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Clunie D, Prior F, Rutherford M, Moore S, Parker W, Kondylakis H, Ludwigs C, Klenk J, Lou B, O'Sullivan LT, Marcus D, Dobes J, Gutman A, Farahani K. Summary of the National Cancer Institute 2023 Virtual Workshop on Medical Image De-identification-Part 1: Report of the MIDI Task Group - Best Practices and Recommendations, Tools for Conventional Approaches to De-identification, International Approaches to De-identification, and Industry Panel on Image De-identification. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01182-y. [PMID: 38997571 DOI: 10.1007/s10278-024-01182-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Revised: 06/16/2024] [Accepted: 06/18/2024] [Indexed: 07/14/2024]
Abstract
De-identification of medical images intended for research is a core requirement for data-sharing initiatives, particularly as the demand for data for artificial intelligence (AI) applications grows. The Center for Biomedical Informatics and Information Technology (CBIIT) of the US National Cancer Institute (NCI) convened a virtual workshop with the intent of summarizing the state of the art in de-identification technology and processes and exploring interesting aspects of the subject. This paper summarizes the highlights of the first day of the workshop, the recordings, and presentations of which are publicly available for review. The topics covered included the report of the Medical Image De-Identification Initiative (MIDI) Task Group on best practices and recommendations, tools for conventional approaches to de-identification, international approaches to de-identification, and an industry panel.
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Affiliation(s)
| | - Fred Prior
- University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | | | - Stephen Moore
- Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | | | - Haridimos Kondylakis
- Institute of Computer Science, Foundation of Research & Technology - Hellas (FORTH), Heraklion, Greece
| | | | | | - Bob Lou
- Google, Mountain View, CA, USA
| | | | | | | | | | - Keyvan Farahani
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
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2
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Clunie D, Taylor A, Bisson T, Gutman D, Xiao Y, Schwarz CG, Greve D, Gichoya J, Shih G, Kline A, Kopchick B, Farahani K. Summary of the National Cancer Institute 2023 Virtual Workshop on Medical Image De-identification-Part 2: Pathology Whole Slide Image De-identification, De-facing, the Role of AI in Image De-identification, and the NCI MIDI Datasets and Pipeline. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01183-x. [PMID: 38980626 DOI: 10.1007/s10278-024-01183-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Revised: 06/16/2024] [Accepted: 06/18/2024] [Indexed: 07/10/2024]
Abstract
De-identification of medical images intended for research is a core requirement for data sharing initiatives, particularly as the demand for data for artificial intelligence (AI) applications grows. The Center for Biomedical Informatics and Information Technology (CBIIT) of the United States National Cancer Institute (NCI) convened a two half-day virtual workshop with the intent of summarizing the state of the art in de-identification technology and processes and exploring interesting aspects of the subject. This paper summarizes the highlights of the second day of the workshop, the recordings and presentations of which are publicly available for review. The topics covered included pathology whole slide image de-identification, de-facing, the role of AI in image de-identification, and the NCI Medical Image De-Identification Initiative (MIDI) datasets and pipeline.
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Affiliation(s)
| | | | - Tom Bisson
- Charité - Universitätsmedizin Berlin, Berlin, Germany
| | | | - Ying Xiao
- Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | - George Shih
- Weill Cornell Medical College, New York, NY, USA
| | | | | | - Keyvan Farahani
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
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3
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Kondylakis H, Catalan R, Alabart SM, Barelle C, Bizopoulos P, Bobowicz M, Bona J, Fotiadis DI, Garcia T, Gomez I, Jimenez-Pastor A, Karatzanis G, Lekadir K, Kogut-Czarkowska M, Lalas A, Marias K, Marti-Bonmati L, Munuera J, Nikiforaki K, Pelissier M, Prior F, Rutherford M, Saint-Aubert L, Sakellariou Z, Seymour K, Trouillard T, Votis K, Tsiknakis M. Documenting the de-identification process of clinical and imaging data for AI for health imaging projects. Insights Imaging 2024; 15:130. [PMID: 38816658 PMCID: PMC11139818 DOI: 10.1186/s13244-024-01711-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 04/26/2024] [Indexed: 06/01/2024] Open
Abstract
Artificial intelligence (AI) is revolutionizing the field of medical imaging, holding the potential to shift medicine from a reactive "sick-care" approach to a proactive focus on healthcare and prevention. The successful development of AI in this domain relies on access to large, comprehensive, and standardized real-world datasets that accurately represent diverse populations and diseases. However, images and data are sensitive, and as such, before using them in any way the data needs to be modified to protect the privacy of the patients. This paper explores the approaches in the domain of five EU projects working on the creation of ethically compliant and GDPR-regulated European medical imaging platforms, focused on cancer-related data. It presents the individual approaches to the de-identification of imaging data, and describes the problems and the solutions adopted in each case. Further, lessons learned are provided, enabling future projects to optimally handle the problem of data de-identification. CRITICAL RELEVANCE STATEMENT: This paper presents key approaches from five flagship EU projects for the de-identification of imaging and clinical data offering valuable insights and guidelines in the domain. KEY POINTS: ΑΙ models for health imaging require access to large amounts of data. Access to large imaging datasets requires an appropriate de-identification process. This paper provides de-identification guidelines from the AI for health imaging (AI4HI) projects.
