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FAIR Convergence Matrix:Optimizing the Reuse of Existing FAIR-Related Resources

data Intelligence 2020年第1期2卷 158-170,313页

作者： Hana Pergl Sustkova Kristina Maria Hettne Peter Wittenburg Annika Jacobsen Tobias Kuhn Robert Pergl Jan Slifka Peter McQuilton Barbara Magagna Susanna-Assunta Sansone Markus Stocker Melanie Imming Larry Lannom Mark Musen Erik Schultes GO FAIR International Support and Coordination Office LeidenThe Netherlands Centre for Digital Scholarship Leiden University LibrariesLeidenThe Netherlands Max Planck Computing and Data Facility Gießenbachstraße 285748 GarchingGermany Leiden University Medical Center Leiden2333 ZAThe Netherlands Department of Computer Science Vrije Universiteit AmsterdamDe Boelelaan 11051081 HV AmsterdamThe Netherlands Czech Technical University in Prague Faculty of Information Technology(FIT CTU)16000 Prague 6Czech Republic Oxford e-Research Centre Department of Engineering SciencesUniversity of OxfordOxford OX13PJUK Environment Agency Austria A-1090 ViennaAustria TIB Leibniz Information Centre for Science and Technology HannoverGermany SURF Utrecht 3511 EPThe Netherlands Corporation for National Research Initiatives(CNRI) RestonVirginia 20191USA Stanford Center for Biomedical Informatics Research StanfordCA 94305USA

The FAIR principles articulate the behaviors expected from digital artifacts that are Findable,Accessible,Interoperable and Reusable by machines and by *** by now widely accepted,the FAIR Principles by design do not explicitly consider actual implementation choices enabling FAIR *** different communities have their own,often well-established implementation preferences and priorities for data reuse,coordinating a broadly accepted,widely used FAIR implementation approach remains a global *** an effort to accelerate broad community convergence on FAIR implementation options,the GO FAIR community has launched the development of the FAIR Convergence *** Matrix is a platform that compiles for any community of practice,an inventory of their self-declared FAIR implementation choices and *** Convergence Matrix is itself a FAIR resource,openly available,and encourages voluntary participation by any self-identified community of practice(not only the GO FAIR Implementation Networks).Based on patterns of use and reuse of existing resources,the Convergence Matrix supports the transparent derivation of strategies that optimally coordinate convergence on standards and technologies in the emerging Internet of FAIR data and Services.

关键词： FAIR Implementation Choices and Challenges Convergence FAIR Communities

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Integrating the pressure-state-response model with the extension catastrophe progression for flood risk and resilience assessment

Environmental and Sustainability Indicators

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Environmental and Sustainability Indicators 2025年 27卷

作者： Shahab S. Band Sultan Noman Qasem Sajad Eslami Zulkefli Mansor Brij B. Gupta Hao-Ting Pai Meghdad Biyari Amir Mosavi Farzaneh Sajedi Hosseini Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 11432 Saudi Arabia School of Business Stevens Institute of Technology Hoboken 07030 NJ USA Research Center for Software Technology and Management Faculty of Information Science and Technology Universiti Kebangsaan Malaysia 43600 Bandar Baru Bangi Selangor Darul Ehsan Malaysia Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan School of Cybersecurity Korea University Seoul South Korea Symbiosis Centre for Information Technology Symbiosis International University Pune India Department of Big Data Business Analytics National Pingtung University Pingtung Taiwan Department of Information Management National Yunlin University of Science and Technology Yunlin Taiwan John von Neumann Faculty of Informatics Obuda University 1034 Budapest Hungary Ludovika University of Public Service Budapest Hungary Abylkas Saginov Karaganda Technical University Karaganda Kazakhstan Reclamation of Arid and Mountainous Regions Department Faculty of Natural Resources University of Tehran Karaj Iran

