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Haver HL, Bahl M, Doo FX, Kamel PI, Parekh VS, Jeudy J, Yi PH. Evaluation of Multimodal ChatGPT (GPT-4V) in Describing Mammography Image Features. Can Assoc Radiol J 2024:8465371241247043. [PMID: 38581353 DOI: 10.1177/08465371241247043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2024] Open
Affiliation(s)
- Hana L Haver
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Intelligent Imaging (UM2ii) Center, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Radiology, Division of Breast Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Manisha Bahl
- Department of Radiology, Division of Breast Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Florence X Doo
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Intelligent Imaging (UM2ii) Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Peter I Kamel
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Intelligent Imaging (UM2ii) Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Vishwa S Parekh
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Intelligent Imaging (UM2ii) Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jean Jeudy
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Intelligent Imaging (UM2ii) Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Paul H Yi
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Intelligent Imaging (UM2ii) Center, University of Maryland School of Medicine, Baltimore, MD, USA
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2
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McKee H, Brown MJ, Kim HHR, Doo FX, Panet H, Rockall AG, Omary RA, Hanneman K. Planetary Health and Radiology: Why We Should Care and What We Can Do. Radiology 2024; 311:e240219. [PMID: 38652030 DOI: 10.1148/radiol.240219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
Climate change adversely affects the well-being of humans and the entire planet. A planetary health framework recognizes that sustaining a healthy planet is essential to achieving individual, community, and global health. Radiology contributes to the climate crisis by generating greenhouse gas (GHG) emissions during the production and use of medical imaging equipment and supplies. To promote planetary health, strategies that mitigate and adapt to climate change in radiology are needed. Mitigation strategies to reduce GHG emissions include switching to renewable energy sources, refurbishing rather than replacing imaging scanners, and powering down unused scanners. Radiology departments must also build resiliency to the now unavoidable impacts of the climate crisis. Adaptation strategies include education, upgrading building infrastructure, and developing departmental sustainability dashboards to track progress in achieving sustainability goals. Shifting practices to catalyze these necessary changes in radiology requires a coordinated approach. This includes partnering with key stakeholders, providing effective communication, and prioritizing high-impact interventions. This article reviews the intersection of planetary health and radiology. Its goals are to emphasize why we should care about sustainability, showcase actions we can take to mitigate our impact, and prepare us to adapt to the effects of climate change. © RSNA, 2024 Supplemental material is available for this article. See also the article by Ibrahim et al in this issue. See also the article by Lenkinski and Rofsky in this issue.
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Affiliation(s)
- Hayley McKee
- From the Temerty Faculty of Medicine (H.M.) and Department of Medical Imaging (H.M., H.P., K.H.), University of Toronto, Toronto, Ontario, Canada; Department of Radiology, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada (M.J.B.); Department of Radiology, Seattle Children's Hospital, University of Washington School of Medicine, Seattle, Wash (H.H.R.K.); University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Radiology and Nuclear Medicine, University of Maryland Medical Center, Baltimore, Md (F.X.D.); Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, England (A.G.R.); Department of Radiology, Imperial College Healthcare NHS Trust, London, England (A.G.R.); Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tenn (R.A.O.); Joint Department of Medical Imaging, University Medical Imaging Toronto, Toronto, Ontario, Canada (K.H.); and Toronto General Hospital Research Institute, University Health Network, University of Toronto, 1 PMB-298, 585 University Ave, Toronto, ON, Canada M5G 2N2 (K.H.)
| | - Maura J Brown
- From the Temerty Faculty of Medicine (H.M.) and Department of Medical Imaging (H.M., H.P., K.H.), University of Toronto, Toronto, Ontario, Canada; Department of Radiology, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada (M.J.B.); Department of Radiology, Seattle Children's Hospital, University of Washington School of Medicine, Seattle, Wash (H.H.R.K.); University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Radiology and Nuclear Medicine, University of Maryland Medical Center, Baltimore, Md (F.X.D.); Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, England (A.G.R.); Department of Radiology, Imperial College Healthcare NHS Trust, London, England (A.G.R.); Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tenn (R.A.O.); Joint Department of Medical Imaging, University Medical Imaging Toronto, Toronto, Ontario, Canada (K.H.); and Toronto General Hospital Research Institute, University Health Network, University of Toronto, 1 PMB-298, 585 University Ave, Toronto, ON, Canada M5G 2N2 (K.H.)
| | - Helen H R Kim
- From the Temerty Faculty of Medicine (H.M.) and Department of Medical Imaging (H.M., H.P., K.H.), University of Toronto, Toronto, Ontario, Canada; Department of Radiology, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada (M.J.B.); Department of Radiology, Seattle Children's Hospital, University of Washington School of Medicine, Seattle, Wash (H.H.R.K.); University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Radiology and Nuclear Medicine, University of Maryland Medical Center, Baltimore, Md (F.X.D.); Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, England (A.G.R.); Department of Radiology, Imperial College Healthcare NHS Trust, London, England (A.G.R.); Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tenn (R.A.O.); Joint Department of Medical Imaging, University Medical Imaging Toronto, Toronto, Ontario, Canada (K.H.); and Toronto General Hospital Research Institute, University Health Network, University of Toronto, 1 PMB-298, 585 University Ave, Toronto, ON, Canada M5G 2N2 (K.H.)
| | - Florence X Doo
- From the Temerty Faculty of Medicine (H.M.) and Department of Medical Imaging (H.M., H.P., K.H.), University of Toronto, Toronto, Ontario, Canada; Department of Radiology, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada (M.J.B.); Department of Radiology, Seattle Children's Hospital, University of Washington School of Medicine, Seattle, Wash (H.H.R.K.); University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Radiology and Nuclear Medicine, University of Maryland Medical Center, Baltimore, Md (F.X.D.); Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, England (A.G.R.); Department of Radiology, Imperial College Healthcare NHS Trust, London, England (A.G.R.); Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tenn (R.A.O.); Joint Department of Medical Imaging, University Medical Imaging Toronto, Toronto, Ontario, Canada (K.H.); and Toronto General Hospital Research Institute, University Health Network, University of Toronto, 1 PMB-298, 585 University Ave, Toronto, ON, Canada M5G 2N2 (K.H.)
| | - Hayley Panet
- From the Temerty Faculty of Medicine (H.M.) and Department of Medical Imaging (H.M., H.P., K.H.), University of Toronto, Toronto, Ontario, Canada; Department of Radiology, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada (M.J.B.); Department of Radiology, Seattle Children's Hospital, University of Washington School of Medicine, Seattle, Wash (H.H.R.K.); University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Radiology and Nuclear Medicine, University of Maryland Medical Center, Baltimore, Md (F.X.D.); Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, England (A.G.R.); Department of Radiology, Imperial College Healthcare NHS Trust, London, England (A.G.R.); Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tenn (R.A.O.); Joint Department of Medical Imaging, University Medical Imaging Toronto, Toronto, Ontario, Canada (K.H.); and Toronto General Hospital Research Institute, University Health Network, University of Toronto, 1 PMB-298, 585 University Ave, Toronto, ON, Canada M5G 2N2 (K.H.)
