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Nosrati H, Nosrati M. Artificial Intelligence in Regenerative Medicine: Applications and Implications. Biomimetics (Basel) 2023; 8:442. [PMID: 37754193 PMCID: PMC10526210 DOI: 10.3390/biomimetics8050442] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 09/16/2023] [Accepted: 09/19/2023] [Indexed: 09/28/2023] Open
Abstract
The field of regenerative medicine is constantly advancing and aims to repair, regenerate, or substitute impaired or unhealthy tissues and organs using cutting-edge approaches such as stem cell-based therapies, gene therapy, and tissue engineering. Nevertheless, incorporating artificial intelligence (AI) technologies has opened new doors for research in this field. AI refers to the ability of machines to perform tasks that typically require human intelligence in ways such as learning the patterns in the data and applying that to the new data without being explicitly programmed. AI has the potential to improve and accelerate various aspects of regenerative medicine research and development, particularly, although not exclusively, when complex patterns are involved. This review paper provides an overview of AI in the context of regenerative medicine, discusses its potential applications with a focus on personalized medicine, and highlights the challenges and opportunities in this field.
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Affiliation(s)
- Hamed Nosrati
- Biosensor Research Center, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran
| | - Masoud Nosrati
- Department of Computer Science, Iowa State University, Ames, IA 50011, USA
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Katirai A, Yamamoto BA, Kogetsu A, Kato K. Perspectives on artificial intelligence in healthcare from a Patient and Public Involvement Panel in Japan: an exploratory study. Front Digit Health 2023; 5:1229308. [PMID: 37781456 PMCID: PMC10533983 DOI: 10.3389/fdgth.2023.1229308] [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: 05/26/2023] [Accepted: 08/28/2023] [Indexed: 10/03/2023] Open
Abstract
Patients and members of the public are the end users of healthcare, but little is known about their views on the use of artificial intelligence (AI) in healthcare, particularly in the Japanese context. This paper reports on an exploratory two-part workshop conducted with members of a Patient and Public Involvement Panel in Japan, which was designed to identify their expectations and concerns about the use of AI in healthcare broadly. 55 expectations and 52 concerns were elicited from workshop participants, who were then asked to cluster and title these expectations and concerns. Thematic content analysis was used to identify 12 major themes from this data. Participants had notable expectations around improved hospital administration, improved quality of care and patient experience, and positive changes in roles and relationships, and reductions in costs and disparities. These were counterbalanced by concerns about problematic changes to healthcare and a potential loss of autonomy, as well as risks around accountability and data management, and the possible emergence of new disparities. The findings reflect participants' expectations for AI as a possible solution for long-standing issues in healthcare, though their overall balanced view of AI mirrors findings reported in other contexts. Thus, this paper offers initial, novel insights into perspectives on AI in healthcare from the Japanese context. Moreover, the findings are used to argue for the importance of involving patient and public stakeholders in deliberation on AI in healthcare.
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Affiliation(s)
- Amelia Katirai
- Research Center on Ethical, Legal, and Social Issues, Osaka University, Suita, Japan
| | | | - Atsushi Kogetsu
- Department of Biomedical Ethics and Public Policy, Graduate School of Medicine, Osaka University, Suita, Japan
| | - Kazuto Kato
- Department of Biomedical Ethics and Public Policy, Graduate School of Medicine, Osaka University, Suita, Japan
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Katirai A. The ethics of advancing artificial intelligence in healthcare: analyzing ethical considerations for Japan's innovative AI hospital system. Front Public Health 2023; 11:1142062. [PMID: 37529426 PMCID: PMC10390248 DOI: 10.3389/fpubh.2023.1142062] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 06/27/2023] [Indexed: 08/03/2023] Open
Abstract
Public and private investments into developing digital health technologies-including artificial intelligence (AI)-are intensifying globally. Japan is a key case study given major governmental investments, in part through a Cross-Ministerial Strategic Innovation Promotion Program (SIP) for an "Innovative AI Hospital System." Yet, there has been little critical examination of the SIP Research Plan, particularly from an ethics approach. This paper reports on an analysis of the Plan to identify the extent to which it addressed ethical considerations set out in the World Health Organization's 2021 Guidance on the Ethics and Governance of Artificial Intelligence for Health. A coding framework was created based on the six ethical principles proposed in the Guidance and was used as the basis for a content analysis. 101 references to aspects of the framework were identified in the Plan, but attention to the ethical principles was found to be uneven, ranging from the strongest focus on the potential benefits of AI to healthcare professionals and patients (n = 44; Principle 2), to no consideration of the need for responsive or sustainable AI (n = 0; Principle 6). Ultimately, the findings show that the Plan reflects insufficient consideration of the ethical issues that arise from developing and implementing AI for healthcare purposes. This case study is used to argue that, given the ethical complexity of the use of digital health technologies, consideration of the full range of ethical concerns put forward by the WHO must urgently be made visible in future plans for AI in healthcare.