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Affiliation(s)
| | - Rocio Catalan
- La Fe University and Polytechnic Hospital, La Fe Health Research Institute, Valencia, Spain
| | | | | | - Paschalis Bizopoulos
- Centre for Research & Technology Hellas, Information Technologies Institute (CERTH-ITI), Central Directorate, Thermi, Thessaloniki, Greece
| | | | - Jonathan Bona
- University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Dimitrios I Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece
| | - Teresa Garcia
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Ignacio Gomez
- La Fe University and Polytechnic Hospital, La Fe Health Research Institute, Valencia, Spain
| | | | | | - Karim Lekadir
- Artificial Intelligence in Medicine Labm Universitat de Barcelona, Barcelona, Spain
| | | | - Antonios Lalas
- Centre for Research & Technology Hellas, Information Technologies Institute (CERTH-ITI), Central Directorate, Thermi, Thessaloniki, Greece
| | | | - Luis Marti-Bonmati
- Hospital Universitario y Politécnico La Fe, Grupo de Investigación Biomédica en Imagen IIS La Fe, Valencia, España
| | - Jose Munuera
- Quantitative Imaging Biomarkers in Medicine, Quibim, Valencia, Spain
| | | | | | - Fred Prior
- University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | | | | | - Zisis Sakellariou
- Centre for Research & Technology Hellas, Information Technologies Institute (CERTH-ITI), Central Directorate, Thermi, Thessaloniki, Greece
| | | | | | - Konstantinos Votis
- Centre for Research & Technology Hellas, Information Technologies Institute (CERTH-ITI), Central Directorate, Thermi, Thessaloniki, Greece
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Nashwan AJ, Gharib S, Alhadidi M, El-Ashry AM, Alamgir A, Al-Hassan M, Khedr MA, Dawood S, Abufarsakh B. Harnessing Artificial Intelligence: Strategies for Mental Health Nurses in Optimizing Psychiatric Patient Care. Issues Ment Health Nurs 2023; 44:1020-1034. [PMID: 37850937 DOI: 10.1080/01612840.2023.2263579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2023]
Abstract
This narrative review explores the transformative impact of Artificial Intelligence (AI) on mental health nursing, particularly in enhancing psychiatric patient care. AI technologies present new strategies for early detection, risk assessment, and improving treatment adherence in mental health. They also facilitate remote patient monitoring, bridge geographical gaps, and support clinical decision-making. The evolution of virtual mental health assistants and AI-enhanced therapeutic interventions are also discussed. These technological advancements reshape the nurse-patient interactions while ensuring personalized, efficient, and high-quality care. The review also addresses AI's ethical and responsible use in mental health nursing, emphasizing patient privacy, data security, and the balance between human interaction and AI tools. As AI applications in mental health care continue to evolve, this review encourages continued innovation while advocating for responsible implementation, thereby optimally leveraging the potential of AI in mental health nursing.