| | - Andrea G Rockall
- From the Temerty Faculty of Medicine (H.M.) and Department of Medical Imaging (H.M., H.P., K.H.), University of Toronto, Toronto, Ontario, Canada; Department of Radiology, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada (M.J.B.); Department of Radiology, Seattle Children's Hospital, University of Washington School of Medicine, Seattle, Wash (H.H.R.K.); University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Radiology and Nuclear Medicine, University of Maryland Medical Center, Baltimore, Md (F.X.D.); Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, England (A.G.R.); Department of Radiology, Imperial College Healthcare NHS Trust, London, England (A.G.R.); Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tenn (R.A.O.); Joint Department of Medical Imaging, University Medical Imaging Toronto, Toronto, Ontario, Canada (K.H.); and Toronto General Hospital Research Institute, University Health Network, University of Toronto, 1 PMB-298, 585 University Ave, Toronto, ON, Canada M5G 2N2 (K.H.)
| | - Reed A Omary
- From the Temerty Faculty of Medicine (H.M.) and Department of Medical Imaging (H.M., H.P., K.H.), University of Toronto, Toronto, Ontario, Canada; Department of Radiology, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada (M.J.B.); Department of Radiology, Seattle Children's Hospital, University of Washington School of Medicine, Seattle, Wash (H.H.R.K.); University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Radiology and Nuclear Medicine, University of Maryland Medical Center, Baltimore, Md (F.X.D.); Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, England (A.G.R.); Department of Radiology, Imperial College Healthcare NHS Trust, London, England (A.G.R.); Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tenn (R.A.O.); Joint Department of Medical Imaging, University Medical Imaging Toronto, Toronto, Ontario, Canada (K.H.); and Toronto General Hospital Research Institute, University Health Network, University of Toronto, 1 PMB-298, 585 University Ave, Toronto, ON, Canada M5G 2N2 (K.H.)
| | - Kate Hanneman
- From the Temerty Faculty of Medicine (H.M.) and Department of Medical Imaging (H.M., H.P., K.H.), University of Toronto, Toronto, Ontario, Canada; Department of Radiology, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada (M.J.B.); Department of Radiology, Seattle Children's Hospital, University of Washington School of Medicine, Seattle, Wash (H.H.R.K.); University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Radiology and Nuclear Medicine, University of Maryland Medical Center, Baltimore, Md (F.X.D.); Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, England (A.G.R.); Department of Radiology, Imperial College Healthcare NHS Trust, London, England (A.G.R.); Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tenn (R.A.O.); Joint Department of Medical Imaging, University Medical Imaging Toronto, Toronto, Ontario, Canada (K.H.); and Toronto General Hospital Research Institute, University Health Network, University of Toronto, 1 PMB-298, 585 University Ave, Toronto, ON, Canada M5G 2N2 (K.H.)
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Savage CH, Park H, Kwak K, Smith AD, Rothenberg SA, Parekh VS, Doo FX, Yi PH. General-Purpose Large Language Models Versus a Domain-Specific Natural Language Processing Tool for Label Extraction From Chest Radiograph Reports. AJR Am J Roentgenol 2024; 222:e2330573. [PMID: 38230901 DOI: 10.2214/ajr.23.30573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2024]
Affiliation(s)
- Cody H Savage
- University of Maryland Medical Intelligent Imaging Center, University of Maryland School of Medicine, Baltimore, MD
| | - Hyoungsun Park
- University of Maryland Medical Intelligent Imaging Center, University of Maryland School of Medicine, Baltimore, MD
| | - Kijung Kwak
- University of Maryland Medical Intelligent Imaging Center, University of Maryland School of Medicine, Baltimore, MD
| | - Andrew D Smith
- University of Alabama at Birmingham, Heersink School of Medicine, Birmingham, AL
| | - Steven A Rothenberg
- University of Alabama at Birmingham, Heersink School of Medicine, Birmingham, AL
| | - Vishwa S Parekh
- University of Maryland Medical Intelligent Imaging Center, University of Maryland School of Medicine, Baltimore, MD
| | - Florence X Doo
- University of Maryland Medical Intelligent Imaging Center, University of Maryland School of Medicine, Baltimore, MD
| | - Paul H Yi
- University of Maryland Medical Intelligent Imaging Center, University of Maryland School of Medicine, Baltimore, MD
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Doo FX, Siegel EL. Conflicts of Interest in Radiology Publishing. J Am Coll Radiol 2024:S1546-1440(24)00303-X. [PMID: 38527640 DOI: 10.1016/j.jacr.2024.03.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 03/15/2024] [Accepted: 03/22/2024] [Indexed: 03/27/2024]
Affiliation(s)
- Florence X Doo
- University of Maryland Medical Intelligent Imaging (UM2ii) Center, University of Maryland Baltimore, Baltimore, Maryland.
| | - Eliot L Siegel
- University of Maryland Medical Intelligent Imaging (UM2ii) Center, University of Maryland Baltimore, Baltimore, Maryland. https://twitter.com/EliotSiegel
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Larson DB, Doo FX, Allen B, Mongan J, Flanders AE, Wald C. Proceedings from the 2022 ACR-RSNA Workshop on Safety, Effectiveness, Reliability, and Transparency in AI. J Am Coll Radiol 2024:S1546-1440(24)00137-6. [PMID: 38354844 DOI: 10.1016/j.jacr.2024.01.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 01/27/2024] [Accepted: 01/27/2024] [Indexed: 02/16/2024]
Abstract
Despite the surge in AI development for healthcare applications, particularly for medical imaging applications, there has been limited adoption of such AI tools into clinical practice. During a one-day workshop in November, 2022, co-organized by the American College of Radiology (ACR) and the Radiological Society of North America (RSNA), participants outlined experiences and problems with implementing AI in clinical practice, defined the needs of various stakeholders in the AI ecosystem, and elicited potential solutions and strategies related to the safety, effectiveness, reliability, and transparency of AI algorithms. Participants included radiologists from academic and community radiology practices, informatics leaders responsible for AI implementation, regulatory agency employees, and specialty society representatives. The major themes that emerged fell into two categories: 1) AI product development and 2) implementation of AI-based applications in clinical practice. In particular, participants highlighted key aspects of AI product development to include clear clinical task definitions; well-curated data from diverse geographic, economical, and healthcare settings; standards and mechanisms to monitor model reliability; and transparency regarding model performance, both in controlled and real-world settings. For implementation, participants emphasized the need for strong institutional governance; systematic evaluation, selection, and validation methods carried out by local teams; seamless integration into the clinical workflow; performance monitoring and support by local teams; performance monitoring by external entities; and alignment of incentives through credentialing and reimbursement. Participants predicted that clinical implementation of AI in radiology will continue to be limited until the safety, effectiveness, reliability, and transparency of such tools are more fully addressed.
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Affiliation(s)
- David B Larson
- Department of Radiology, Stanford University Medical Center, Stanford, CA.
| | - Florence X Doo
- University of Maryland Medical Intelligent Imaging (UM2ii) Center, Baltimore, MD. https://twitter.com/flo_doo
| | - Bibb Allen
- Department of Radiology, Grandview Medical Center, Birmingham, AL. https://twitter.com/bibballen
| | - John Mongan
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA. https://twitter.com/MonganMD
| | - Adam E Flanders
- Department of Radiology, Thomas Jefferson University, Philadelphia, PA. https://twitter.com/BFlanksteak
| | - Christoph Wald
- Department of Radiology, Lahey Hospital and Medical Center, Boston, MA. https://twitter.com/waldchristoph
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6
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Doo FX, Kulkarni P, Siegel EL, Toland M, Yi PH, Carlos RC, Parekh VS. Economic and Environmental Costs of Cloud Technologies for Medical Imaging and Radiology Artificial Intelligence. J Am Coll Radiol 2024; 21:248-256. [PMID: 38072221 DOI: 10.1016/j.jacr.2023.11.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/07/2023] [Accepted: 11/10/2023] [Indexed: 01/18/2024]
Abstract
Radiology is on the verge of a technological revolution driven by artificial intelligence (including large language models), which requires robust computing and storage capabilities, often beyond the capacity of current non-cloud-based informatics systems. The cloud presents a potential solution for radiology, and we should weigh its economic and environmental implications. Recently, cloud technologies have become a cost-effective strategy by providing necessary infrastructure while reducing expenditures associated with hardware ownership, maintenance, and upgrades. Simultaneously, given the optimized energy consumption in modern cloud data centers, this transition is expected to reduce the environmental footprint of radiologic operations. The path to cloud integration comes with its own challenges, and radiology informatics leaders must consider elements such as cloud architectural choices, pricing, data security, uptime service agreements, user training and support, and broader interoperability. With the increasing importance of data-driven tools in radiology, understanding and navigating the cloud landscape will be essential for the future of radiology and its various stakeholders.
