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arXiv 2024年

作者： Fleurence, Rachael L. Dawoud, Dalia Bian, Jiang Higashi, Mitchell K. Wang, Xiaoyan Xu, Hua Chhatwal, Jagpreet Ayer, Turgay Office of the Director National Institutes of Health National Institute of Biomedical Imaging and Bioengineering BethesdaMD United States National Institute for Health and Care Excellence London United Kingdom Cairo University Faculty of Pharmacy Cairo Egypt Health Outcomes and Biomedical Informatics College of Medicine University of Florida FL United States Biomedical Informatics Clinical and Translational Science Institute University of Florida FL United States Office of Data Science and Research Implementation University of Florida Health GainesvilleFL United States ISPOR The Professional Society for Health Economics and Outcomes Research LawrencevilleNJ United States Tulane University School of Public Health and Tropical Medicine New OrleansLA United States Intelligent Medical Objects RosemontIL United States Institute Department of Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT United States Institute for Technology Assessment Massachusetts General Hospital Harvard Medical School BostonMA United States Center for Health Decision Science Harvard University BostonMA United States Value Analytics Labs BostonMA United States Center for Health & Humanitarian Systems Georgia Institute of Technology AtlantaGA United States

Introduction: Generative Artificial Intelligence, particularly large language models (LLMs), offers transformative potential for Health Economics and Outcomes Research (HEOR). However, evaluating the quality, transparency, and rigor of LLM-assisted research lacks standardized guidance. This article introduces the ELEVATE-AI LLMs framework and checklist, designed to support researchers and reviewers in assessing LLM use in HEOR. Methods: The ELEVATE-AI LLMs framework was developed through a targeted review of existing guidelines and evaluation frameworks. The framework comprises ten evaluation domains, including model characteristics, accuracy, comprehensiveness, and fairness. The accompanying checklist operationalizes the framework. To validate the framework, we applied it to two published studies, demonstrating its usability across different HEOR tasks. Results: The ELEVATE-AI LLMs framework provides a comprehensive structure for evaluating LLM-assisted research, while the checklist facilitates practical application. Validation of the framework and checklist on studies of systematic literature reviews and health economic modeling highlighted their ability to identify strengths and gaps in reporting. Limitations: While the ELEVATE-AI LLMs framework provides robust guidance, its broader generalizability and applicability to diverse HEOR tasks require further empirical testing. Additionally, several metrics adapted from computer science, need further validation in HEOR contexts. Conclusion: The ELEVATE-AI LLMs framework and checklist fill a critical gap in HEOR by offering structured guidance for evaluating LLM-assisted research. By promoting transparency, accuracy, and reproducibility, they aim to standardize and improve the integration of LLMs into HEOR, ensuring their outputs meet the field's rigorous standards. © 2024, CC BY.

关键词： Ability testing

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Machine learning applications for therapeutic tasks with genomics data

arXiv

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arXiv 2021年

作者： Huang, Kexin Xiao, Cao Glass, Lucas M. Critchlow, Cathy W. Gibson, Greg Sun, Jimeng Health Data Science Program Harvard University Boston02120 United States Analytics Center of Excellence IQVIA CambridgeMA02139 United States Center for Observational Research Amgen Thousand OaksCA91320 United States Center for Integrative Genomics Georgia Institute of Technology AtlantaGA30332 United States Department of Computer Science Carle's Illinois College of Medicine University of Illinois at Urbana-Champaign Urbana61820 United States

The genome contains instructions for building the function and structure, and guiding the evolution of molecules and organisms. Recent high-throughput techniques allow the generation of a vast amount of genomics data. However, the path of transforming genomics data into tangible therapeutics is filled with obstacles. We observe that genomics data alone are insufficient but rather require investigation of its interplay with data such as compounds, proteins, electronic health records, images, texts, etc. To make sense of these complex data, machine learning techniques are often utilized for identifying patterns and drawing insights from data. In this review, we study twenty-two genomics applications of machine learning that can enable faster and more efficacious therapeutic development, from discovering novel targets, personalized medicine, developing gene-editing tools all the way to clinical trials and post-market studies. Challenges remain, including technical issues such as learning under different contexts given specific low resource constraints, and practical issues such as mistrust of models, privacy, and fairness. Copyright © 2021, The Authors. All rights reserved.

