As clinical AI moves from recommending actions to taking them, teams need clear rules for when it can proceed and when a person must step in. Without those rules, human oversight can easily become a rubber stamp or a bottleneck.

The answer is rarely to require approval for everything. That would limit the value of agentic AI and create more work for already-busy clinical teams. Giving a system broad freedom without defined limits creates a different set of problems.

Effective oversight places human review where judgment, context, and accountability matter most. It also allows lower-risk, well-understood tasks to keep moving.

What Is Agentic AI in Clinical Trials?

Agentic AI can plan and complete a series of actions toward a defined goal. Instead of producing a single answer and waiting for the next prompt, it may gather information, update a workflow, route a task, contact another system, or take a follow-up action.

In clinical research, an agentic workflow could:

• Monitor incoming data and route potential issues
• Compare study records with defined requirements
• Draft and distribute routine follow-up requests
• Assemble information for site or study review
• Track unresolved actions and send reminders
• Recommend or initiate the next step in a process
• Escalate exceptions to a qualified reviewer

These capabilities can reduce repetitive coordination work. They also raise an important operational question: how much authority should the AI have at each step?

The answer should be based on the specific action, the available evidence, and the consequences of getting it wrong.

Should a Person Review Every AI Action?

Requiring human approval at every step may feel cautious, but it can weaken the workflow. Reviewers may receive so many low-value requests that they begin approving them quickly without meaningful consideration.

This creates the appearance of oversight without much practical protection. It can also make the AI-enabled process slower than the process it replaced.

Teams should instead identify the points where human involvement adds real value. Review may be especially important when an action:

• Could affect participant safety or rights
• Influences eligibility or clinical interpretation
• Changes critical study data
• Creates a regulatory or quality record
• Sends sensitive information outside an approved environment
• Cannot be easily reversed
• Depends on incomplete or conflicting evidence
• Falls outside the system’s validated use

Routine, reversible actions may require less intervention. An AI agent could send reminders, organize records, route standard tasks, or flag missing information without waiting for individual approval each time.

How Can Teams Set the Right Level of Autonomy?

Autonomy does not need to be treated as an all-or-nothing decision. A clinical AI system can operate at different levels depending on the task and risk.

A practical model may include four levels:

1. Recommend: The AI suggests an action, but a person decides whether to proceed.
2. Prepare: The AI completes the work in draft form, and a person reviews it before release.
3. Act with notification: The AI completes an approved action and informs the responsible user.
4. Act independently: The AI completes a defined, low-risk action without routine review, while its performance remains monitored.

The same system may operate at several levels within one workflow. For example, an agent could independently organize incoming documents, prepare a proposed classification for review, and require explicit approval before changing a controlled record.

Breaking the workflow into individual actions makes these decisions easier. It prevents teams from giving an entire system a single, overly broad level of authority.

Where Should Human Review Occur?

Review should happen before the action that carries meaningful risk, while the reviewer still has a chance to change the outcome. Placing approval at the end of a long automated process may be too late if earlier actions have already affected data, communications, or downstream systems.

Teams can identify these points by mapping the workflow from beginning to end. For each step, they should ask:

• What decision is being made?
• What information supports it?
• What could happen if the decision is wrong?
• Can the action be reversed?
• Does the action require professional judgment?
• Who is accountable for the outcome?
• What uncertainty should stop the workflow?

This exercise often reveals that oversight is needed at a few critical points rather than throughout the entire process.

It may also show where existing review steps add little value. Those steps can sometimes be simplified while stronger controls are placed around higher-risk decisions.

What Should Trigger an Escalation?

An AI agent needs clear conditions for asking a person to step in. These conditions should be defined before deployment and tested against realistic scenarios.

Useful escalation triggers may include:

• Missing, inconsistent, or outdated information
• Confidence below an approved threshold
• A result that conflicts with an established rule
• An unusual pattern outside expected operating conditions
• A proposed action involving sensitive or critical data
• A decision that exceeds the agent’s approved authority
• Repeated failure to complete a task
• Disagreement between systems or data sources
• A request from a user to pause or review the process

Escalation rules should be specific enough to guide the system and the reviewer. A general instruction to “ask for help when uncertain” leaves too much room for inconsistent behavior.

Teams should also decide what happens after an escalation. The workflow needs a named destination, an expected response time, and a way to prevent the task from disappearing into a queue.