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Affiliation(s)
- Florence X Doo
- University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Radiology and Nuclear Medicine, University of Maryland, Baltimore, Maryland.
| | - Pranav Kulkarni
- University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Radiology and Nuclear Medicine, University of Maryland, Baltimore, Maryland. https://twitter.com/itsPranavK
| | - Eliot L Siegel
- University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Radiology and Nuclear Medicine, University of Maryland, Baltimore, Maryland; Associate Vice Chair, University of Maryland, Baltimore, Maryland. https://twitter.com/EliotSiegel
| | - Michael Toland
- Senior Director of IT, Department of Diagnostic Imaging and Nuclear Medicine, University of Maryland Medical System, Baltimore, Maryland
| | - Paul H Yi
- University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Radiology and Nuclear Medicine, University of Maryland, Baltimore, Maryland. https://twitter.com/PaulYiMD
| | - Ruth C Carlos
- University of Michigan, Ann Arbor, Michigan; and Editor-in-Chief, Journal of the American College of Radiology. https://twitter.com/ruthcarlosmd
| | - Vishwa S Parekh
- University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Radiology and Nuclear Medicine, University of Maryland, Baltimore, Maryland. https://twitter.com/vishwa_parekh
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7
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Doo FX, Vosshenrich J, Cook TS, Moy L, Almeida EP, Woolen SA, Gichoya JW, Heye T, Hanneman K. Environmental Sustainability and AI in Radiology: A Double-Edged Sword. Radiology 2024; 310:e232030. [PMID: 38411520 PMCID: PMC10902597 DOI: 10.1148/radiol.232030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 10/21/2023] [Accepted: 11/17/2023] [Indexed: 02/28/2024]
Abstract
According to the World Health Organization, climate change is the single biggest health threat facing humanity. The global health care system, including medical imaging, must manage the health effects of climate change while at the same time addressing the large amount of greenhouse gas (GHG) emissions generated in the delivery of care. Data centers and computational efforts are increasingly large contributors to GHG emissions in radiology. This is due to the explosive increase in big data and artificial intelligence (AI) applications that have resulted in large energy requirements for developing and deploying AI models. However, AI also has the potential to improve environmental sustainability in medical imaging. For example, use of AI can shorten MRI scan times with accelerated acquisition times, improve the scheduling efficiency of scanners, and optimize the use of decision-support tools to reduce low-value imaging. The purpose of this Radiology in Focus article is to discuss this duality at the intersection of environmental sustainability and AI in radiology. Further discussed are strategies and opportunities to decrease AI-related emissions and to leverage AI to improve sustainability in radiology, with a focus on health equity. Co-benefits of these strategies are explored, including lower cost and improved patient outcomes. Finally, knowledge gaps and areas for future research are highlighted.
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Affiliation(s)
- Florence X. Doo
- From the University of Maryland Medical Intelligent Imaging (UM2ii)
Center, Department of Radiology and Nuclear Medicine, University of Maryland,
Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel,
Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
New York, NY (J.V., L.M.); Department of Radiology, Perelman School of Medicine
at the University of Pennsylvania, Philadelphia, Pa (T.S.C.); Joint Department
of Medical Imaging, University Health Network, Toronto, Ontario, Canada
(E.P.R.P.A., K.H.); Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, Calif (S.A.W.); Department of
Radiology and Imaging Sciences, Emory University, Atlanta, Ga (J.W.G.); Toronto
General Hospital Research Institute, University Health Network, University of
Toronto, 585 University Ave, 1 PMB-298, Toronto, ON, Cananda M5G 2N2 (K.H.); and
Department of Medical Imaging, University Medical Imaging Toronto, University of
Toronto, Toronto, Ontario, Canada (K.H.)
| | - Jan Vosshenrich
- From the University of Maryland Medical Intelligent Imaging (UM2ii)
Center, Department of Radiology and Nuclear Medicine, University of Maryland,
Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel,
Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
New York, NY (J.V., L.M.); Department of Radiology, Perelman School of Medicine
at the University of Pennsylvania, Philadelphia, Pa (T.S.C.); Joint Department
of Medical Imaging, University Health Network, Toronto, Ontario, Canada
(E.P.R.P.A., K.H.); Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, Calif (S.A.W.); Department of
Radiology and Imaging Sciences, Emory University, Atlanta, Ga (J.W.G.); Toronto
General Hospital Research Institute, University Health Network, University of
Toronto, 585 University Ave, 1 PMB-298, Toronto, ON, Cananda M5G 2N2 (K.H.); and
Department of Medical Imaging, University Medical Imaging Toronto, University of
Toronto, Toronto, Ontario, Canada (K.H.)
| | - Tessa S. Cook
- From the University of Maryland Medical Intelligent Imaging (UM2ii)
Center, Department of Radiology and Nuclear Medicine, University of Maryland,
Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel,
Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
New York, NY (J.V., L.M.); Department of Radiology, Perelman School of Medicine
at the University of Pennsylvania, Philadelphia, Pa (T.S.C.); Joint Department
of Medical Imaging, University Health Network, Toronto, Ontario, Canada
(E.P.R.P.A., K.H.); Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, Calif (S.A.W.); Department of
Radiology and Imaging Sciences, Emory University, Atlanta, Ga (J.W.G.); Toronto
General Hospital Research Institute, University Health Network, University of
Toronto, 585 University Ave, 1 PMB-298, Toronto, ON, Cananda M5G 2N2 (K.H.); and
Department of Medical Imaging, University Medical Imaging Toronto, University of
Toronto, Toronto, Ontario, Canada (K.H.)
| | - Linda Moy
- From the University of Maryland Medical Intelligent Imaging (UM2ii)
Center, Department of Radiology and Nuclear Medicine, University of Maryland,
Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel,
Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
New York, NY (J.V., L.M.); Department of Radiology, Perelman School of Medicine
at the University of Pennsylvania, Philadelphia, Pa (T.S.C.); Joint Department
of Medical Imaging, University Health Network, Toronto, Ontario, Canada
(E.P.R.P.A., K.H.); Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, Calif (S.A.W.); Department of
Radiology and Imaging Sciences, Emory University, Atlanta, Ga (J.W.G.); Toronto
General Hospital Research Institute, University Health Network, University of
Toronto, 585 University Ave, 1 PMB-298, Toronto, ON, Cananda M5G 2N2 (K.H.); and
Department of Medical Imaging, University Medical Imaging Toronto, University of
Toronto, Toronto, Ontario, Canada (K.H.)