Despite the devastating effects of floods, the concept of resilience is still not fully considered in the assessment and management of flood risk. To study how resilience can lower the risk of floods and further enhance disaster response, this research aims to close this knowledge gap. With a focus on the Kashkan watershed in Iran, the study combines the extended catastrophe progression method with the pressure-state-response model. Three catastrophe models, namely the cusp, swallowtail, and butterfly, are applied. According to the findings, southern regions, i.e., Pol-Dokhtar city, have the highest risk of floods and the lowest resilience. Resilience and flood risk have a complementary relationship, according to the analysis, and resilience is a helpful metric for risk assessment. The results emphasize the necessity to incorporate resilience-focused pre-disruption planning and post-disaster recovery into flood risk management strategy. This work offers a foundation to incorporate resilience into future flood policies and strategies.

关键词： Flood Risk Resilience Pressure-state-response model Extension catastrophe progression method

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ASHP and ASHP Foundation Pharmacy Forecast 2025: Strategic Planning Guidance for Pharmacy Departments in Hospitals and Health Systems

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AMERICAN JOURNAL OF HEALTH-SYSTEM PHARMACY 2024年第2期82卷 17-47页

作者： DiPiro, Joseph T. Hoffman, James M. Tichy, Eric Shea, Suzanne Sanborn, Michael Hung, Anna Fox, Erin R. Watanabe, Jonathan H. Torrise, Virginia Abourjaily, Paul Cunningham, Francesca E. Schweitzer, Pamela Nelson, Scott D. Stump, Lisa S. Castro, Harvey Nesbit, Todd W. Scott, Christopher M. Professor Emeritus School of Pharmacy Virginia Commonwealth University Richmond VA USA Senior Vice President – Quality and Safety and Member Department of Pharmacy and Pharmaceutical Sciences St. Jude Children’s Research Hospital Memphis TN USA Division Chair Mayo Clinic Rochester MN and Associate Professor Mayo Clinic College of Medicine and Science Rochester MN USA Vice President System Pharmacy and Clinical Nutrition Sharp HealthCare San Diego CA USA Chief Growth Officer Baylor Scott & White Health Dallas TX USA Assistant Professor Duke University School of Medicine Durham NC Core Faculty Duke Institute for Health Policy Durham NC Research Health Scientist Durham VA Healthcare System Durham NC USA Associate Chief Pharmacy Officer Shared Services University of Utah Health Salt Lake City UT Professor (Adjunct) Department of Pharmacotherapy University of Utah College of Pharmacy Salt Lake City UT USA Associate Dean of Assessment and Quality and Director Center for Data-Driven Drugs Research and Policy University of California Irvine School of Pharmacy & Pharmaceutical Sciences Irvine CA USA Deputy Chief Consultant Pharmacy Benefits Management Services Department of Veterans Affairs Washington DC USA Senior Clinical Pharmacy Specialist Tufts Medical Center Boston MA Associate Professor Tufts University School of Medicine Boston MA USA Director VA Center for Medication Safety and Associate Chief Consultant PBM Department of Veterans Affairs Hines IL USA Former Assistant Surgeon General Chief Pharmacist Officer US Public Health Service Commissioned Corps Windsor Mill MD USA Associate Professor Department of Biomedical Informatics Vanderbilt University Medical Center Nashville TN USA Chief Digital Information Officer of the Mount Sinai Health System and Dean for Information Technology (IT) of the Icahn School of Medicine at Mount Sinai New York NY USA Physician & Healthcare Consultant Chief Medical AI Officer Helpp.ai Dallas TX U

