As AI continues to transform clinical development, data management, and decision-making, one critical question emerges – how do we preserve human judgment, ethics, and accountability in AI-driven clinical trials?

This white paper, authored by Laxmiraju Kandikatla, MPharm, CQA, CSV Lead, Maxis AI, in collaboration with Branislav Radeljić, PhD (The Aula Fellowship for AI, Montreal), presents a risk-based framework for implementing human oversight in AI systems across regulated environments such as clinical research and life sciences.

It introduces actionable methods to link AI model risk with the right level of human oversight – ensuring that AI enhances efficiency and data integrity while maintaining ethical standards, patient safety, and compliance with global regulations.

Through examples inspired by clinical operations, pharmacovigilance, and patient data management, the paper highlights how Human-in-Command (HIC), Human-in-the-Loop (HITL), and Human-on-the-Loop (HOTL) models can be applied to balance automation with oversight at every level of clinical research.

What You’ll Learn in This White Paper: 

• Establish Oversight Where It Matters Most: Identify where human involvement is essential – from AI-enabled patient recruitment to risk-based monitoring and adverse event reporting.
• Safeguard Human Agency in Clinical Decisions: Understand how structured oversight reinforces investigator judgment, patient safety, and data transparency.
• Adopt a Risk-Based AI Governance Model: Learn how to apply ISO 31000 and EU AI Act principles to assess model risk and oversight intensity in GxP-regulated settings.
• Integrate Oversight into Clinical Operations: Explore examples showing how AI systems in clinical trials can function under HIC, HITL, or HOTL models for accountability and compliance.

Why It Matters:

AI is becoming a true co-pilot in clinical research, supporting site selection, patient recruitment, and data review. Yet, without defined oversight mechanisms, even advanced AI can risk bias, data misinterpretation, or non-compliance.
This white paper offers a structured, scalable governance model that ensures every AI-enabled decision supports ethical standards, patient safety, and regulatory confidence — without slowing innovation.

Who Should Read This:

• Clinical Research Leaders
• Clinical Data and AI Governance Teams
• Pharmacovigilance Experts
• Quality and Compliance Officers
• Regulatory and Ethics Committees

Get your copy to learn how your organization can embed human oversight in AI-driven clinical workflows, ensure risk-aligned governance across the trial lifecycle, and build transparent, trustworthy AI systems that advance smarter, safer clinical trials.