| | - Eduardo P.R.P. Almeida
- From the University of Maryland Medical Intelligent Imaging (UM2ii)
Center, Department of Radiology and Nuclear Medicine, University of Maryland,
Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel,
Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
New York, NY (J.V., L.M.); Department of Radiology, Perelman School of Medicine
at the University of Pennsylvania, Philadelphia, Pa (T.S.C.); Joint Department
of Medical Imaging, University Health Network, Toronto, Ontario, Canada
(E.P.R.P.A., K.H.); Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, Calif (S.A.W.); Department of
Radiology and Imaging Sciences, Emory University, Atlanta, Ga (J.W.G.); Toronto
General Hospital Research Institute, University Health Network, University of
Toronto, 585 University Ave, 1 PMB-298, Toronto, ON, Cananda M5G 2N2 (K.H.); and
Department of Medical Imaging, University Medical Imaging Toronto, University of
Toronto, Toronto, Ontario, Canada (K.H.)
| | - Sean A. Woolen
- From the University of Maryland Medical Intelligent Imaging (UM2ii)
Center, Department of Radiology and Nuclear Medicine, University of Maryland,
Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel,
Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
New York, NY (J.V., L.M.); Department of Radiology, Perelman School of Medicine
at the University of Pennsylvania, Philadelphia, Pa (T.S.C.); Joint Department
of Medical Imaging, University Health Network, Toronto, Ontario, Canada
(E.P.R.P.A., K.H.); Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, Calif (S.A.W.); Department of
Radiology and Imaging Sciences, Emory University, Atlanta, Ga (J.W.G.); Toronto
General Hospital Research Institute, University Health Network, University of
Toronto, 585 University Ave, 1 PMB-298, Toronto, ON, Cananda M5G 2N2 (K.H.); and
Department of Medical Imaging, University Medical Imaging Toronto, University of
Toronto, Toronto, Ontario, Canada (K.H.)
| | - Judy Wawira Gichoya
- From the University of Maryland Medical Intelligent Imaging (UM2ii)
Center, Department of Radiology and Nuclear Medicine, University of Maryland,
Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel,
Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
New York, NY (J.V., L.M.); Department of Radiology, Perelman School of Medicine
at the University of Pennsylvania, Philadelphia, Pa (T.S.C.); Joint Department
of Medical Imaging, University Health Network, Toronto, Ontario, Canada
(E.P.R.P.A., K.H.); Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, Calif (S.A.W.); Department of
Radiology and Imaging Sciences, Emory University, Atlanta, Ga (J.W.G.); Toronto
General Hospital Research Institute, University Health Network, University of
Toronto, 585 University Ave, 1 PMB-298, Toronto, ON, Cananda M5G 2N2 (K.H.); and
Department of Medical Imaging, University Medical Imaging Toronto, University of
Toronto, Toronto, Ontario, Canada (K.H.)
| | - Tobias Heye
- From the University of Maryland Medical Intelligent Imaging (UM2ii)
Center, Department of Radiology and Nuclear Medicine, University of Maryland,
Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel,
Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
New York, NY (J.V., L.M.); Department of Radiology, Perelman School of Medicine
at the University of Pennsylvania, Philadelphia, Pa (T.S.C.); Joint Department
of Medical Imaging, University Health Network, Toronto, Ontario, Canada
(E.P.R.P.A., K.H.); Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, Calif (S.A.W.); Department of
Radiology and Imaging Sciences, Emory University, Atlanta, Ga (J.W.G.); Toronto
General Hospital Research Institute, University Health Network, University of
Toronto, 585 University Ave, 1 PMB-298, Toronto, ON, Cananda M5G 2N2 (K.H.); and
Department of Medical Imaging, University Medical Imaging Toronto, University of
Toronto, Toronto, Ontario, Canada (K.H.)
| | - Kate Hanneman
- From the University of Maryland Medical Intelligent Imaging (UM2ii)
Center, Department of Radiology and Nuclear Medicine, University of Maryland,
Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel,
Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
New York, NY (J.V., L.M.); Department of Radiology, Perelman School of Medicine
at the University of Pennsylvania, Philadelphia, Pa (T.S.C.); Joint Department
of Medical Imaging, University Health Network, Toronto, Ontario, Canada
(E.P.R.P.A., K.H.); Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, Calif (S.A.W.); Department of
Radiology and Imaging Sciences, Emory University, Atlanta, Ga (J.W.G.); Toronto
General Hospital Research Institute, University Health Network, University of
Toronto, 585 University Ave, 1 PMB-298, Toronto, ON, Cananda M5G 2N2 (K.H.); and
Department of Medical Imaging, University Medical Imaging Toronto, University of
Toronto, Toronto, Ontario, Canada (K.H.)
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8
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Doo FX, Parekh VS, Kanhere A, Savani D, Tejani AS, Sapkota A, Yi PH. Evaluation of Climate-Aware Metrics Tools for Radiology Informatics and Artificial Intelligence: Toward a Potential Radiology Ecolabel. J Am Coll Radiol 2024; 21:239-247. [PMID: 38043630 DOI: 10.1016/j.jacr.2023.11.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/10/2023] [Accepted: 11/22/2023] [Indexed: 12/05/2023]
Abstract
Radiology is a major contributor to health care's impact on climate change, in part due to its reliance on energy-intensive equipment as well as its growing technological reliance. Delivering modern patient care requires a robust informatics team to move images from the imaging equipment to the workstations and the health care system. Radiology informatics is the field that manages medical imaging IT. This involves the acquisition, storage, retrieval, and use of imaging information in health care to improve access and quality, which includes PACS, cloud services, and artificial intelligence. However, the electricity consumption of computing and the life cycle of various computer components expands the carbon footprint of health care. The authors provide a general framework to understand the environmental impact of clinical radiology informatics, which includes using the international Greenhouse Gas Protocol to draft a definition of scopes of emissions pertinent to radiology informatics, as well as exploring existing tools to measure and account for these emissions. A novel standard ecolabel for radiology informatics tools, such as the Energy Star label for consumer devices or Leadership in Energy and Environmental Design certification for buildings, should be developed to promote awareness and guide radiologists and radiology informatics leaders in making environmentally conscious decisions for their clinical practice. At this critical climate juncture, the radiology community has a unique and pressing obligation to consider our shared environmental responsibility in innovating clinical technology for patient care.
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Affiliation(s)
- Florence X Doo
- University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Radiology and Nuclear Medicine, University of Maryland, Baltimore, Maryland.