Purpose: The 2025 ASHP Pharmacy Forecast report is written to identify and contextualize emerging trends that will influence healthcare, health systems, and the pharmacy profession. It provides recommendations to inform strategic planning that should prompt action by pharmacists and health-system leaders. Methods: Drawing on the "wisdom of crowds" concept, a survey was constructed by an advisory committee with 6 themes, each with 7 focused statements and a seventh theme on preparedness (54 survey items in total). A survey panel of 321 pharmacy leaders was purposefully designed to capture opinions from a wide range of pharmacists across the United States. Panelists were asked to consider the likelihood of the statements occurring in the next 5 years as being likely, somewhat likely, somewhat unlikely, or very unlikely to occur. Forecast panelists also assessed health systems' preparedness (from very unprepared to very prepared) for 12 of the statements. Results: The 6 survey themes identified were The Business of Healthcare: Challenges in Health-System Pharmacy, Managing Ultra-High-Cost Drugs, Addressing the Gap in Primary Care, Whole Person Health, Navigating Generative Artificial Intelligence: Opportunity and Risk, and Stabilizing the Pharmacy Workforce. The survey was completed by 269 respondents, yielding an 84% response rate. Descriptive statistics of the survey results were provided to chapter authors to inform commentary and strategic recommendations for each theme. Conclusion: The Pharmacy Forecast provides insights on large-scale, long-term trends that will influence healthcare and the pharmacy profession over the years ahead. Insight from the report can stimulate strategic thinking and provides a starting point to proactively position leaders, their teams, and departments for potential future trends.

关键词： artificial intelligence cell and gene therapy forecasting pharmacy workforce primary care strategic planning whole person health

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Clinical and Biological Effects of Combined CD27 and CD20 Antibody Therapy in Relapsed/Refractory B-Cell Lymphoma: The Riva Trial

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BLOOD 2021年第Sup1期138卷 715-715页

作者： Lim, Sean H. Sow, Heng Sheng Wignall, Christopher Mercer, Katy Caddy, Josh Boxall, Cherish Thorne, Kerensa Northey, Joshua Stanton, Louise Konn, Zoe Fines, Keira Medd, Patrick G. Coleman, Adam Nunn, Lorna Lown, Robert McKay, Pamela Osborne, Wendy Linton, Kim Collins, Graham P. Gentles, Andrew J. Rose-Zerilli, Matthew Griffiths, Gareth Antibody and Vaccine Group Centre for Cancer Immunology School of Cancer Sciences Faculty of Medicine University of Southampton Southampton General Hospital Southampton United Kingdom Centre for Cancer Immunology University of Southampton Southampton United Kingdom Southampton Cancer Research UK Centre Southampton United Kingdom Cancer Research UK Southampton Clinical Trials Unit University of Southampton Southampton United Kingdom Department of Haematology University Hospitals Plymouth NHS Trust Plymouth United Kingdom Cancer Research UK Experimental Cancer Medicines Centre University of Southampton Southampton United Kingdom Cancer Research UK Experimental Cancer Medicines Centre UK University of Southampton Southampton United Kingdom University Hospital Southampton NHS Foundation Trust Southampton United Kingdom Beatson West of Scotland Cancer Centre Glasgow United Kingdom Department of Haematology Freeman Hospital Newcastle Upon Tyne Hospitals NHS Foundation Trust Newcastle Upon Tyne United Kingdom The Christie NHS Foundation Trust and Manchester Cancer Research Centre Manchester United Kingdom NIHR Oxford Biomedical Research Centre Churchill Hospital Oxford United Kingdom Departments of Medicine (Biomedical Informatics Research) and Biomedical Data Science Stanford University School of Medicine Stanford CA Cancer Sciences Faculty of Medicine University of Southampton Southampton United Kingdom