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Kao CL, Chien LC, Wang MC, Tang JS, Huang PC, Chuang CC, Shih CL. The development of new remote technologies in disaster medicine education: A scoping review. Front Public Health 2023; 11:1029558. [PMID: 37033011 PMCID: PMC10080133 DOI: 10.3389/fpubh.2023.1029558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Accepted: 02/27/2023] [Indexed: 04/11/2023] Open
Abstract
Background Remote teaching and online learning have significantly changed the responsiveness and accessibility after the COVID-19 pandemic. Disaster medicine (DM) has recently gained prominence as a critical issue due to the high frequency of worldwide disasters, especially in 2021. The new artificial intelligence (AI)-enhanced technologies and concepts have recently progressed in DM education. Objectives The aim of this article is to familiarize the reader with the remote technologies that have been developed and used in DM education over the past 20 years. Literature scoping reviews Mobile edge computing (MEC), unmanned aerial vehicles (UAVs)/drones, deep learning (DL), and visual reality stimulation, e.g., head-mounted display (HMD), are selected as promising and inspiring designs in DM education. Methods We performed a comprehensive review of the literature on the remote technologies applied in DM pedagogy for medical, nursing, and social work, as well as other health discipline students, e.g., paramedics. Databases including PubMed (MEDLINE), ISI Web of Science (WOS), EBSCO (EBSCO Essentials), Embase (EMB), and Scopus were used. The sourced results were recorded in a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart and followed in accordance with the PRISMA extension Scoping Review checklist. We included peer-reviewed articles, Epubs (electronic publications such as databases), and proceedings written in English. VOSviewer for related keywords extracted from review articles presented as a tabular summary to demonstrate their occurrence and connections among these DM education articles from 2000 to 2022. Results A total of 1,080 research articles on remote technologies in DM were initially reviewed. After exclusion, 64 articles were included in our review. Emergency remote teaching/learning education, remote learning, online learning/teaching, and blended learning are the most frequently used keywords. As new remote technologies used in emergencies become more advanced, DM pedagogy is facing more complex problems. Discussions Artificial intelligence-enhanced remote technologies promote learning incentives for medical undergraduate students or graduate professionals, but the efficacy of learning quality remains uncertain. More blended AI-modulating pedagogies in DM education could be increasingly important in the future. More sophisticated evaluation and assessment are needed to implement carefully considered designs for effective DM education.
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Affiliation(s)
- Chia-Lung Kao
- Department of Emergency Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Regional Emergency Medical Operations Center-Tainan Branch, Ministry of Health and Welfare, Taipei City, Taiwan
| | - Li-Chien Chien
- Department of Emergency Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Regional Emergency Medical Operations Center-Tainan Branch, Ministry of Health and Welfare, Taipei City, Taiwan
| | - Mei-Chin Wang
- Department of Nursing, Chung Hwa University of Medical Technology, Tainan, Taiwan
| | - Jing-Shia Tang
- Department of Nursing, Chung Hwa University of Medical Technology, Tainan, Taiwan
| | - Po-Chang Huang
- Department of Emergency Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Chia-Chang Chuang
- Department of Emergency Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Regional Emergency Medical Operations Center-Tainan Branch, Ministry of Health and Welfare, Taipei City, Taiwan
- *Correspondence: Chia-Chang Chuang
| | - Chung-Liang Shih
- Department of Medical Affairs, Ministry of Health and Welfare, Taipei City, Taiwan
- Department of Emergency Medicine, College of Medicine, National Taiwan University, Taipei City, Taiwan
- Chung-Liang Shih
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Okaniwa F, Yoshida H. Evaluation of Dietary Management Using Artificial Intelligence and Human Interventions: Nonrandomized Controlled Trial. JMIR Form Res 2022; 6:e30630. [PMID: 35675107 PMCID: PMC9218879 DOI: 10.2196/30630] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 09/07/2021] [Accepted: 03/17/2022] [Indexed: 11/19/2022] Open
Abstract
Background There has been an increase in personal health records with the increased use of wearable devices and smartphone apps to improve health. Traditional health promotion programs by human professionals have limitations in terms of cost and reach. Due to labor shortages and to save costs, there has been a growing emphasis in the medical field on building health guidance systems using artificial intelligence (AI). AI will replace advanced human tasks to some extent in the future. However, it is difficult to sustain behavioral change through technology alone at present. Objective This study investigates whether AI alone can effectively encourage healthy behaviors or whether human interventions are needed to achieve and sustain health-related behavioral change. We examined the effectiveness of AI and human interventions to encourage dietary management behaviors. In addition, we elucidated the conditions for maximizing the effect of AI on health improvement. We hypothesized that the combination of AI and human interventions will maximize their effectiveness. Methods We conducted a 3-month experiment by recruiting participants who were users of a smartphone diet management app. We recruited 102 participants and divided them into 3 groups. Treatment group I received text messages using the standard features of the app (AI-based text message intervention). Treatment group II received video messages from a companion, in addition to the text messages (combined text message and human video message intervention by AI). The control group used the app to keep a dietary record, but no feedback was provided (no intervention). We examine the participants’ continuity and the effects on physical indicators. Results Combined AI and video messaging (treatment group II) led to a lower dropout rate from the program compared to the control group, and the Cox proportional-hazards model estimate showed a hazard ratio (HR) of 0.078, which was statistically significant at the 5% level. Further, human intervention with AI and video messaging significantly reduced the body fat percentage (BFP) of participants after 3 months compared to the control group, and the rate of reduction was greater in the group with more individualized intervention. The AI-based text messages affected the BMI but had no significant effect on the BFP. Conclusions This experiment shows that it is challenging to sustain participants' healthy behavior with AI intervention alone. The results also suggest that even if the health information conveyed is the same, the information conveyed by humans and AI is more effective in improving health than the information sent by AI alone. The support received from the companion in the form of video messages may have promoted voluntary health behaviors. It is noteworthy that companions were competent, even though they were nonexperts. This means that person-to-person communication is crucial for health interventions.