| | - Vishwa S Parekh
- University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Radiology and Nuclear Medicine, University of Maryland, Baltimore, Maryland. https://twitter.com/vishwa_parekh
| | - Adway Kanhere
- University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Radiology and Nuclear Medicine, University of Maryland, Baltimore, Maryland. https://twitter.com/AdwayKanhere
| | - Dharmam Savani
- University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Radiology and Nuclear Medicine, University of Maryland, Baltimore, Maryland
| | - Ali S Tejani
- University of Texas Southwestern Medical Center, Dallas, Texas; and Co-Chair, Resident-Fellow Section AI Subcommittee. https://twitter.com/AliTejaniMD
| | - Amir Sapkota
- Chair, Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland
| | - Paul H Yi
- University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Radiology and Nuclear Medicine, University of Maryland, Baltimore, Maryland; Vice Chair, Program Planning Committee, Society for Imaging Informatics in Medicine; and Associate Editor of Radiology: Artificial Intelligence. https://twitter.com/PaulYiMD
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9
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Doo FX, Parekh VS. Beyond the AJR: Early Applications of Generative Artificial Intelligence for Radiology Report Interpretation. AJR Am J Roentgenol 2023. [PMID: 38117099 DOI: 10.2214/ajr.23.30696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Affiliation(s)
- Florence X Doo
- University of Maryland Medical Intelligent Imaging Center, Baltimore, MD
| | - Vishwa S Parekh
- University of Maryland Medical Intelligent Imaging Center, Baltimore, MD
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10
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Dhanani Z, Doo FX, Spalluto LB, Yee J, Flores EJ, Meltzer CC, Poullos PD. Prevalence of Diversity Statements and Disability Inclusion Among Radiology Residency Program Websites. J Am Coll Radiol 2023; 20:922-927. [PMID: 37028498 DOI: 10.1016/j.jacr.2023.02.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 02/17/2023] [Accepted: 02/17/2023] [Indexed: 04/08/2023]
Abstract
INTRODUCTION Radiology has widely acknowledged the need to improve inclusion of racial, ethnic, gender, and sexual minorities, with recent discourse also underscoring the importance of disability diversity and inclusion efforts. Yet studies have shown a paucity of diversity among radiology residents, despite increasing efforts to foster diversity and inclusion. Thus, the purpose of this study is to assess radiology residency program websites' diversity statements for inclusion of race and ethnicity, gender, sexual orientation, and disability as commonly underrepresented groups. METHODS A cross-sectional, observational study of websites of all diagnostic radiology programs in the Electronic Residency Application Service directory was conducted. Program websites that met inclusion criteria were audited for presence of a diversity statement; if the statement was specific to the residency program, radiology department, or institution; and if it was presented or linked on the program or department website. All statements were evaluated for the inclusion of four diversity categories: race or ethnicity, gender, sexual orientation, and disability. RESULTS One hundred ninety-two radiology residencies were identified using Electronic Residency Application Service. Programs with missing or malfunctioning hyperlinks (n = 33) or required logins (n = 1) were excluded. One hundred fifty-eight websites met inclusion criteria for analysis. Two-thirds (n = 103; 65.1%) had a diversity statement within their residency, department, or institution, with only 28 (18%) having residency program-specific statements and 22 (14%) having department-specific statements. Of the websites with diversity statements, inclusion of gender diversity was most frequent (43.0%), followed by race or ethnicity (39.9%), sexual orientation (32.9%), and disability (25.3%). Race or ethnicity was most included in institution-level diversity statements. CONCLUSIONS Less than 20% of radiology residency websites include a diversity statement, and disability is the least-included category among the diversity statements. As radiology continues to lead diversity and inclusion efforts in health care, a more comprehensive approach with equitable representation of different groups, including those with disabilities, would foster a broader sense of belonging. This comprehensive approach can help to overcome systemic barriers and bridge gaps in disability representation.
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Affiliation(s)
- Zainub Dhanani
- Stanford University School of Medicine, Palo Alto, California; and Founder and Executive Director, Medical Students with Disability and Chronic Illness National Organization.
| | - Florence X Doo
- Chief Fellow, Body Imaging, Department of Radiology, Stanford Healthcare; Board Member, Housestaff Information Technology Enhancement Council, Stanford University, Palo Alto, California; ACR Informatics fellow 2022-2023; Inaugural Chair of the AUR ACER In-Training Committee; Member, Committee on Economics in Academic Radiology, under the ACR Commission on Economics; ABR Diagnostic Radiology Initial Certification Advisory Committee Member. https://twitter.com/flo_doo
| | - Lucy B Spalluto
- Vice Chair of Health Equity, Department of Radiology, Vanderbilt University Medical Center, Nashville, Tennessee; Co-Chair, RSNA Health Equity Committee. https://twitter.com/LBSrad
| | - Judy Yee
- Chair of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, New York, New York. https://twitter.com/JudyYeeMD
| | - Efren J Flores
- Associate Chair for Equity, Inclusion, and Community Health, Mass General Brigham Enterprise Radiology, Mass General Hospital, Boston, Massachusetts. https://twitter.com/EFloresMD
| | - Carolyn C Meltzer
- Dean, Keck School of Medicine at the University of Southern California, Los Angeles, California. https://twitter.com/DeanMeltzer
| | - Peter D Poullos
- Stanford University School of Medicine, Palo Alto, California; Founder and Cochair of the Stanford Medicine Alliance for Disability Inclusion and Equity. https://twitter.com/PetePoullos
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11
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Doo FX, McGinty GB. Building Diversity, Equity, and Inclusion Within Radiology Artificial Intelligence: Representation Matters, From Data to the Workforce. J Am Coll Radiol 2023; 20:852-856. [PMID: 37453602 DOI: 10.1016/j.jacr.2023.06.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 06/14/2023] [Indexed: 07/18/2023]
Abstract
Diversity, equity, and inclusion (DEI) is both a critical ingredient and moral imperative in shaping the future of radiology artificial intelligence (AI) for improved patient care, from design to deployment. At the design level: Potential biases and discrimination within data sets results in inaccurate radiology AI models, and there is an urgent need to purposefully embed DEI principles throughout the AI development and implementation process. At the deployment level: Diverse representation in radiology AI leadership, research, and career development is necessary to avoid worsening structural and historical health inequities. To create an inclusive and equitable AI-enabled future in healthcare, a DEI radiology AI leadership training program may be needed to cultivate a diverse and sustainable pipeline of leaders in the field.
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Affiliation(s)
- Florence X Doo
- Director of Innovation, University of Maryland Medical Intelligent Imaging Center (UM2ii), Baltimore, Maryland; Member, Committee on Economics in Academic Radiology, under the ACR Commission on Economics.
| | - Geraldine B McGinty
- Senior Associate Dean for Clinical Affairs, Professor of Clinical Radiology and Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, New York; Founder, RADEqual; Chair, International Society of Radiology Commission on Education. https://twitter.com/DrGMcGinty
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12
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Doo FX, Cook TS, Siegel EL, Joshi A, Parekh V, Elahi A, Yi PH. Exploring the Clinical Translation of Generative Models Like ChatGPT: Promise and Pitfalls in Radiology, From Patients to Population Health. J Am Coll Radiol 2023; 20:877-885. [PMID: 37467871 DOI: 10.1016/j.jacr.2023.07.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 06/22/2023] [Accepted: 07/05/2023] [Indexed: 07/21/2023]
Abstract
Generative artificial intelligence (AI) tools such as GPT-4, and the chatbot interface ChatGPT, show promise for a variety of applications in radiology and health care. However, like other AI tools, ChatGPT has limitations and potential pitfalls that must be considered before adopting it for teaching, clinical practice, and beyond. We summarize five major emerging use cases for ChatGPT and generative AI in radiology across the levels of increasing data complexity, along with pitfalls associated with each. As the use of AI in health care continues to grow, it is crucial for radiologists (and all physicians) to stay informed and ensure the safe translation of these new technologies.
