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Clinical, Cultural, Computational, and Regulatory Considerations to Deploy AI in Radiology: Perspectives of RSNA and MICCAI Experts.
Linguraru, Marius George; Bakas, Spyridon; Aboian, Mariam; Chang, Peter D; Flanders, Adam E; Kalpathy-Cramer, Jayashree; Kitamura, Felipe C; Lungren, Matthew P; Mongan, John; Prevedello, Luciano M; Summers, Ronald M; Wu, Carol C; Adewole, Maruf; Kahn, Charles E.
Afiliação
  • Linguraru MG; 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, Departme
  • Bakas S; 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, Departme
  • Aboian M; 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, Departme
  • Chang PD; 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, Departme
  • Flanders AE; 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, Departme
  • Kalpathy-Cramer J; 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, Departme
  • Kitamura FC; 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, Departme
  • Lungren MP; 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, Departme
  • Mongan J; 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, Departme
  • Prevedello LM; 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, Departme
  • Summers RM; 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, Departme
  • Wu CC; 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, Departme
  • Adewole M; 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, Departme
  • Kahn CE; 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, Departme
Radiol Artif Intell ; 6(4): e240225, 2024 Jul.
Article em En | MEDLINE | ID: mdl-38984986
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. Keywords Adults and Pediatrics, Computer Applications-General (Informatics), Diagnosis, Prognosis © RSNA, 2024.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Radiologia / Inteligência Artificial Limite: Humans Idioma: En Revista: Radiol Artif Intell Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Radiologia / Inteligência Artificial Limite: Humans Idioma: En Revista: Radiol Artif Intell Ano de publicação: 2024 Tipo de documento: Article