain how AI was used if regulators, partners, or study teams ask?
If the answers are unclear, AI use may be moving faster than governance.
Responsible AI in Clinical Trials Requires Structure
AI has the potential to improve clinical trial efficiency, expand access, reduce operational burden, and strengthen decision-making. But these benefits depend on responsible implementation.
Clinical trial AI governance should include clear intended use, risk tiering, validation, bias assessment, human oversight, monitoring, documentation, and vendor accountability.
The organizations that lead in AI-enabled clinical research will not simply be those that use the most tools.
They will be the organizations that can demonstrate that their AI systems are fit for purpose, responsibly governed, and aligned with participant safety, scientific integrity, and regulatory expectations.
Need Support With AI Governance in Clinical Trials?
CROSS Global Research & Strategy advises healthcare, pharma, digital health, and life sciences organizations on responsible AI strategy, governance, validation, and implementation.
We help teams evaluate AI use cases, assess vendor risk, define governance workflows, build monitoring plans, and operationalize AI accountability across clinical care and clinical trial environments.
To discuss how your organization can strengthen AI governance across clinical trial workflows, contact CROSS Global Research & Strategy.
Suggested References
US Food and Drug Administration. Artificial Intelligence for Drug Development. FDA. Updated 2026.
US Food and Drug Administration. Using Artificial Intelligence & Machine Learning in the Development of Drug & Biological Products: Discussion Paper. FDA; revised 2025.
European Medicines Agency. Reflection Paper on the Use of Artificial Intelligence in the Medicinal Product Lifecycle. EMA; 2024.
National Institute of Standards and Technology. Artificial Intelligence Risk Management Framework (AI RMF 1.0). NIST; 2023.
URAC. Health Care AI: Accountability in Practice. URAC; 2026
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