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Affiliation(s)
- Florence X Doo
- Director of Innovation, University of Maryland Medical Intelligent Imaging Center (UM2ii), Baltimore, Maryland; Member, Committee on Economics in Academic Radiology, under the ACR Commission on Economics.
| | - Tessa S Cook
- Vice Chair for Practice Transformation, Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; Fellowship Director, Imaging Informatics, and Chief, 3-D and Advanced Imaging, Department of Radiology, Penn Medicine, Philadelphia, Pennsylvania; Chair, Society for Imaging Informatics in Medicine; and Vice Chair, ACR Commission on Patient- and Family-Centered Care; Chair, RAHSR Affinity Group. https://twitter.com/asset25
| | - Eliot L Siegel
- Vice Chair, Research Information Systems, University of Maryland, Baltimore, Maryland; Lead, Radiology and Nuclear Medicine Diagnostics, US Department of Veterans Affairs Veterans Integrated Services Network; Chief, Imaging, US Department of Veterans Affairs Maryland Healthcare System; Radiology AI Senior Consultant. https://twitter.com/EliotSiegel
| | - Anupam Joshi
- Oros Family Professor and Chair, Computer Science and Electrical Engineering, University of Maryland, Baltimore, Maryland; Director, University of Maryland, Baltimore County, Center for Cybersecurity; Director, CyberScholars Program; Associate Editor, IEEE Transactions on Dependable and Secure Computing
| | - Vishwa Parekh
- Technical Director, University of Maryland Medical Intelligent Imaging (UM2ii) Center, Baltimore, Maryland; Review Editor, Frontiers in Oncology. https://twitter.com/vishwa_parekh
| | - Ameena Elahi
- University of Pennsylvania, Philadelphia, Pennsylvania; Application Manager, Information Services, Penn Medicine, Philadelphia, Pennsylvania; Informatics Operations Director, RAD-AID International. https://twitter.com/AmeenaElahi
| | - Paul H Yi
- Director, University of Maryland Medical Intelligent Imaging (UM2ii) Center, Baltimore, Maryland; Vice Chair, Society of Imaging Informatics in Medicine Program Planning Committee; Associate Editor, Radiology: Artificial Intelligence. https://twitter.com/PaulYiMD
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13
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Lakhani DA, Doo FX, Chung C. Developing a Comprehensive Resident-driven Research Training Pathway: A Chief Resident's Perspective. Curr Probl Diagn Radiol 2023; 52:93-96. [PMID: 36050135 DOI: 10.1067/j.cpradiol.2022.07.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 07/09/2022] [Accepted: 07/27/2022] [Indexed: 02/05/2023]
Abstract
Wide variation exists in research training, experience, opportunities, and exposure across various radiology residency training programs, ranging from having a dedicated research track to no exposure to hypothesis driven projects. Studies conducted at different residency training programs with varied resources and National Institutes of Health funding have shown that resident-driven research initiatives and mentorship programs have the potential to improve research experience during residency training, engage more medical students in research, increase departmental peer-reviewed publications and increase peer-reviewed publications of early-career faculty physicians. In an attempt to standardize the research training during radiology residency, we propose a standardized resident-led program which institutions may adapt, as well as resources that the American Alliance of Academic Chief Residents in Radiology (A3CR2) might compile in collaboration with other national organizations to improve trainee's research experience during their radiology residency training.
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Affiliation(s)
- Dhairya A Lakhani
- Chief Resident, Department of Radiology, West Virginia University, Morgantown, WV.; American Alliance of Academic Chief Residents in Radiology (A³CR²), Association of University Radiologists; The William W. Olmsted Trainee Editorial Fellow, The Radiological Society of North America (RSNA), Oak Brook, IL.
| | - Florence X Doo
- American Alliance of Academic Chief Residents in Radiology (A³CR²), Association of University Radiologists; Chief Resident, Department of Radiology, Icahn School of Medicine at Mount Sinai West, New York, NY.; Department of Radiology, Stanford University, Stanford, CA
| | - Charlotte Chung
- American Alliance of Academic Chief Residents in Radiology (A³CR²), Association of University Radiologists; Chief Resident, Department of Radiology, Emory University, Atlanta, GA.; Department of Radiology, New York University Langone Health, New York, NY
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14
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Tailor TD, Bell S, Doo FX, Carlos RC. Repeat Annual Lung Cancer Screening After Baseline Screening Among Screen-Negative Individuals: No-Cost Coverage Is Not Enough. J Am Coll Radiol 2023; 20:29-36. [PMID: 36436778 DOI: 10.1016/j.jacr.2022.11.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 10/24/2022] [Accepted: 11/03/2022] [Indexed: 11/27/2022]
Abstract
PURPOSE Adherence to lung cancer screening (LCS) is central to effective screening. The authors evaluated the likelihood of repeat annual LCS in a national commercially insured population and associations with individual characteristics, insurance characteristics, and annual out-of-pocket cost (OOPC) burden. METHODS Using claims data from an employer-insured population (Clinformatics), individuals 55 to 80 years of age undergoing LCS between January 1, 2015, to September 30, 2019, with "negative" LCS were included. Repeat LCS was defined as low-dose chest CT occurring 10 to 15 months after the preceding LCS. Analysis was conducted over a 6-year period. Multivariable logistic regression was used to evaluate associations between repeat LCS and individual characteristics, insurance characteristics, and total OOPC incurred by the individual in the year of the index LCS, even if unrelated to LCS. RESULTS Of 14,943 individuals with negative LCS, 4,561 (30.5%) underwent repeat LCS. Likelihood of repeat LCS was decreased for men (adjusted odds ratio [aOR], 0.91; 95% confidence interval [CI], 0.86-0.97), Hispanic ethnicity (aOR, 0.82; 95% CI, 0.69-0.97), and indemnity insurance plans (aOR, 0.36; 95% CI, 0.25-0.53). Relative to New England, individuals in nearly all US geographic regions were less likely to undergo repeat LCS. Finally, individuals with total OOPC in the highest two quartiles were less likely to undergo repeat LCS (aOR, 0.85 [95% CI, 0.77-0.92] for OOPC >$1,069.02-$2,475.09 vs $0-$351.82; aOR, 0.75 [95% CI, 0.68-0.82] for OOPC >$2,475.09 vs $0-$351.82). CONCLUSIONS Although federal policies facilitate LCS without cost sharing, individuals incurring high OOPC, even when unrelated to LCS, are less likely to undergo repeat LCS. Future policy design should consider the permeative burden of OOPC across the health continuum on preventive services use.
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Affiliation(s)
- Tina D Tailor
- Department of Radiology, Duke University Medical Center; Research Director, Duke Lung Cancer Screening Program; and Fellowship Director, Cardiothoracic Radiology, Duke Radiology, Durham, North Carolina.
