Pitfalls of Using AI in Writing Device Clinical Trial Protocols

Artificial Intelligence (AI) systems offer new opportunities to streamline the creation of clinical trial protocols. However, when not implemented carefully, these tools can inadvertently increase the workload for research teams. Although AI systems are capable of generating text and automating specific tasks, they may also introduce errors and create a misleading perception of enhanced efficiency for researchers.

How AI can create extra work in clinical protocol development

1. More editing and validation needed: Although AI can quickly produce drafts, these outputs are not always precise or suited to the specific requirements of a clinical study protocol.  This often means human experts must spend considerable time reviewing and revising the content, which can offset any initial time savings.

2. Challenges with regulatory compliance: If AI tools are not carefully validated, they may generate protocols that fail to meet regulatory requirements, causing hold-ups during the approval process.

3. Over-reliance and insufficient oversight: Relying too heavily on AI, without adequate supervision, can result in mistakes.

4. Limitations with complex protocols: AI excels at routine tasks, but more complicated or innovative studies often require nuanced human judgment. If AI technology is not equipped to handle these complexities, additional revisions and adjustments most likely will be required.

Best Practices for Using AI in Clinical Trial Protocol Preparation

1. Define the scope clearly: Identify the aspects of protocol development that can be supported by AI and the aspects where human expertise remains crucial. Focus on automating repetitive processes with AI, such as drafting initial versions or generating standard templates.

2. Validate and review: Apply strict validation protocols to confirm the accuracy and reliability of AI-generated material, ensuring all content is reviewed and refined by human experts.

3. Encourage collaboration: Foster an environment where AI tools and researchers work together, with AI serving to enhance, and not replace, human contributions.

4. Monitor and refine: Continually assess the effectiveness of AI tools and make improvements as needed. Regularly gather feedback from users to help identify opportunities for optimization.

By adhering to these recommendations, research teams can benefit from the efficiencies of AI technology while reducing the need for additional work and minimizing errors in clinical trial protocols.