AI Article Synopsis

  • Innovation in medical imaging using AI and machine learning requires thorough data collection and algorithm improvements, along with careful evaluation of factors like bias and trustworthiness.
  • Successfully integrating AI/ML into clinical settings is challenging and hinges on addressing issues in model design, development, regulatory compliance, and stakeholder collaboration.
  • Tackling these complexities is essential not only for overcoming current obstacles but also for unlocking new opportunities in the field of radiology.

Article Abstract

Innovation in medical imaging artificial intelligence (AI)/machine learning (ML) demands extensive data collection, algorithmic advancements, and rigorous performance assessments encompassing aspects such as generalizability, uncertainty, bias, fairness, trustworthiness, and interpretability. Achieving widespread integration of AI/ML algorithms into diverse clinical tasks will demand a steadfast commitment to overcoming issues in model design, development, and performance assessment. The complexities of AI/ML clinical translation present substantial challenges, requiring engagement with relevant stakeholders, assessment of cost-effectiveness for user and patient benefit, timely dissemination of information relevant to robust functioning throughout the AI/ML lifecycle, consideration of regulatory compliance, and feedback loops for real-world performance evidence. This commentary addresses several hurdles for the development and adoption of AI/ML technologies in medical imaging. Comprehensive attention to these underlying and often subtle factors is critical not only for tackling the challenges but also for exploring novel opportunities for the advancement of AI in radiology.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11140849PMC
http://dx.doi.org/10.1093/bjrai/ubae006DOI Listing

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