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Affiliation(s)
- Abdulqadir J Nashwan
- Nursing Department, Hamad Medical Corporation, Doha, Qatar
- Department of Public Health, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
| | - Suzan Gharib
- Nursing Department, Al-Khaldi Hospital, Amman, Jordan
| | - Majdi Alhadidi
- Psychiatric & Mental Health Nursing, Faculty of Nursing, Al-Zaytoonah University of Jordan, Amman, Jordan
| | | | | | | | | | - Shaimaa Dawood
- Faculty of Nursing, Alexandria University, Alexandria, Egypt
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Artificial intelligence in emergency radiology: A review of applications and possibilities. Diagn Interv Imaging 2023; 104:6-10. [PMID: 35933269 DOI: 10.1016/j.diii.2022.07.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 07/23/2022] [Indexed: 01/10/2023]
Abstract
Artificial intelligence (AI) applications in radiology have been rising exponentially in the last decade. Although AI has found usage in various areas of healthcare, its utilization in the emergency department (ED) as a tool for emergency radiologists shows great promise towards easing some of the challenges faced daily. There have been numerous reported studies examining the application of AI-based algorithms in identifying common ED conditions to ensure more rapid reporting and in turn quicker patient care. In addition to interpretive applications, AI assists with many of the non-interpretive tasks that are encountered every day by emergency radiologists. These include, but are not limited to, protocolling, image quality control and workflow prioritization. AI continues to face challenges such as physician uptake or costs, but is a long-term investment that shows great potential to relieve many difficulties faced by emergency radiologists and ultimately improve patient outcomes. This review sums up the current advances of AI in emergency radiology, including current diagnostic applications (interpretive) and applications that stretch beyond imaging (non-interpretive), analyzes current drawbacks of AI in emergency radiology and discusses future challenges.
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Gong B, Salehi F, Hurrell C, Patlas MN. 2021 Year in Review. Can Assoc Radiol J 2022; 73:443-445. [PMID: 35272532 DOI: 10.1177/08465371221083860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Affiliation(s)
- Bo Gong
- Department of Radiology, 8166University of British Columbia, Vancouver, BC, Canada
| | - Fateme Salehi
- Department of Radiology, McMaster University, Juravinski Hospital, Hamilton, ON, Canada
| | - Casey Hurrell
- Canadian Association of Radiologists, Ottawa, ON, Canada
| | - Michael N Patlas
- Department of Radiology, McMaster University, Hamilton ON, Canada
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Affiliation(s)
- Haksoo Ko
- Seoul National University Law School, Seoul, South Korea. .,Seoul National University AI Institute, Seoul, South Korea.
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Abstract
Trust in artificial intelligence (AI) by society and the development of trustworthy AI systems and ecosystems are critical for the progress and implementation of AI technology in medicine. With the growing use of AI in a variety of medical and imaging applications, it is more vital than ever to make these systems dependable and trustworthy. Fourteen core principles are considered in this article aiming to move the needle more closely to systems that are accurate, resilient, fair, explainable, safe, and transparent: toward trustworthy AI.
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Zeleňák K, Krajina A, Meyer L, Fiehler J, Behme D, Bulja D, Caroff J, Chotai AA, Da Ros V, Gentric JC, Hofmeister J, Kass-Hout O, Kocatürk Ö, Lynch J, Pearson E, Vukasinovic I. How to Improve the Management of Acute Ischemic Stroke by Modern Technologies, Artificial Intelligence, and New Treatment Methods. Life (Basel) 2021; 11:life11060488. [PMID: 34072071 PMCID: PMC8229281 DOI: 10.3390/life11060488] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 05/25/2021] [Accepted: 05/25/2021] [Indexed: 12/22/2022] Open
Abstract
Stroke remains one of the leading causes of death and disability in Europe. The European Stroke Action Plan (ESAP) defines four main targets for the years 2018 to 2030. The COVID-19 pandemic forced the use of innovative technologies and created pressure to improve internet networks. Moreover, 5G internet network will be helpful for the transfer and collecting of extremely big databases. Nowadays, the speed of internet connection is a limiting factor for robotic systems, which can be controlled and commanded potentially from various places in the world. Innovative technologies can be implemented for acute stroke patient management soon. Artificial intelligence (AI) and robotics are used increasingly often without the exception of medicine. Their implementation can be achieved in every level of stroke care. In this article, all steps of stroke health care processes are discussed in terms of how to improve them (including prehospital diagnosis, consultation, transfer of the patient, diagnosis, techniques of the treatment as well as rehabilitation and usage of AI). New ethical problems have also been discovered. Everything must be aligned to the concept of “time is brain”.