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Affiliation(s)
- Fusae Okaniwa
- Department of Theoretical Social Security Research, National Institute of Population and Social Security Research, Tokyo, Japan
| | - Hiroshi Yoshida
- Graduate School of Economics and Management, Tohoku University, Miyagi, Japan
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Coppola F, Faggioni L, Gabelloni M, De Vietro F, Mendola V, Cattabriga A, Cocozza MA, Vara G, Piccinino A, Lo Monaco S, Pastore LV, Mottola M, Malavasi S, Bevilacqua A, Neri E, Golfieri R. Human, All Too Human? An All-Around Appraisal of the "Artificial Intelligence Revolution" in Medical Imaging. Front Psychol 2021; 12:710982. [PMID: 34650476 PMCID: PMC8505993 DOI: 10.3389/fpsyg.2021.710982] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 09/02/2021] [Indexed: 12/22/2022] Open
Abstract
Artificial intelligence (AI) has seen dramatic growth over the past decade, evolving from a niche super specialty computer application into a powerful tool which has revolutionized many areas of our professional and daily lives, and the potential of which seems to be still largely untapped. The field of medicine and medical imaging, as one of its various specialties, has gained considerable benefit from AI, including improved diagnostic accuracy and the possibility of predicting individual patient outcomes and options of more personalized treatment. It should be noted that this process can actively support the ongoing development of advanced, highly specific treatment strategies (e.g., target therapies for cancer patients) while enabling faster workflow and more efficient use of healthcare resources. The potential advantages of AI over conventional methods have made it attractive for physicians and other healthcare stakeholders, raising much interest in both the research and the industry communities. However, the fast development of AI has unveiled its potential for disrupting the work of healthcare professionals, spawning concerns among radiologists that, in the future, AI may outperform them, thus damaging their reputations or putting their jobs at risk. Furthermore, this development has raised relevant psychological, ethical, and medico-legal issues which need to be addressed for AI to be considered fully capable of patient management. The aim of this review is to provide a brief, hopefully exhaustive, overview of the state of the art of AI systems regarding medical imaging, with a special focus on how AI and the entire healthcare environment should be prepared to accomplish the goal of a more advanced human-centered world.
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Affiliation(s)
- Francesca Coppola
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
- SIRM Foundation, Italian Society of Medical and Interventional Radiology, Milan, Italy
| | - Lorenzo Faggioni
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Michela Gabelloni
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Fabrizio De Vietro
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Vincenzo Mendola
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Arrigo Cattabriga
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Maria Adriana Cocozza
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Giulio Vara
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Alberto Piccinino
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Silvia Lo Monaco
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Luigi Vincenzo Pastore
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Margherita Mottola
- Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
| | - Silvia Malavasi
- Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
| | - Alessandro Bevilacqua
- Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
| | - Emanuele Neri
- SIRM Foundation, Italian Society of Medical and Interventional Radiology, Milan, Italy
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Rita Golfieri
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
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Abstract
This document discusses the Japanese context of Society 5.0. Based on a society-centered approach, Society 5.0 seeks to take advantage of technological advances to finally solve the problems that currently threaten Japan, such as aging, birth rates and lack of competitiveness, among others. Additionally, another objective is to contribute to the progress of the country and develop the foundations for a better world, in which no individual can be excluded from the technological advances of our current society, to achieve this goal, the Sustainable Development Goals (SDG) have been developed. SDGs seek to assess the methods of use of modern technology and thus find the best strategies and tools to use it in a way that guarantees sustainability within the framework of a new society that demands constant renovations.
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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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