Navigating AI/ML Risks in Clinical Trials: A Cautionary Approach to Innovation

From automating routine tasks to predicting dropout risks, AI/ML is a promising prospect for greatly improving trial operations, including:

• Recruitment and retention forecasting
• Real-time risk-based monitoring
• Adaptive protocol design
• NLP for data mining from clinical records

But these advancements introduce complexity. Many AI systems require large datasets to train effectively – and when those datasets contain sensitive clinical data, the risk profile changes.

Data Leakage and Model Misuse: The Overlooked AI Risk

The most dangerous vulnerabilities are the ones you can’t see – like a model quietly learning from your trial data, only to reuse that learning elsewhere.

AI platforms, especially those relying on continual training, may:

• Absorb clinical or patient data without proper segregation
• Blend study-specific insights into models shared across clients
• Lack transparent reporting on how datasets are stored or utilized

This creates a hidden pathway where confidential or unique data can unintentionally influence future outputs, or worse – become part of shared models used outside your organization.

Governance Challenges: Are Current Controls Built for AI?

Regulations like HIPAA, GDPR, 21 CFR Part 11, and ICH GCP guidelines form a compliance foundation – but AI requires a more nuanced framework. AI tools often function dynamically, learning and adapting in real time – beyond what static audit trails or role-based access systems can track.

Beyond the usual IT guidelines (which cannot be overlooked with AI/ML!) clinical teams should also ask questions such as:

• What “human-in-the-loop” touchpoints are in place to prevent data drift and ensure functionality is maintained?
• Are vendors contractually restricted from using trial data for future training?
• Who approves new AI workflows – and who audits them post-implementation?
• What protocols exist to decommission AI tools when trials conclude?

While some governing bodies are working towards guidance (such as the FDA’s draft AI guidance), teams may wish to develop their own detailed AI/ML frameworks or work with peer groups / consortiums looking to establish a common framework.

Mitigating AI Risk: What Operational Safeguards Matter Most

When assessing risk factors, the goal should be to apply structure (rather than stall innovation). Teams can empower themselves or encourage others to move forward by building safeguards into the adoption process:

• Conduct vendor diligence: Require clarity on data boundaries, model training practices, and opt-in mechanisms.
• Avoid “black box” deployments: Prioritize systems with explainable logic and traceable outcomes.
• Establish joint ownership: Align clinical, tech, and compliance teams early to define decision rights.
• Build documentation into deployment: Ensure there is visibility into when, how, and why an AI solution is integrated.

Unifora’s Perspective: Supporting Smart Technology Adoption

At Unifora, we work with sponsors, CROs, and site partners to evaluate innovative technologies – not just for potential, but for fit. We also work with technology partners on refining the experience and value – to further expand the aforementioned “fit.”

We help assess technology tools such as AI/ML in the context of study operations, data workflows, and long-term governance – so your tech decisions hold up under regulatory, operational, and ethical scrutiny.

We don’t chase trends. We design processes that scale.

In clinical research, progress should never compromise precision.

AI will continue to reshape trial operations, but the real differentiator won’t be who has the most tools - it will be who builds the right systems around them. When innovation outpaces regulation, thoughtful implementation, strong governance, and long-term adaptability become the foundation for trust and impact. 

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Looking to talk more about technology options that work for you? Schedule a free consultation with Unifora at the link below.‍