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Affiliation(s)
- Kamil Zeleňák
- Clinic of Radiology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 03659 Martin, Slovakia
- ESMINT Artificial Intelligence and Robotics Ad hoc Committee, ESMINT, 8008 Zurich, Switzerland; (E.A.I.R.A.h.C.); (D.B.); (D.B.); (J.C.); (A.A.C.); (V.D.R.); (J.-C.G.); (J.H.); (O.K.-H.); (Ö.K.); (J.L.); (E.P.); (I.V.)
- Correspondence: ; Tel.: +421-43-4203-990
| | - Antonín Krajina
- Department of Radiology, Charles University Faculty of Medicine and University Hospital, CZ-500 05 Hradec Králové, Czech Republic;
| | - Lukas Meyer
- Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany; (L.M.); (J.F.)
| | - Jens Fiehler
- Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany; (L.M.); (J.F.)
| | | | - Daniel Behme
- ESMINT Artificial Intelligence and Robotics Ad hoc Committee, ESMINT, 8008 Zurich, Switzerland; (E.A.I.R.A.h.C.); (D.B.); (D.B.); (J.C.); (A.A.C.); (V.D.R.); (J.-C.G.); (J.H.); (O.K.-H.); (Ö.K.); (J.L.); (E.P.); (I.V.)
- University Clinic for Neuroradiology, Medical Faculty, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany
| | - Deniz Bulja
- ESMINT Artificial Intelligence and Robotics Ad hoc Committee, ESMINT, 8008 Zurich, Switzerland; (E.A.I.R.A.h.C.); (D.B.); (D.B.); (J.C.); (A.A.C.); (V.D.R.); (J.-C.G.); (J.H.); (O.K.-H.); (Ö.K.); (J.L.); (E.P.); (I.V.)
- Diagnostic-Interventional Radiology Department, Clinic of Radiology, Clinical Center of University of Sarajevo, 71000 Sarajevo, Bosnia and Herzegovina
| | - Jildaz Caroff
- ESMINT Artificial Intelligence and Robotics Ad hoc Committee, ESMINT, 8008 Zurich, Switzerland; (E.A.I.R.A.h.C.); (D.B.); (D.B.); (J.C.); (A.A.C.); (V.D.R.); (J.-C.G.); (J.H.); (O.K.-H.); (Ö.K.); (J.L.); (E.P.); (I.V.)
- Department of Interventional Neuroradiology–NEURI Brain Vascular Center, Bicêtre Hospital, APHP, 94270 Paris, France
| | - Amar Ajay Chotai
- ESMINT Artificial Intelligence and Robotics Ad hoc Committee, ESMINT, 8008 Zurich, Switzerland; (E.A.I.R.A.h.C.); (D.B.); (D.B.); (J.C.); (A.A.C.); (V.D.R.); (J.-C.G.); (J.H.); (O.K.-H.); (Ö.K.); (J.L.); (E.P.); (I.V.)
- Department of Neuroradiology, Royal Victoria Infirmary, Newcastle upon Tyne NE14LP, UK
| | - Valerio Da Ros
- ESMINT Artificial Intelligence and Robotics Ad hoc Committee, ESMINT, 8008 Zurich, Switzerland; (E.A.I.R.A.h.C.); (D.B.); (D.B.); (J.C.); (A.A.C.); (V.D.R.); (J.-C.G.); (J.H.); (O.K.-H.); (Ö.K.); (J.L.); (E.P.); (I.V.)
- Department of Biomedicine and Prevention, University Hospital of Rome “Tor Vergata”, 00133 Rome, Italy
| | - Jean-Christophe Gentric
- ESMINT Artificial Intelligence and Robotics Ad hoc Committee, ESMINT, 8008 Zurich, Switzerland; (E.A.I.R.A.h.C.); (D.B.); (D.B.); (J.C.); (A.A.C.); (V.D.R.); (J.-C.G.); (J.H.); (O.K.-H.); (Ö.K.); (J.L.); (E.P.); (I.V.)
- Interventional Neuroradiology Unit, Hôpital de la Cavale Blanche, 29200 Brest, France
| | - Jeremy Hofmeister
- ESMINT Artificial Intelligence and Robotics Ad hoc Committee, ESMINT, 8008 Zurich, Switzerland; (E.A.I.R.A.h.C.); (D.B.); (D.B.); (J.C.); (A.A.C.); (V.D.R.); (J.-C.G.); (J.H.); (O.K.-H.); (Ö.K.); (J.L.); (E.P.); (I.V.)