| | - Sarah Bell
- Department of Obstetrics and Gynecology, University of Michigan Medical Center, Ann Arbor, Michigan
| | - Florence X Doo
- Department of Radiology, Stanford Health Care, Palo Alto, California; and ACR Informatics Fellow Member, Committee on Economics in Academic Radiology, ACR Commission on Economics
| | - Ruth C Carlos
- Department of Radiology, University of Michigan Medical Center, Ann Arbor, Michigan; Chair, GE AUR Research Radiology Academic Fellowship; and Editor-in-Chief, JACR
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15
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Karandikar A, Solberg A, Fung A, Lee AY, Farooq A, Taylor AC, Oliveira A, Narayan A, Senter A, Majid A, Tong A, McGrath AL, Malik A, Brown AL, Roberts A, Fleischer A, Vettiyil B, Zigmund B, Park B, Curran B, Henry C, Jaimes C, Connolly C, Robson C, Meltzer CC, Phillips CH, Dove C, Glastonbury C, Pomeranz C, Kirsch CFE, Burgan CM, Scher C, Tomblinson C, Fuss C, Santillan C, Daye D, Brown DB, Young DJ, Kopans D, Vargas D, Martin D, Thompson D, Jordan DW, Shatzkes D, Sun D, Mastrodicasa D, Smith E, Korngold E, Dibble EH, Arleo EK, Hecht EM, Morris E, Maltin EP, Cooke EA, Schwartz ES, Lehrman E, Sodagari F, Shah F, Doo FX, Rigiroli F, Vilanilam GK, Landinez G, Kim GGY, Rahbar H, Choi H, Bandesha H, Ojeda-Fournier H, Ikuta I, Dragojevic I, Schroeder JLT, Ivanidze J, Katzen JT, Chiang J, Nguyen J, Robinson JD, Broder JC, Kemp J, Weaver JS, Conyers JM, Robbins JB, Leschied JR, Wen J, Park J, Mongan J, Perchik J, Barbero JPM, Jacob J, Ledbetter K, Macura KJ, Maturen KE, Frederick-Dyer K, Dodelzon K, Cort K, Kisling K, Babagbemi K, McGill KC, Chang KJ, Feigin K, Winsor KS, Seifert K, Patel K, Porter KK, Foley KM, Patel-Lippmann K, McIntosh LJ, Padilla L, Groner L, Harry LM, Ladd LM, Wang L, Spalluto LB, Mahesh M, Marx MV, Sugi MD, Sammer MBK, Sun M, Barkovich MJ, Miller MJ, Vella M, Davis MA, Englander MJ, Durst M, Oumano M, Wood MJ, McBee MP, Fischbein NJ, Kovalchuk N, Lall N, Eclov N, Madhuripan N, Ariaratnam NS, Vincoff NS, Kothary N, Yahyavi-Firouz-Abadi N, Brook OR, Glenn OA, Woodard PK, Mazaheri P, Rhyner P, Eby PR, Raghu P, Gerson RF, Patel R, Gutierrez RL, Gebhard R, Andreotti RF, Masum R, Woods R, Mandava S, Harrington SG, Parikh S, Chu S, Arora SS, Meyers SM, Prabhu S, Shams S, Pittman S, Patel SN, Payne S, Hetts SW, Hijaz TA, Chapman T, Loehfelm TW, Juang T, Clark TJ, Potigailo V, Shah V, Planz V, Kalia V, DeMartini W, Dillon WP, Gupta Y, Koethe Y, Hartley-Blossom Z, Wang ZJ, McGinty G, Haramati A, Allen LM, Germaine P. Radiologists staunchly support patient safety and autonomy, in opposition to the SCOTUS decision to overturn Roe v Wade. Clin Imaging 2023; 93:117-121. [PMID: 36064645 DOI: 10.1016/j.clinimag.2022.07.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 07/26/2022] [Indexed: 11/29/2022]
Affiliation(s)
| | | | - Alice Fung
- Oregon Health & Science University (OHSU), United States of America
| | - Amie Y Lee
- University of California, San Francisco, United States of America
| | | | - Amy C Taylor
- University of Virginia, Charlottesville, VA, United States of America
| | | | - Anand Narayan
- University of Wisconsin Hospitals and Clinics, Madison, WI, United States of America
| | | | | | | | | | | | | | - Anne Roberts
- University of California San Diego, United States of America
| | | | | | - Beth Zigmund
- Larner College of Medicine at University of Vermont, United States of America
| | - Brian Park
- Oregon Health & Science University (OHSU), United States of America
| | - Bruce Curran
- Virginia Commonwealth University Health System, United States of America
| | - Cameron Henry
- Vanderbilt University Medical Center, United States of America
| | - Camilo Jaimes
- Boston Children's Hospital and Harvard Medical School, United States of America
| | - Cara Connolly
- Vanderbilt University Medical Center, United States of America
| | - Caroline Robson
- Boston Children's Hospital and Harvard Medical School, United States of America
| | - Carolyn C Meltzer
- Keck School of Medicine of the University of Southern California, United States of America
| | | | - Christine Dove
- Vanderbilt University Medical Center, United States of America
| | | | | | | | | | - Courtney Scher
- Henry Ford Health, Detroit, MI, United States of America
| | | | - Cristina Fuss
- Oregon Health & Science University (OHSU), United States of America
| | | | - Dania Daye
- Massachusetts General Hospital/Harvard Medical School, United States of America
| | - Daniel B Brown
- Vanderbilt University Medical Center, United States of America
| | - Daniel J Young
- Oregon Health & Science University (OHSU), United States of America
| | | | | | - Dann Martin
- Vanderbilt University Medical Center, United States of America
| | | | - David W Jordan
- University Hospitals Cleveland Medical Center & Case Western Reserve University, United States of America
| | | | - Derek Sun
- University of California, San Francisco, United States of America
| | | | | | - Elena Korngold
- Oregon Health & Science University (OHSU), United States of America
| | - Elizabeth H Dibble
- The Warren Alpert Medical School of Brown University, United States of America
| | | | | | | | | | - Erin A Cooke
- Vanderbilt University Medical Center, United States of America
| | - Erin Simon Schwartz
- Perelman School of Medicine, University of Pennsylvania, United States of America
| | | | - Faezeh Sodagari
- Massachusetts General Hospital, Harvard Medical School, United States of America
| | - Faisal Shah
- Radiology Partners, United States of America
| | | | | | - George K Vilanilam
- Dept of Radiology, University of Arkansas for Medical Sciences, United States of America
| | - Gina Landinez
- University of California, San Francisco, United States of America
| | | | - Habib Rahbar
- University of Washington, United States of America
| | - Hailey Choi
- University of California, San Francisco, United States of America
| | | | | | - Ichiro Ikuta
- Yale University School of Medicine, Department of Radiology & Biomedical Imaging, United States of America
| | | | | | | | | | - Jason Chiang
- Ronald Reagan UCLA Medical Center, United States of America
| | - Jeffers Nguyen
- Yale University School of Medicine, Department of Radiology & Biomedical Imaging, United States of America
| | | | - Jennifer C Broder
- Lahey Hospital and Medical Center, Burlington, MA, United States of America
| | - Jennifer Kemp
- University of Colorado School of Medicine, United States of America
| | | | | | - Jessica B Robbins
- University of Wisconsin School of Medicine and Public Health, United States of America
| | | | - Jessica Wen
- Stanford University, United States of America
| | - Jocelyn Park
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, United States of America
| | | | - Jordan Perchik
- University of Alabama at Birmingham, United States of America
| | | | - Jubin Jacob
- St Lawrence Radiology, United States of America
| | | | | | | | | | | | | | - Kelly Kisling
- University of California San Diego, United States of America
| | | | | | | | | | | | - Kimberly Seifert
- Stanford University School of Medicine, United States of America
| | - Kirang Patel
- University of Texas Southwestern Medical Center, United States of America
| | - Kristin K Porter
- University of Alabama at Birmingham Hospital, United States of America
| | | | | | | | - Laura Padilla
- University of California San Diego, United States of America
| | | | - Lauren M Harry
- Indiana University School of Medicine, United States of America
| | - Lauren M Ladd
- Indiana University School of Medicine, United States of America
| | - Lisa Wang
- Oregon Health & Science University (OHSU), United States of America
| | - Lucy B Spalluto
- Vanderbilt University Medical Center, United States of America
| | - M Mahesh
- Johns Hopkins University School of Medicine, United States of America
| | | | - Mark D Sugi
- University of California, San Francisco, United States of America
| | | | - Maryellen Sun
- Mount Auburn Hospital/Harvard Medical School, Cambridge, MA, United States of America
| | | | | | - Maya Vella
- University of California, San Francisco, United States of America
| | | | | | | | - Michael Oumano
- Rhode Island Hospital (Brown University), Providence, RI, United States of America
| | - Monica J Wood
- Mount Auburn Hospital/Harvard Medical School, Cambridge, MA, United States of America
| | - Morgan P McBee
- Medical University of South Carolina, United States of America
| | | | | | - Neil Lall