关键词： Genes

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Brugada syndrome in Japan and Europe: a genome-wide association study reveals shared genetic architecture and new risk loci

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European heart journal 2024年第26期45卷 2320-2332页

作者： Taisuke Ishikawa Tatsuo Masuda Tsuyoshi Hachiya Christian Dina Floriane Simonet Yuki Nagata Michael W T Tanck Kyuto Sonehara Charlotte Glinge Rafik Tadros Apichai Khongphatthanayothin Tzu-Pin Lu Chihiro Higuchi Tadashi Nakajima Kenshi Hayashi Yoshiyasu Aizawa Yukiko Nakano Akihiko Nogami Hiroshi Morita Seiko Ohno Takeshi Aiba Christian Krijger Juárez John Mauleekoonphairoj Yong Poovorawan Jean-Baptiste Gourraud Wataru Shimizu Vincent Probst Minoru Horie Arthur A M Wilde Richard Redon Jyh-Ming Jimmy Juang Koonlawee Nademanee Connie R Bezzina Julien Barc Toshihiro Tanaka Yukinori Okada Jean-Jacques Schott Naomasa Makita Omics Research Center National Cerebral and Cardiovascular Center Suita Japan. Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan. StemRIM Institute of Regeneration-Inducing Medicine Osaka University Graduate School of Medicine Suita Japan. Department of Obstetrics and Gynecology Osaka University Graduate School of Medicine Suita Japan. Institute for Biomedical Sciences Iwate Medical University Iwate Japan. L'institut du thorax Nantes Université CHU Nantes CNRS INSERM Nantes France. Bioresource Research Center Tokyo Medical and Dental University Tokyo Japan. Department of Human Genetics and Disease Diversity Tokyo Medical and Dental University Tokyo Japan. Epidemiology and Data Science Amsterdam UMC location University of Amsterdam Amsterdam The Netherlands. Department of Genome Informatics Graduate School of Medicine the University of Tokyo Tokyo Japan. Department of Experimental Cardiology Amsterdam UMC location University of Amsterdam Heart Centre Amsterdam The Netherlands. Heart Failure & Arrhythmias Amsterdam Cardiovascular Sciences Amsterdam The Netherlands. Department of Cardiology The Heart Centre Copenhagen University Hospital Rigshospitalet Copenhagen Denmark. European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart (ERN GUARD-HEART https://guardheart.ern-net.eu). Montreal Heart Institute Universite de Montreal Cardiovascular Genetics Centre Montreal Quebec Canada. Department of Medicine Center of Excellence in Arrhythmia Research Faculty of Medicine Chulalongkorn University Bangkok Thailand. Department of Pediatrics Division of Cardiology Faculty of Medicine Chulalongkorn University Bangkok Thailand. Department of Cardiology Bangkok Hospital Bangkok Thailand. Department of Public Health Institute of Health Data Analytics and Statistics National Taiwan University Taipei Taiwan. Artificial Intelligence Center for Health and Biomedical Research National Institutes of Bio

BACKGROUND AND AIMS:Brugada syndrome (BrS) is an inherited arrhythmia with a higher disease prevalence and more lethal arrhythmic events in Asians than in Europeans. Genome-wide association studies (GWAS) have revealed its polygenic architecture mainly in European populations. The aim of this study was to identify novel BrS-associated loci and to compare allelic effects across ancestries. METHODS:A GWAS was conducted in Japanese participants, involving 940 cases and 1634 controls, followed by a cross-ancestry meta-analysis of Japanese and European GWAS (total of 3760 cases and 11 635 controls). The novel loci were characterized by fine-mapping, gene expression, and splicing quantitative trait associations in the human heart. RESULTS:The Japanese-specific GWAS identified one novel locus near ZSCAN20 (P = 1.0 × 10-8), and the cross-ancestry meta-analysis identified 17 association signals, including six novel loci. The effect directions of the 17 lead variants were consistent (94.1%; P for sign test = 2.7 × 10-4), and their allelic effects were highly correlated across ancestries (Pearson's R = .91; P = 2.9 × 10-7). The genetic risk score derived from the BrS GWAS of European ancestry was significantly associated with the risk of BrS in the Japanese population [odds ratio 2.12 (95% confidence interval 1.94-2.31); P = 1.2 × 10-61], suggesting a shared genetic architecture across ancestries. Functional characterization revealed that a lead variant in CAMK2D promotes alternative splicing, resulting in an isoform switch of calmodulin kinase II-δ, favouring a pro-inflammatory/pro-death pathway. CONCLUSIONS:This study demonstrates novel susceptibility loci implicating potentially novel pathogenesis underlying BrS. Despite differences in clinical expressivity and epidemiology, the polygenic architecture of BrS was substantially shared across ancestries.

关键词： SCN5A Brugada syndrome Cross-ancestry meta-analysis Genetic risk score Genome-wide association studies

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Transparent Tracing System on gRPC based Microservice Applications Running on Kubernetes

Transparent Tracing System on gRPC based Microservice Applic...