Background: CD27 antibody stimulation of T cells has been shown to activate and promote myeloid cell infiltration leading to enhanced antibody-dependent cellular phagocytosis (ADCP) by anti-CD20 in lymphoma preclinical models (Turaj et al Cancer Cell 2017). In this phase IIa study (RiVa NCT03307746), the safety and efficacy of rituximab (ritux) and varlilumab (varli, anti-CD27) was tested in relapsed/refractory B-cell lymphoma. Transcriptional effects of therapy were investigated by RNA sequencing of pre- and post-treatment tumour biopsies and single cell RNA sequencing of in vitro peripheral blood mononuclear cells (PBMC). Methods: Eligible patients with relapsed/refractory CD20 + B-cell lymphoma were randomized 1:1 to arms A: cycle 1 day 1 ritux, day 2 varli; or B: cycle 1 day 1 ritux, day 8 varli. Cycles 2-6 are identical in both arms (day 1 ritux (cycles 2-6); day 2 varli (cycles 3 and 5); 2-weekly cycles. The primary endpoints were overall response and safety. RiVa was funded by CRUK (CRUKD/17/008) and Celldex Therapeutics Inc. Intratumoral biopsies were taken pre-treatment and on cycle 1 day 7/8 post treatment, i.e. post- ritux and varli in arm A, and post-ritux alone in arm B, and subjected to RNA sequencing, deconvolution by CIBERSORTx and Gene Set Enrichment Analysis (GSEA). To gain further insight into how anti-CD27 stimulated T cells activate myeloid cells, PBMC from healthy donors were treated with varli or an isotype control for 48 h and analysed by CITE-seq (10x Genomics). Results: Twenty-seven patients were randomised; 15 indolent B-cell non-Hodgkin lymphoma cases (NHL) (1 mantle cell lymphoma and 14 follicular lymphoma (FL) grade 1, 2 or 3a) and 12 aggressive B-NHL (9 diffuse large B-cell lymphoma, 2 FL grade 3b and one transformed FL). Median age was 71 (range 49-87), median lines of previous treatment were 4 (range 1-13) and 22% were refractory to the last line of treatment. Thirteen patients completed all 6 cycles of treatment, 1 patient was withd

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Environment scan of generative AI infrastructure for clinical and translational science

Npj health systems

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Npj health systems 2025年第1期2卷 4页

作者： Betina Idnay Zihan Xu William G Adams Mohammad Adibuzzaman Nicholas R Anderson Neil Bahroos Douglas S Bell Cody Bumgardner Thomas Campion Mario Castro James J Cimino I Glenn Cohen David Dorr Peter L Elkin Jungwei W Fan Todd Ferris David J Foran David Hanauer Mike Hogarth Kun Huang Jayashree Kalpathy-Cramer Manoj Kandpal Niranjan S Karnik Avnish Katoch Albert M Lai Christophe G Lambert Lang Li Christopher Lindsell Jinze Liu Zhiyong Lu Yuan Luo Peter McGarvey Eneida A Mendonca Parsa Mirhaji Shawn Murphy John D Osborne Ioannis C Paschalidis Paul A Harris Fred Prior Nicholas J Shaheen Nawar Shara Ida Sim Umberto Tachinardi Lemuel R Waitman Rosalind J Wright Adrian H Zai Kai Zheng Sandra Soo-Jin Lee Bradley A Malin Karthik Natarajan W Nicholson Price Ii Rui Zhang Yiye Zhang Hua Xu Jiang Bian Chunhua Weng Yifan Peng Department of Biomedical Informatics Columbia University Irving Medical Center New York NY USA. Department of Population Health Sciences Weill Cornell Medicine New York NY USA. Department of Pediatrics Boston Medical Center Boston MA USA Chobanian & Avedisian School of Medicine Boston University Boston MA USA. Oregon Clinical and Translational Research Institute Oregon Health and Science University Portland OR USA. Department of Public Health Sciences University of California Davis Davis CA USA. Keck School of Medicine University of Southern California Los Angeles CA USA. Department of Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA. Department of Pathology and Laboratory Medicine University of Kentucky College of Medicine Lexington KY USA. Clinical and Translational Science Center Weill Cornell Medicine New York NY USA. Division of Pulmonary Critical Care and Sleep Medicine University of Kansas School of Medicine Kansas City KS USA. Department of Biomedical Informatics and Data Science Heersink School of Medicine University of Alabama Birmingham AL USA. Harvard Law School Petrie-Flom Center for Health Law Policy Biotechnology and Bioethics Harvard University Cambridge MA USA. Department of Biomedical Informatics University at Buffalo Buffalo NY USA. Center for Clinical and Translational Science Mayo Clinic Rochester MN USA. Technology and Digital Solutions Stanford Medicine Stanford University Stanford CA USA. Center for Biomedical Informatics Rutgers Cancer Institute New Brunswick NJ USA. Department of Learning Health Sciences University of Michigan Medical School Ann Arbor MI USA. Altman Clinical and Translational Research Institute (ACTRI) University of California San Diego La Jolla CA USA. Department of Biostatistics and Health Data Science School of Medicine Indiana University Indianapolis IN USA. Department of Ophthalmology CCTSI University of Colorado Aurora CO USA. Center for Clinic