- Emory University, Atlanta, GA, United States of America
| | - Neville Eclov
- Duke University, Durham, NC, United States of America
| | | | | | - Nina S Vincoff
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, United States of America
| | - Nishita Kothary
- Stanford University School of Medicine, United States of America
| | | | - Olga R Brook
- Beth Israel Deaconess Medical Center, Boston, MA, United States of America
| | - Orit A Glenn
- University of California, San Francisco, United States of America
| | - Pamela K Woodard
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States of America
| | - Parisa Mazaheri
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, United States of America
| | | | - Peter R Eby
- Virginia Mason Franciscan Health, United States of America
| | - Preethi Raghu
- University of California, San Francisco, United States of America
| | - Rachel F Gerson
- Northwest Radiologists, Inc, PS, Bellingham, WA, United States of America
| | - Rina Patel
- University of California, San Francisco, United States of America
| | | | - Robyn Gebhard
- The Ohio State University, Columbus, OH, United States of America
| | | | - Rukya Masum
- The Ohio State University, Columbus, OH, United States of America
| | - Ryan Woods
- University of Wisconsin School of Medicine and Public Health, United States of America
| | - Sabala Mandava
- Henry Ford Health, Detroit, MI, United States of America
| | | | - Samir Parikh
- Henry Ford Health, Jackson, MI, United States of America
| | - Sammy Chu
- University of Washington (Seattle, WA), United States of America
| | | | - Sandra M Meyers
- University of California San Diego, United States of America
| | - Sanjay Prabhu
- Boston Children's Hospital, United States of America
| | | | - Sarah Pittman
- Stanford University School of Medicine, United States of America
| | | | | | - Steven W Hetts
- University of California, San Francisco, United States of America
| | - Tarek A Hijaz
- Northwestern Memorial Hospital/Feinberg School of Medicine of Northwestern University, Chicago, IL, United States of America
| | - Teresa Chapman
- University of Washington (Seattle, WA), United States of America
| | - Thomas W Loehfelm
- University of California, Davis, Sacramento, CA, United States of America
| | | | | | | | - Vinil Shah
- University of California, San Francisco, United States of America
| | - Virginia Planz
- Vanderbilt University Medical Center, United States of America
| | - Vivek Kalia
- Texas Scottish Rite for Children Hospital, United States of America
| | - Wendy DeMartini
- Stanford University School of Medicine, United States of America
| | - William P Dillon
- University of California, San Francisco, United States of America
| | - Yasha Gupta
- Memorial Sloan Kettering Cancer Center, United States of America
| | - Yilun Koethe
- Oregon Health & Science University (OHSU), United States of America
| | | | - Zhen Jane Wang
- University of California, San Francisco, United States of America
| | | | - Adina Haramati
- Massachusetts General Hospital, Boston, MA, United States of America
| | - Laveil M Allen
- Vanderbilt University Medical Center, United States of America
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16
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Adleberg J, Wardeh A, Doo FX, Marinelli B, Cook TS, Mendelson DS, Kagen A. Predicting Patient Demographics From Chest Radiographs With Deep Learning. J Am Coll Radiol 2022; 19:1151-1161. [PMID: 35964688 DOI: 10.1016/j.jacr.2022.06.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 06/13/2022] [Accepted: 06/21/2022] [Indexed: 11/29/2022]
Abstract
BACKGROUND Deep learning models are increasingly informing medical decision making, for instance, in the detection of acute intracranial hemorrhage and pulmonary embolism. However, many models are trained on medical image databases that poorly represent the diversity of the patients they serve. In turn, many artificial intelligence models may not perform as well on assisting providers with important medical decisions for underrepresented populations. PURPOSE Assessment of the ability of deep learning models to classify the self-reported gender, age, self-reported ethnicity, and insurance status of an individual patient from a given chest radiograph. METHODS Models were trained and tested with 55,174 radiographs in the MIMIC Chest X-ray (MIMIC-CXR) database. External validation data came from two separate databases, one from CheXpert and another from a multihospital urban health care system after institutional review board approval. Macro-averaged area under the curve (AUC) values were used to evaluate performance of models. Code used for this study is open-source and available at https://github.com/ai-bias/cxr-bias, and pixelstopatients.com/models/demographics. RESULTS Accuracy of models to predict gender was nearly perfect, with 0.999 (95% confidence interval: 0.99-0.99) AUC on held-out test data and 0.994 (0.99-0.99) and 0.997 (0.99-0.99) on external validation data. There was high accuracy to predict age and ethnicity, ranging from 0.854 (0.80-0.91) to 0.911 (0.88-0.94) AUC, and moderate accuracy to predict insurance status, with AUC ranging from 0.705 (0.60-0.81) on held-out test data to 0.675 (0.54-0.79) on external validation data. CONCLUSIONS Deep learning models can predict the age, self-reported gender, self-reported ethnicity, and insurance status of a patient from a chest radiograph. Visualization techniques are useful to ensure deep learning models function as intended and to demonstrate anatomical regions of interest. These models can be used to ensure that training data are diverse, thereby ensuring artificial intelligence models that work on diverse populations.
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Affiliation(s)
- Jason Adleberg
- Department of Radiology, Mount Sinai Health System, New York, New York.
| | - Amr Wardeh
- Deaprtment of Radiology, Upstate University Hospital, Syracuse, New York
| | - Florence X Doo
- Department of Radiology, Mount Sinai Health System, New York, New York
| | - Brett Marinelli
- Department of Radiology, Mount Sinai Health System, New York, New York
| | - Tessa S Cook
- Director, 3D and Advanced Imaging Laboratory and Director, Center for Practice Transformation in Radiology, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - David S Mendelson
- Vice Chair, Informatics, Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Alexander Kagen
- Site Chair, Department of Radiology, Mount Sinai West and Mount Sinai St. Luke's Hospitals, Icahn School of Medicine at Mount Sinai, New York, New York
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17
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Doo FX, Kassim G, Lefton DR, Patterson S, Pham H, Belani P. Rare presentations of COVID-19: PRES-like leukoencephalopathy and carotid thrombosis. Clin Imaging 2021; 69:94-101. [PMID: 32707411 PMCID: PMC7365057 DOI: 10.1016/j.clinimag.2020.07.007] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 06/19/2020] [Accepted: 07/13/2020] [Indexed: 12/13/2022]
Abstract
Coronavirus disease 2019 (COVID-19) is a global pandemic, and it is increasingly important that physicians recognize and understand its atypical presentations. Neurological symptoms such as anosmia, altered mental status, headache, and myalgias may arise due to direct injury to the nervous system or by indirectly precipitating coagulopathies. We present the first COVID-19 related cases of carotid artery thrombosis and acute PRES-like leukoencephalopathy with multifocal hemorrhage.
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Affiliation(s)
- Florence X Doo
- Mount Sinai Health System, Diagnostic, Molecular and Interventional Radiology, 1000 10th Avenue, Radiology, 4B 25, New York 10019, NY, USA.
| | - Gassan Kassim
- Mount Sinai Health System, Internal Medicine, 1000 10th Ave, New York 10019, NY, USA
| | - Daniel R Lefton
- Mount Sinai Health System, Diagnostic, Molecular and Interventional Radiology, 1000 10th Avenue, Radiology, 4B 25, New York 10019, NY, USA
| | - Shanna Patterson
- Mount Sinai Health System, Neurology, 1000 10th Ave, New York 10019, NY, USA
| | - Hien Pham
- Mount Sinai Health System, Diagnostic, Molecular and Interventional Radiology, 1000 10th Avenue, Radiology, 4B 25, New York 10019, NY, USA
| | - Puneet Belani
- Mount Sinai Health System, Diagnostic, Molecular and Interventional Radiology, 1000 10th Avenue, Radiology, 4B 25, New York 10019, NY, USA
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