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Cavallo JJ, Davis MA. Establishing robust governance of clinical artificial intelligence software - Why radiologists should lead. Clin Imaging 2024; 110:110163. [PMID: 38678765 DOI: 10.1016/j.clinimag.2024.110163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Revised: 04/12/2024] [Accepted: 04/15/2024] [Indexed: 05/01/2024]
Affiliation(s)
- Joseph J Cavallo
- Yale Department of Radiology, Yale New Haven Hospital, 330 Cedar Street, TE 2-214, New Haven, CT 06520, United States of America.
| | - Melissa A Davis
- Yale Department of Radiology, Yale New Haven Hospital, 330 Cedar Street, TE 2-214, New Haven, CT 06520, United States of America.
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Lee I, Aninos A, Lester J, Rotemberg V, Schlessinger DI, Weed J, Wongvibulsin S, Daneshjou R. Engaging industry effectively and ethically in artificial intelligence from the Augmented Artificial Intelligence Committee Standards Workgroup. J Am Acad Dermatol 2024:S0190-9622(24)00552-8. [PMID: 38691074 DOI: 10.1016/j.jaad.2024.03.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 03/02/2024] [Indexed: 05/03/2024]
Affiliation(s)
- Ivy Lee
- Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, California
| | - Arik Aninos
- American Academy of Dermatology, Rosemont, Illinois
| | - Jenna Lester
- Department of Dermatology, University of California San Francisco, San Francisco, California
| | - Veronica Rotemberg
- Dermatology Service, Memorial Sloan Kettering Cancer Center, New York, New York
| | | | - Jason Weed
- Department of Dermatology, New York University, New York, New York
| | - Shannon Wongvibulsin
- Division of Dermatology, University of California Los Angeles, Los Angeles, California
| | - Roxana Daneshjou
- Department of Dermatology, Stanford University, Palo Alto, California.
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Pham N, Hill V, Rauschecker A, Lui Y, Niogi S, Fillipi CG, Chang P, Zaharchuk G, Wintermark M. Critical Appraisal of Artificial Intelligence-Enabled Imaging Tools Using the Levels of Evidence System. AJNR Am J Neuroradiol 2023; 44:E21-E28. [PMID: 37080722 PMCID: PMC10171388 DOI: 10.3174/ajnr.a7850] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 03/16/2023] [Indexed: 04/22/2023]
Abstract
Clinical adoption of an artificial intelligence-enabled imaging tool requires critical appraisal of its life cycle from development to implementation by using a systematic, standardized, and objective approach that can verify both its technical and clinical efficacy. Toward this concerted effort, the ASFNR/ASNR Artificial Intelligence Workshop Technology Working Group is proposing a hierarchal evaluation system based on the quality, type, and amount of scientific evidence that the artificial intelligence-enabled tool can demonstrate for each component of its life cycle. The current proposal is modeled after the levels of evidence in medicine, with the uppermost level of the hierarchy showing the strongest evidence for potential impact on patient care and health care outcomes. The intended goal of establishing an evidence-based evaluation system is to encourage transparency, foster an understanding of the creation of artificial intelligence tools and the artificial intelligence decision-making process, and to report the relevant data on the efficacy of artificial intelligence tools that are developed. The proposed system is an essential step in working toward a more formalized, clinically validated, and regulated framework for the safe and effective deployment of artificial intelligence imaging applications that will be used in clinical practice.
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Affiliation(s)
- N Pham
- From the Department of Radiology (N.P., G.Z.), Stanford School of Medicine, Palo Alto, California
| | - V Hill
- Department of Radiology (V.H.), Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - A Rauschecker
- Department of Radiology (A.R.), University of California, San Francisco, San Francisco, California
| | - Y Lui
- Department of Radiology (Y.L.), NYU Grossman School of Medicine, New York, New York
| | - S Niogi
- Department of Radiology (S.N.), Weill Cornell Medicine, New York, New York
| | - C G Fillipi
- Department of Radiology (C.G.F.), Tufts University School of Medicine, Boston, Massachusetts
| | - P Chang
- Department of Radiology (P.C.), University of California, Irvine, Irvine, California
| | - G Zaharchuk
- From the Department of Radiology (N.P., G.Z.), Stanford School of Medicine, Palo Alto, California
| | - M Wintermark
- Department of Neuroradiology (M.W.), The University of Texas MD Anderson Cancer Center, Houston, Texas
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Xue H, Hu G, Hong N, Dunnick NR, Jin Z. How to keep artificial intelligence evolving in the medical imaging world? Challenges and opportunities. Sci Bull (Beijing) 2023; 68:648-652. [PMID: 36964087 DOI: 10.1016/j.scib.2023.03.031] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/26/2023]
Affiliation(s)
- Huadan Xue
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Ge Hu
- Medical Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Nan Hong
- Department of Radiology, Peking University People's Hospital, Beijing 100044, China.
| | - N Reed Dunnick
- Department of Radiology, University of Michigan, Ann Arbor 48109, USA.
| | - Zhengyu Jin
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China.
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Santomartino SM, Siegel E, Yi PH. Academic Radiology Departments Should Lead Artificial Intelligence Initiatives. Acad Radiol 2022; 30:971-974. [PMID: 35965155 DOI: 10.1016/j.acra.2022.07.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 07/13/2022] [Accepted: 07/14/2022] [Indexed: 11/26/2022]
Abstract
RATIONALE AND OBJECTIVES With a track record of innovation and unique access to digital data, radiologists are distinctly positioned to usher in a new medical era of artificial intelligence (AI). MATERIALS AND METHODS In this Perspective piece, we summarize AI initiatives that academic radiology departments should consider related to the traditional pillars of education, research, and clinical excellence, while also introducing a new opportunity for engagement with industry. RESULTS We provide early successful examples of each as well as suggestions to guide departments towards future success. CONCLUSION Our goal is to assist academic radiology leaders in bringing their departments into the AI era and realizing its full potential in our field.
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Affiliation(s)
- Samantha M Santomartino
- University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, W. Baltimore Street, First Floor, Rm. 1172, 21201 Baltimore, MD
| | - Eliot Siegel
- University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, W. Baltimore Street, First Floor, Rm. 1172, 21201 Baltimore, MD
| | - Paul H Yi
- University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, W. Baltimore Street, First Floor, Rm. 1172, 21201 Baltimore, MD; Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD.
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Artificial Intelligence (AI) for Screening Mammography, From the AI Special Series on AI Applications. AJR Am J Roentgenol 2022; 219:369-380. [PMID: 35018795 DOI: 10.2214/ajr.21.27071] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Artificial intelligence (AI) applications for screening mammography are being marketed for clinical use in the interpretative domains of lesion detection and diagnosis, triage, and breast density assessment, and in the noninterpretive domains of breast cancer risk assessment, image quality control, image acquisition, and dose reduction. Evidence in support of these nascent applications, particularly for lesion detection and diagnosis, is largely based on multireader studies with cancer-enriched datasets rather than rigorous clinical evaluation aligned with the application's specific intended clinical use. This article reviews commercial AI algorithms for screening mammography that are currently available for clinical practice, their use, and evidence supporting their performance. Clinical implementation considerations, such as workflow integration, governance, and ethical issues, are also described. In addition, the future of AI for screening mammography is discussed, including the development of interpretive and noninterpretive AI applications and strategic priorities for research and development.
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