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Randomised controlled trials evaluating artificial intelligence in clinical practice: a scoping review.

Ryan Han1, Julián N Acosta2, Zahra Shakeri3

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Artificial intelligence (AI) shows growing use in clinical practice, with many trials reporting positive diagnostic results. However, concerns about generalizability and the need for more robust research remain.

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Area of Science:

  • Clinical Medicine
  • Artificial Intelligence
  • Health Informatics

Background:

  • Interest in artificial intelligence (AI) in clinical practice is rapidly expanding across various medical specialties and geographical regions.
  • The USA and China are at the forefront of research, with a significant focus on deep learning for medical imaging analysis.

Purpose of the Study:

  • To conduct a scoping review of randomized controlled trials (RCTs) evaluating the application of AI in clinical practice.
  • To identify trends, leading countries, AI technologies, and clinical areas involved in AI research.
  • To assess the reported outcomes and identify limitations in current AI research for clinical settings.

Main Methods:

  • Systematic search for randomized controlled trials (RCTs) on artificial intelligence in clinical practice.
  • Data extraction on study characteristics, AI applications (e.g., deep learning, medical imaging), clinical specialties, and reported outcomes.
  • Analysis of trial reporting standards, including demographics and operational efficiency.

Main Results:

  • A growing number of RCTs investigate AI in clinical practice, with deep learning for medical imaging (gastroenterology, radiology) being prominent.
  • Most trials (81%) reported positive primary endpoints, mainly concerning diagnostic yield or performance.
  • Concerns exist regarding generalizability due to single-center designs, limited demographic data, and variable reporting of operational efficiency.

Conclusions:

  • While AI shows promise in improving diagnostic capabilities in healthcare, current research has limitations.
  • Publication bias, lack of multicenter trials, and insufficient reporting of patient-relevant outcomes hinder the full understanding of AI's impact.
  • Future AI research must prioritize multicenter designs, diverse outcome measures, and standardized reporting to ensure clinical utility and patient benefit.