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International Conference of Advanced Informatics: Concept, Theory and Application (ICAICTA)

作者： Abram Perdanaputra Achmad Imam Kistijantoro School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia University Center of Excellence on Artificial Intelligence for Vision Natural Language Processing & Big Data Analytics (U-CoE AI-VLB) Indonesia

ISBN: (纸本)9781728180397

With the increasing use of distributed system-based applications, the demand for tracing system as debugging tools for respective system also increases. Specifically, on Kubernetes environment, tracing on microservice- based systems is not an easy task due to the heterogeneity of the architecture. gRPC provides a more performant way to communicate between microservice. This leads to a need of a tracing system that are transparent with respect to the applications that supports gRPC as its main communication protocol to ease the use of tracing on heterogeneous environment. The approach used to achieve transparency is passive tracing. Passive tracing requires the system to perform tracing without any modifications to the application or the application runtime. This system implements passive tracing by performing packet interception on every packet sent by the application, recording the gRPC request data, and writing the trace data to a log. The resulting log will be stored in Elasticsearch and visualized using Kibana for further analysis. The implementation of this system adds an average overhead CPU usage of 8.8% with Elastic stack and 3.5% without Elastic stack and memory usage of 959 MB with Elastic stack and 378 MB without Elastic stack. In addition, 61.9% of the actual request successfully traced using this system. This implementation successfully traces transparently, but the log-based approach makes the system unable to trace recursively. This results request causality will not be known. This means the trace results can only produce metadata for each request without knowing the relationship of one request with another. Furthermore, the absence of request causality results an inaccurate number of the success rate of the trace due to matching can only be done by service name and method without the context of each request.

关键词： Runtime Protocols data visualization Debugging Writing Tools Metadata

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INTRODUCTION

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Bulletin of the American Meteorological Society 2024年第8期105卷 S1-S11页

作者： Boyer, T. Blunden, J. Dunn, R.J.H. NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Scripps Institution of Oceanography University of California San Diego La Jolla California NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Brandenburg University of Technology (BTU) Cottbus-Senftenberg Germany Servicio Meteorológico Nacional Buenos Aires Argentina NOAA/OAR Physical Sciences Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research and School of Physics University of Costa Rica San José Costa Rica Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Hampton University Hampton Virginia Seychelles Meteorological Authority Mahe Seychelles Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder Colorado Servicio Nacional de Meteorología e Hidrología del Perú Lima Perú Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Department o

In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and Russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. A

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The incel lexicon: Deciphering the emergent cryptolect of a global misogynistic community

arXiv

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arXiv 2021年

作者： Gothard, Kelly Dewhurst, David Rushing Minot, Joshua R. Adams, Jane Lydia Danforth, Christopher M. Dodds, Peter Sheridan Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Core The University of Vermont BurlingtonVT05401 United States Charles River Analytics 625 Mount Auburn Street CambridgeMA02138 United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05401 United States Department of Computer Science University of Vermont BurlingtonVT05401 United States

Evolving out of a gender-neutral framing of an involuntary celibate identity, the concept of 'incels' has come to refer to an online community of men who bear antipathy towards themselves, women, and society-at-large for their perceived inability to find and maintain sexual relationships. By exploring incel language use on Reddit, a global online message board, we contextualize the incel community's online expressions of misogyny and real-world acts of violence perpetrated against women. After assembling around three million comments from incel-themed Reddit channels, we analyze the temporal dynamics of a data driven rank ordering of the glossary of phrases belonging to an emergent incel lexicon. Our study reveals the generation and normalization of an extensive coded misogynist vocabulary in service of the group's identity. Copyright © 2021, The Authors. All rights reserved.

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Redefining pandemic preparedness:Multidisciplinary insights from the CERP modelling workshop in infectious diseases,workshop report

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Infectious Disease Modelling 2024年第2期9卷 501-518页