- Unité de Neuroradiologie Interventionnelle, Service de Neuroradiologie Diagnostique et Interventionnelle, 1205 Genève, Switzerland
| | - Omar Kass-Hout
- ESMINT Artificial Intelligence and Robotics Ad hoc Committee, ESMINT, 8008 Zurich, Switzerland; (E.A.I.R.A.h.C.); (D.B.); (D.B.); (J.C.); (A.A.C.); (V.D.R.); (J.-C.G.); (J.H.); (O.K.-H.); (Ö.K.); (J.L.); (E.P.); (I.V.)
- Stroke and Neuroendovascular Surgery, Rex Hospital, University of North Carolina, 4207 Lake Boone Trail, Suite 220, Raleigh, NC 27607, USA
| | - Özcan Kocatürk
- ESMINT Artificial Intelligence and Robotics Ad hoc Committee, ESMINT, 8008 Zurich, Switzerland; (E.A.I.R.A.h.C.); (D.B.); (D.B.); (J.C.); (A.A.C.); (V.D.R.); (J.-C.G.); (J.H.); (O.K.-H.); (Ö.K.); (J.L.); (E.P.); (I.V.)
- Balikesir Atatürk City Hospital, Gaziosmanpaşa Mahallesi 209., Sok. No: 26, 10100 Altıeylül/Balıkesir, Turkey
| | - Jeremy Lynch
- ESMINT Artificial Intelligence and Robotics Ad hoc Committee, ESMINT, 8008 Zurich, Switzerland; (E.A.I.R.A.h.C.); (D.B.); (D.B.); (J.C.); (A.A.C.); (V.D.R.); (J.-C.G.); (J.H.); (O.K.-H.); (Ö.K.); (J.L.); (E.P.); (I.V.)
- Department of Neuroradiology, Toronto Western Hospital, Toronto, ON M5T 2S8, Canada
| | - Ernesto Pearson
- ESMINT Artificial Intelligence and Robotics Ad hoc Committee, ESMINT, 8008 Zurich, Switzerland; (E.A.I.R.A.h.C.); (D.B.); (D.B.); (J.C.); (A.A.C.); (V.D.R.); (J.-C.G.); (J.H.); (O.K.-H.); (Ö.K.); (J.L.); (E.P.); (I.V.)
- CH Bergerac-Centre Hospitalier, Samuel Pozzi 9 Boulevard du Professeur Albert Calmette, 24100 Bergerac, France
| | - Ivan Vukasinovic
- ESMINT Artificial Intelligence and Robotics Ad hoc Committee, ESMINT, 8008 Zurich, Switzerland; (E.A.I.R.A.h.C.); (D.B.); (D.B.); (J.C.); (A.A.C.); (V.D.R.); (J.-C.G.); (J.H.); (O.K.-H.); (Ö.K.); (J.L.); (E.P.); (I.V.)
- Department of Neuroradiology, University Clinical Center of Serbia, 11000 Belgrade, Serbia
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Abstract
The past year has been one of unprecedented challenge for the modern world and especially the medical profession. This review explores some of the most impactful topics published in the CARJ during the COVID-19 pandemic including physician wellbeing and burnout, patient safety, and technological innovations including dual energy CT, quantitative imaging and ultra-high frequency ultrasound. The impact of the COVID-19 pandemic on trainee education is discussed and evidence-based tips for providing value-added care are reviewed. Patient privacy considerations relevant to the development of artificial intelligence applications for medical imaging are explored. These publications in the CARJ demonstrate that although this year has brought adversity, it has also been a harbinger for new and exciting areas of focus in our field.
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Affiliation(s)
- Caitlin J Ward
- Department of Diagnostic Imaging, Hamilton Health Sciences, Hamilton, Ontario, Canada.,McMaster University, Hamilton, Ontario, Canada
| | - Christian B van der Pol
- Department of Diagnostic Imaging, Hamilton Health Sciences, Hamilton, Ontario, Canada.,McMaster University, Hamilton, Ontario, Canada
| | - Michael N Patlas
- Department of Diagnostic Imaging, Hamilton Health Sciences, Hamilton, Ontario, Canada.,McMaster University, Hamilton, Ontario, Canada
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11
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Yao L, Zhang H, Zhang M, Chen X, Zhang J, Huang J, Zhang L. Application of artificial intelligence in renal disease. CLINICAL EHEALTH 2021. [DOI: 10.1016/j.ceh.2021.11.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
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