Bridging the Gap: From AI Success in Clinical Trials to Real-World Healthcare Implementation-A Narrative Review

Area of Science:

• Medical Informatics
• Artificial Intelligence in Healthcare
• Clinical Decision Support Systems

Background:

• Artificial intelligence (AI) demonstrates high diagnostic accuracy in controlled clinical trials, often matching or exceeding clinician performance.
• Real-world AI effectiveness is frequently reduced in diverse clinical settings due to methodological flaws, limited multicenter studies, and inadequate validation.
• Discrepancies exist between AI's controlled trial performance and its inconsistent implementation in actual clinical practice.

Purpose of the Study:

• Critically review the gap between AI's trial performance and real-world implementation.
• Synthesize methodological, ethical, and operational challenges hindering AI integration in healthcare.
• Propose a comprehensive framework to facilitate responsible AI adoption in clinical settings.

Main Methods:

• Conducted a thematic synthesis of peer-reviewed studies from PubMed, IEEE Xplore, and Scopus (2014-2024).
• Included studies focused on diagnostic, therapeutic, or operational AI applications and implementation challenges in healthcare.
• Excluded non-peer-reviewed articles and studies lacking rigorous analysis.

Main Results:

• Identified key barriers: algorithmic bias, workflow misalignment, increased clinician workload, and ethical concerns (transparency, accountability, data privacy).
• Scalability challenges include interoperability issues, insufficient methodological rigor, and inconsistent reporting standards.
• Introduced the AI Healthcare Integration Framework (AI-HIF) for responsible AI implementation.

Conclusions:

• Translating AI from controlled settings to real-world clinical practice requires a multifaceted, interdisciplinary approach.
• Future research should focus on large-scale pragmatic trials and observational studies.
• Empirical validation of the AI Healthcare Integration Framework (AI-HIF) in diverse real-world contexts is crucial.