This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the CTSA Program led by the National Center for Advancing Translational sciences (NCATS) of the National Institutes of Health (NIH) at the United States. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The results underscore the need for a more coordinated approach to GenAI governance, emphasizing collaboration among senior leaders, clinicians, information technology staff, and researchers. Our analysis reveals that 53% of institutions identified data security as a primary concern, followed by lack of clinician trust (50%) and AI bias (44%), which must be addressed to ensure the ethical and effective implementation of GenAI technologies.

关键词： Health services Public health

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EpiBeds: data informed modelling of the COVID-19 hospital burden in England

arXiv

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arXiv 2021年

作者： Overton, Christopher E. Pellis, Lorenzo Stage, Helena B. Scarabel, Francesca Burton, Joshua Fraser, Christophe Hall, Ian House, Thomas A. Jewell, Chris Nurtay, Anel Pagani, Filippo Lythgoe, Katrina A. Department of Mathematics University of Manchester United Kingdom Clinical Data Science Unit Manchester University NHS Foundation Trust United Kingdom Joint UNIversities Pandemic and Epidemiological Research Humboldt University of Berlin Germany University of Potsdam Germany MRC Biostatistics Unit University of Cambridge United Kingdom Division of Informatics Imaging and Data Sciences Faculty of Biology Medicine and Health University of Manchester United Kingdom CHICAS Lancaster Medical School Lancaster University United Kingdom Big Data Institute Nuffield Department of Medicine University of Oxford United Kingdom Department of Zoology University of Oxford United Kingdom Wellcome Centre for Human Genetics Nuffield Department of Medicine NIHR Biomedical Research Centre University of Oxford United Kingdom Wellcome Sanger Institute Cambridge United Kingdom Alan Turing Institute United Kingdom Emergency Preparedness Health Protection Division Public Health England United Kingdom

The first year of the COVID-19 pandemic put considerable strain on the national healthcare system in England. In order to predict the effect of the local epidemic on hospital capacity in England, we used a variety of data streams to inform the construction and parameterisation of a hospital progression model, which was coupled to a model of the generalised epidemic. We named this model EpiBeds. data from a partially complete patient-pathway line-list was used to provide initial estimates of the mean duration that individuals spend in the different hospital compartments. We then fitted EpiBeds using complete data on hospital occupancy and hospital deaths, enabling estimation of the proportion of individuals that follow different clinical pathways, and the reproduction number of the generalised epidemic. The construction of EpiBeds makes it straightforward to adapt to different patient pathways and settings beyond England. As part of the UK response to the pandemic, EpiBeds has provided weekly forecasts to the NHS for hospital bed occupancy and admissions in England, Wales, Scotland, and Northern Ireland. © 2021, CC BY.

