Clinical predictive artificial intelligence evaluation: A narrative review of trial designs and practical...

Area of Science:

• Medical Informatics
• Clinical Trial Design
• Artificial Intelligence in Medicine

Background:

• Conventional randomized controlled trials (RCTs) are inadequate for evaluating adaptive clinical artificial intelligence (AI) tools.
• AI algorithms evolve, necessitating dynamic evaluation frameworks beyond static RCT designs.

Purpose of the Study:

• To propose a paradigm shift in evaluating clinical AI, moving from traditional methods to adaptive, iterative, and context-specific assessments.
• To introduce a predictive-AI-specific framework integrating performance monitoring, clinical impact, and evidence generation for AI tools.

Main Methods:

• Narrative review of limitations in traditional AI evaluation frameworks.
• Proposal of a novel framework linking performance monitoring, clinical impact monitoring, and scientific evidence generation.
• Discussion of adaptive trial designs, causal inference, and governance protocols for AI model updates.

Main Results:

• Outlined limitations of conventional RCTs for clinical AI evaluation.
• Proposed a framework connecting AI performance, clinical impact, evidence generation, and governance.
• Recommended continuous monitoring prioritizing patient outcomes, health equity, and workflow integration.

Conclusions:

• A new evaluation paradigm is needed for clinical AI, integrating monitoring and evidence generation.
• Adaptive, iterative, and context-specific methodologies are crucial for reliable AI tools.
• Successful implementation requires clinician engagement, transparency, and education on AI capabilities and limitations.