Clinical, Cultural, Computational, and Regulatory Considerations to Deploy AI in Radiology: Perspectives of RSNA and MICCAI Experts.

Journal: Radiology. Artificial intelligence
Published Date: Jul 1, 2024

Abstract

The Radiological Society of North of America (RSNA) and the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society have led a series of joint panels and seminars focused on the present impact and future directions of artificial intelligence (AI) in radiology. These conversations have collected viewpoints from multidisciplinary experts in radiology, medical imaging, and machine learning on the current clinical penetration of AI technology in radiology and how it is impacted by trust, reproducibility, explainability, and accountability. The collective points-both practical and philosophical-define the cultural changes for radiologists and AI scientists working together and describe the challenges ahead for AI technologies to meet broad approval. This article presents the perspectives of experts from MICCAI and RSNA on the clinical, cultural, computational, and regulatory considerations-coupled with recommended reading materials-essential to adopt AI technology successfully in radiology and, more generally, in clinical practice. The report emphasizes the importance of collaboration to improve clinical deployment, highlights the need to integrate clinical and medical imaging data, and introduces strategies to ensure smooth and incentivized integration. Adults and Pediatrics, Computer Applications-General (Informatics), Diagnosis, Prognosis © RSNA, 2024.

Authors

  • Marius George Linguraru
    Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Medical Center, Washington, DC 20010, USA; Departments of Radiology and Pediatrics, School of Medicine and Health Sciences, George Washington University, Washington, DC 20037, USA.
  • Spyridon Bakas
    Perelman School of Medicine, Philadelphia, PA, USA.
  • Mariam Aboian
    Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT.
  • Peter D Chang
    Department of Radiological Sciences and Center for Artificial Intelligence in Diagnostic Medicine, University of California Irvine, Orange, California.
  • Adam E Flanders
  • Jayashree Kalpathy-Cramer
    Department of Radiology, MGH/Harvard Medical School, Charlestown, Massachusetts.
  • Felipe C Kitamura
  • Matthew P Lungren
  • John Mongan
    From the Departments of Urology (T.C., M.U., H.C.C., M.S.) and Radiology and Biomedical Imaging (J.M., M.P.K., A.T., P.J., R.G., S.W.), University of California, San Francisco. 505 Parnassus Ave, M-391, San Francisco, CA 94143; and Division of Urology, Faculty of Medicine, King Chulalongkorn Memorial Hospital, Chulalongkorn University, The Thai Red Cross Society, Bangkok, Thailand (M.U.).
  • Luciano M Prevedello
  • Ronald M Summers
    National Institutes of Health, Clinical Center, Radiology and Imaging Sciences, 10 Center Drive, Bethesda, MD 20892, USA.
  • Carol C Wu
    University of Texas MD Anderson Cancer Center, Houston, Texas.
  • Maruf Adewole
    From the Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC (M.G.L.); Divisions of Radiology and Pediatrics, George Washington University School of Medicine and Health Sciences, Washington, DC (M.G.L.); Division of Computational Pathology, Department of Pathology & Laboratory Medicine, School of Medicine, Indiana University, Indianapolis, Ind (S.B.); Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pa (M.A.); Department of Radiological Sciences, University of California Irvine, Irvine, Calif (P.D.C.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa (A.E.F.); Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.); Department of Applied Innovation and AI, Diagnósticos da América SA (DasaInova), São Paulo, Brazil (F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São Paulo, São Paulo, Brazil (F.C.K.); Microsoft, Nuance, Burlington, Mass (M.P.L.); Department of Radiology and Biomedical Imaging and Center for Intelligent Imaging, University of California San Francisco, San Francisco, Calif (J.M.); Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio (L.M.P.); Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); Division of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston, Tex (C.C.W.); Medical Artificial Intelligence Laboratory, University of Lagos College of Medicine, Lagos, Nigeria (M.A.); and Department of Radiology, University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA 19104-6243 (C.E.K.).
  • Charles E Kahn
    Perelman School of Medicine, University of Pennsylvania, Philadelphia 19104, USA.

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