关键词： Hospitals

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Security improvements of several basic quantum private query protocols with o(log n) communication complexity

arXiv

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arXiv 2020年

作者： Yu, Fang Qiu, Daowen Wang, Xiaoming Li, Qin Li, Lvzhou Gruska, Jozef Department of Computer Science and Technology Jinan University Guangzhou China Institute of Computer Science Theory School of Data and Computer Science Sun Yat-sen University Guangzhou China College of Information Engineering Xiangtan University Xiangtan China Faculty of Informatics Masaryk University Brno Czech Republic Instituto de Telecomunicações Departamento de Matemática Instituto Superior Técnico Universidade de Lisboa Av. Rovisco Pais Lisbon1049-001 Portugal

New quantum private database (with N elements) query protocols are presented and analyzed. Protocols preserve O(log N) communication complexity of known protocols for the same task, but achieve several significant improvements in security, especially concerning user privacy. For example, the randomized form of our protocol has a cheat-sensitive property - it allows the user to detect a dishonest database with a nonzero probability, while the phase-encoded private query protocols [6, 7] for the same task do not have such a property. Moreover, when the database performs the computational basis measurement, a particular projective measurement which can cause a significant loss of user privacy in the previous private query protocols with O(log N) communication complexity, at most half of the user privacy could leak to such a database in our protocol, while in the QPQ protocol [5], the entire user privacy could leak out. In addition, it is proved here that for large N, the user could detect a cheating via the computational basis measurement, with a probability close to 21 using O(√N) special queries. Finally, it is shown here, for both forms of our protocol, basic and randomized, how a dishonest database has to act in case it could not learn user’s queries. Copyright © 2020, The Authors. All rights reserved.

关键词： Computational complexity

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A systematic review of challenges and proposed solutions in modeling multimodal data

arXiv

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arXiv 2025年

作者： Farhadizadeh, Maryam Weymann, Maria Blaß, Michael Kraus, Johann Gundler, Christopher Walter, Sebastian Hempen, Noah Binder, Harald Binder, Nadine Institute of General Practice/Family Medicine Faculty of Medicine Medical Center University of Freiburg Elsässer Straße 2M Freiburg79110 Germany Freiburg Center for Data Analysis Modeling and AI University of Freiburg Ernst-Zermelo-Str. 1 Freiburg79104 Germany Institute of Medical Biometry and Statistics Faculty of Medicine Medical Center University of Freiburg Stefan-Meier-Straße 26 Freiburg79104 Germany Institute for Applied Medical Informatics University Medical Center Hamburg-Eppendorf Martinistraße 52 Hamburg20251 Germany Institute of Medical Systems Biology Ulm University Helmholtzstraße 16 Ulm89081 Germany Department of Computer Science Faculty of Engineering University of Freiburg Georges-Köhler-Allee 051 Freiburg79110 Germany

Multimodal data modeling has emerged as a powerful approach in clinical research, enabling the integration of diverse data types such as imaging, genomics, wearable sensors, and electronic health records. Despite its potential to improve diagnostic accuracy and support personalized care, modeling such heterogeneous data presents significant technical challenges. This systematic review synthesizes findings from 69 studies to identify common obstacles, including missing modalities, limited sample sizes, dimensionality imbalance, interpretability issues, and finding the optimal fusion techniques. We highlight recent methodological advances, such as transfer learning, generative models, attention mechanisms, and neural architecture search that offer promising solutions. By mapping current trends and innovations, this review provides a comprehensive overview of the field and offers practical insights to guide future research and development in multimodal modeling for medical applications. © 2025, CC BY.

关键词： Clinical research

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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Abnormalities in neurotransmission and energy metabolism in Chronic Fatigue Syndrome and long-COVID – a 7T MRS study

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Neuroscience Applied 2022年 1卷

作者： B. Godlewska A. Sylvester S. Williams A.J. Goncalves L. Valkovic B. Raman P.J. Cowen 1University of Oxford Department of Psychiatry Oxford United Kingdom 2University of Manchester Division of Informatics- Imaging and Data Science- Faculty of Medicine- Biology and Health Manchester United Kingdom 3University of Manchester Wolfson Molecular Imaging Centre- Faculty of Biology- Medicine and Health Manchester United Kingdom 4University of Oxford Division of Cardiovascular Medicine- Radcliffe Department of Medicine- Oxford Centre for Clinical Magnetic Resonance Research Oxford United Kingdom

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