Who Should Provide Human Oversight?

The right reviewer depends on the decision being made. Technical teams may understand how the system works, but they may not have the clinical, operational, quality, or regulatory expertise needed to evaluate a specific result.

Oversight responsibilities may involve:

• Investigators or clinicians for medical interpretation
• Research coordinators for site-level workflow decisions
• Data managers for data quality and review activities
• Safety professionals for potential safety signals
• Quality teams for controlled processes and compliance
• Study leaders for operational prioritization
• Privacy and security teams for data access concerns

The workflow should route each issue to someone with the appropriate knowledge and authority. Sending every exception to one central AI team can slow response times and separate decisions from the people who understand the work.

Reviewers also need adequate time and training. Assigning accountability without providing the information or capacity to act does not create meaningful oversight.

What Information Does a Reviewer Need?

A person cannot evaluate an AI recommendation effectively if the system presents only the proposed action. The reviewer needs enough context to understand what the AI found and why the issue requires attention.

The review screen may need to show:

• The proposed action
• The information used to reach it
• Relevant source records or documents
• Missing or conflicting information
• The rules or criteria applied
• The reason for escalation
• The potential downstream effect
• Available alternatives
• The time remaining to respond

The goal is to make review focused and efficient. Users should not have to search several systems or reconstruct the AI’s work before they can make a decision.

The interface should also make disagreement easy. Reviewers need a clear way to modify, reject, or pause an action without working around the system.

How Can Teams Avoid Rubber-Stamp Reviews?

Human review loses value when users approve outputs automatically. This can happen when the AI performs well most of the time, when review requests are too frequent, or when the evidence behind a recommendation is difficult to examine.

Teams can reduce this risk by:

• Limiting review to decisions that warrant attention
• Showing the most relevant evidence clearly
• Highlighting uncertainty and conflicting information
• Rotating or sampling routine reviews when appropriate
• Monitoring unusually fast or uniform approval patterns
• Asking reviewers to provide reasons for significant decisions
• Using rejected or revised outputs to improve the workflow

Review quality should be assessed as part of system performance. A high approval rate may indicate strong AI performance, but it can also signal that users are not reviewing carefully. Teams need enough context to tell the difference.

How Should Oversight Be Measured?

The number of human approvals provides little insight on its own. Organizations should evaluate whether oversight improves decisions and keeps the workflow moving.

Useful measures may include:

• Frequency and reason for escalations
• Time required to resolve escalated tasks
• Percentage of AI recommendations changed or rejected
• Types of errors identified by reviewers
• Actions reversed after completion
• Tasks delayed while waiting for approval
• Differences across studies, sites, or user groups
• Recurring issues that should change the system’s authority
• Reviewer workload and satisfaction

These measures can reveal where the system escalates too often, where it fails to ask for help, or where review requirements create unnecessary delays.

They can also help teams make better decisions about future autonomy. A task that performs reliably over time may move from approval to notification. A task with frequent corrections may require tighter limits or a different workflow.

Oversight Should Change as the System Changes

Agentic AI workflows will continue to evolve after implementation. Models improve, data sources change, users find new applications, and organizations become more comfortable with certain forms of automation.

Human oversight should evolve with them. Teams should review decision rights whenever the system, workflow, intended use, or operating environment changes.

Expanding autonomy should be a deliberate decision supported by evidence. The organization should understand how the system has performed, whether users trust it appropriately, and whether safeguards remain effective.

The same principle applies when autonomy needs to be reduced. Unexpected behavior, changing regulations, new data limitations, or shifts in workflow may justify returning an action to human approval.

Give People a Clear Role in the Workflow

Human oversight works best when people know exactly where they enter the process, what they are expected to evaluate, and what authority they have.

Agentic AI can handle more of the routine coordination that slows clinical research. People remain essential where decisions require judgment, context, professional responsibility, or consideration of consequences that the system cannot fully understand.

Clear decision rights allow both to contribute effectively. The AI can keep appropriate work moving, while qualified people focus their attention on the moments when their involvement matters most.

Continue the Conversation at SCOPE Summit Europe

Agentic AI, human oversight, clinical workflow design, and responsible implementation will be important areas of discussion at SCOPE Summit Europe. Leaders from across clinical research will explore how organizations can introduce greater automation while preserving judgment, accountability, and confidence in critical decisions.

Learn more and register here.