Clinical trials informed framework for real world clinical implementation and deployment of artificial intelligence...

Area of Science:

• Health Informatics
• Artificial Intelligence in Medicine
• Clinical Research Methodology

Background:

• Rapid advancements in artificial intelligence (AI) necessitate structured implementation strategies within healthcare.
• Current AI integration lacks a standardized framework, posing risks to patient safety and clinical workflows.
• Existing healthcare systems require adaptable models for incorporating novel AI technologies.

Purpose of the Study:

• To propose a novel, clinical trial-informed framework for the phased implementation of artificial intelligence in healthcare settings.
• To outline a systematic approach that prioritizes safety, efficacy, and equitable deployment of AI solutions.
• To guide healthcare organizations in developing robust roadmaps for AI integration.

Main Methods:

• Development of a four-phase implementation framework inspired by clinical trial design.
• Phase 1: Safety assessment and validation.
• Phase 2: Efficacy evaluation in controlled environments.
• Phase 3: Effectiveness assessment against current standards.
• Phase 4: Post-implementation monitoring and continuous evaluation.

Main Results:

• The proposed framework provides a structured pathway for AI implementation, moving from controlled testing to real-world application.
• Each phase addresses critical aspects of AI deployment, ensuring a progressive and evidence-based approach.
• The framework emphasizes the importance of rigorous evaluation at every stage.

Conclusions:

• A clinical trial-informed approach is crucial for the safe, effective, and equitable implementation of artificial intelligence in healthcare.
• Inter-institutional collaboration and national funding are vital for supporting advanced AI deployments.
• This framework offers a scalable model for healthcare organizations navigating AI integration.