AI can produce convincing answers even when information is incomplete, ambiguous or wrong.
Clinical trials therefore need more than technically capable models.
They need defined use cases, controlled inputs, documented review and clear accountability.
Convincing Errors
AI systems can generate statements that sound plausible but are not supported by the source information.
In clinical trials, this can affect data interpretation, statistical methods, process requirements, regulatory statements, coding decisions and risk assessments.
Missing Context
AI may not understand study-specific conventions, operational history, informal decisions, sponsor expectations, protocol intent, why a deviation was accepted or which source is authoritative.
Human review is needed to place outputs in the correct context.
Data Privacy and Confidentiality
Clinical trial data may contain confidential, sensitive or personal information.
AI use therefore requires careful control of uploaded content, user access, data storage, retention, third-party processing, contractual requirements and anonymisation or pseudonymisation.
Bias and Incomplete Information
AI output depends on its training data and the information provided.
Incomplete inputs can lead to incomplete or biased conclusions.
Fitness for Purpose
Not every AI tool is suitable for every task.
The required level of control depends on the intended use, the risk of an incorrect output, whether the result affects study data, whether it influences a regulatory decision, whether the output is advisory or operational and whether the result can be independently verified.
Validation and Testing
AI-supported workflows may require defined acceptance criteria, representative test cases, error handling, documented limitations, human review steps, audit trails, change control and periodic performance checks.
Accountability
AI can support preparation, comparison and prioritisation.
It cannot take responsibility for approving a document, closing a query, changing study data, selecting a statistical method, determining compliance, declaring database-lock readiness or making a sponsor decision.
Conclusion
The value of AI in clinical trials depends not only on what the tool can do, but on how responsibly it is used.