作者： Marta C.Nunes Edward Thommes Holger Fröhlich Antoine Flahault Julien Arino Marc Baguelin Matthew Biggerstaff Gaston Bizel-Bizellot Rebecca Borchering Giacomo Cacciapaglia Simon Cauchemez Alex Barbier-Chebbah Carsten Claussen Christine Choirat Monica Cojocaru Catherine Commaille-Chapus Chitin Hon Jude Kong Nicolas Lambert Katharina B.Lauer Thorsten Lehr Cédric Mahe Vincent Marechal Adel Mebarki Seyed Moghadas Rene Niehus Lulla Opatowski Francesco Parino Gery Pruvost Andreas Schuppert Rodolphe Thiébaut Andrea Thomas-Bachli Cecile Viboud Jianhong Wu Pascal Crépey Laurent Coudeville Center of Excellence in Respiratory Pathogens(CERP) Hospices Civils de Lyon(HCL)and Centre International de Recherche en Infectiologie(CIRI)Équipe SantéPubliqueÉpidémiologie etÉcologieÉvolutive des Maladies Infectieuses(PHE3ID)Inserm U1111CNRS UMR5308ENS de LyonUniversitéClaude Bernard Lyon 1LyonFrance South African Medical Research Council Vaccines&Infectious Diseases Analytics Research UnitFaculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa New Products and Innovation(NPI) Sanofi Vaccines(Global)TorontoOntarioCanada Department of Mathematics and Statistics University of GuelphGuelphOntarioCanada Fraunhofer Institute for Algorithms and Scientific Computing(SCAI) Department of BioinformaticsSchloss BirlinghovenSankt AugustinGermany University of Bonn Bonn-Aachen International Center for IT(b-it)BonnGermany Institute of Global Health Faculty of MedicineUniversity of GenevaGenevaSwitzerland and Swiss School of Public HealthZürichSwitzerland Department of Mathematics University of ManitobaWinnipegManitobaCanada MRC Centre for Global Infectious Disease Analysis School of Public HealthImperial College LondonLondonUK Centre for Mathematical Modelling of Infectious Diseases Department of Infectious Disease EpidemiologyLondon School of Hygiene&Tropical MedicineLondonUK National Center for Immunization and Respiratory Diseases(NCIRD) US Centers for Disease Control and Prevention(CDC)AtlantaGAUSA Departement of Computational Biology Departement of Global HealthInstitut PasteurParisFrance Institut de Physique des Deux Infinis de Lyon(IP2I) UMR5822IN2P3/CNRSUniversitéClaude Bernard Lyon 1VilleurbanneFrance Mathematical Modelling of Infectious Diseases Unit Institut PasteurUniversitéParis CitéUMR2000 CNRSParisFrance Decision and Bayesian Computation Institut PasteurUniversitéParis CitéCNRS UMR 3571France Fraunhofer-Institute for Translational Medicine and Pharmacology HamburgGermany Institute of Global Health Faculty of MedicineUniversity of

In July 2023,the center of excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 *** report summarizes the rich discussions that occurred during the *** workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data,social media,and wastewater *** advancements were noted in the development of predictive models,with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease *** role of open collaboration between various stakeholders in modelling was stressed,advocating for the continuation of such partnerships beyond the pandemic.A major gap identified was the absence of a common international framework for data sharing,which is crucial for global pandemic ***,the workshop underscored the need for robust,adaptable modelling frameworks and the integration of different data sources and collaboration across sectors,as key elements in enhancing future pandemic response and preparedness.

关键词： Modelling Covid-19 Infectious diseases Pandemic preparedness Workshop

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Correction: Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study

JMIR aging

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JMIR aging 2025年 8卷 e75690页

作者： Natthanaphop Isaradech Wachiranun Sirikul Nida Buawangpong Penprapa Siviroj Amornphat Kitro Department of Community Medicine Faculty of Medicine Chiang Mai University 110 Intrawarorot Road Meaung 50200 Thailand 66 53935472 66 935476. Environmental and Occupational Medicine Excellence Center Faculty of Medicine Chiang Mai University Chiang Mai Thailand. Center of Data Analytics and Knowledge Synthesis for Health Care Faculty of Medicine Chiang Mai University Chiang Mai Thailand. Department of Biomedical Informatics and Clinical Epidemiology Faculty of Medicine Chiang Mai University Chiang Mai Thailand. Department of Family Medicine Faculty of Medicine Chiang Mai University Chiang Mai Thailand.

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g-circulant matrix and its matrix-vector multiplication algorithm on transformer neural network

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AIP Conference Proceedings 2024年第1期3201卷

作者： Euis Asriani Intan Muchtadi-Alamsyah Ayu Purwarianti Doctoral Program of Mathematics Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Indonesia Algebra Research Group Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Indonesia University Center of Excellence on Artificial Intelligence for Vision Natural Language Processing and Big Data Analytics (U-CoE AI-VLB) Institut Teknologi Bandung Indonesia Informatics Research Group School of Electrical Engineering and Informatics Institut Teknologi Bandung Indonesia

A g-circulant matrix is an n-dimensional matrix Cn,g that can be expressed as Cn,g=[c(gr−s)modn]r,s=0n−1 . This study will investigate the use of g-circulant matrices as weight matrices on transformer neural networks for machine translation. On transformer model tested, the g-circulant weight matrix undergoes multiplication with any vector and involves the DCT-DST algorithm. This algorithm is proposed as a substitute for the commonly used FFT algorithm. For this reason, an orthogonal matrix Un,g was defined by multiplying the Un matrix and appropriate permutation matrix, where Un matrix is a matrix that is spanned by the combination of the real and imaginary parts of eigenvectors of the real 1-circulant matrix. Then we form a Schur decomposition of the g-circulant matrix, composes the Qn,g matrix, and using the DCT-DST to formulate the multiplication of Qn,gUn,g. We discovered that 2-circulant matrices are outperform compared to the other g-circulant matrices and the original model of transformer in terms of accuracy and loss training.

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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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