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Performance analysis by working day in the placement of techano brick

AIP Conference Proceedings 2022年第1期2574卷

作者： Angel Guillermo Montaño Herrera Jose David Quizhpe Campoverde Marco Avila-Calle Marlon Lenin Aguilar Gomez 1Catholic University of Cuenca Master’s Program in Construction with a major in Sustainable Construction management camino a Patamarca y Cojimíes – Uncovía Cuenca Ecuador 2Catholic University of Cuenca Academic Unit of Engineering Industry and Construction Av. De las Américas y General Torres esquina s/n Cuenca Ecuador 3Independed profesional Software engineer Cuenca – Ecuador

Considering the current economic and social situation that we are going through, which forces the construction companies of the city of Cuenca to optimize costs that allow them to be competitive in the current economic situation. Considering the different scenarios that can occur in a high-rise building, several of them are analysed, which allow the gradual increase in the performance of workers in the placement of techano brick, with the aim of improving the construction processes that are carried out in the different works of the city of Cuenca - Ecuador, which is where the study is carried out, with the definition of specific tasks of the workers and with the help of mechanical and seasonal equipment that directly affect the sequential increase of the units placed by day. The information is gathered from surveys conducted among construction residents and builders in charge of staff in the city.

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The Jimmie Hamilton Award

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Naval Engineers Journal 1971年第3期83卷 36-38页

作者： NACHTSHEIM, JOHN J. BALLOU, L. DENNIS John J. Nachtsheim:is currently the Deputy Assistant Administrator for Research & Development for the Maritime Administration. His duties are the planning coordinating organizing evaluating and directing of the R&D activities of MarAd. His past experiences include: Naval Architect for the Naval Ship Engineering Center 1959 Deputy Chief Design Engineer for the Puget Sound Naval Shipyard 1958 to 1959 and Naval Architect the former Bureau of Ships 1948 to 1958. His education is comprised of a B.S. degree from the Webb Institute of Naval Architecture an L.L.B. degree from the George Washington University Law School completion of the Advanced Management Program at Harvard University and current study of Transportation at the American University. He is a Registered Professional Engineer in the District of Columbia and a Member of the Bar in the District of Columbia and the State of Maryland. In addition to ASNE his other professional memberships include the Society of Naval Architects and Marine Engineers the Society of Aeronautical Weight Engineers and the Association of Senior Engineers of the Naval Ships Systems Command (Honorary). USN Commander L. Dennis Ballou: USN is the Head of the Engineering Service Office Naval Ship Engineering Center. He is involved in computer hardware and software services to support engineering design automatic data processing systems design work study and quality assurance. Prior to NavSec duty Commander Ballou served in various billets afloat and ashore: tours on the USS Skagit and Tang supervision of the USS Skipjack's first overhul construction of the USS Nathanael Greene and helping to establish the Polaris overhaul program. He is a graduate of the U.S. Naval Academy Officers' Submarine School and the Webb Institute of Naval Architecture. He holds BS and MS degrees in marine engineering and naval architecture respectively. He has also completed many graduate

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Refining the E+A Galaxy: A spatially resolved spectrophotometric sample of nearby post-starburst systems in SDSS-IV MaNGA (MPL-5)

arXiv

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arXiv 2021年

作者： Greene, Olivia A. Anderson, Miguel R. Marinelli, Mariarosa Holley-Bockelmann, Kelly Campbell, Lauren E.P. Liu, Charles T. Department of Physics & Astronomy Vanderbilt University PMB 401807 2301 Vanderbilt Place NashvilleTN37206 United States Department of Software Engineering Bloomberg LP 731 Lexington Avenue New YorkNY10022 United States Department of Planetarium Education Science Museum of Virginia 2500 West Broad Street RichmondVA23220 United States Department of Life & Physical Sciences Fisk University 1000 17th Avenue North NashvilleTN37208 United States Department of Physics & Astronomy City University of New York College of Staten Island 2800 Victory Blvd Staten IslandNY10314 United States Department of Astrophysics & Hayden Planetarium American Museum of Natural History Central Park West at 79th Street New YorkNY10024-5192 United States Physics Program The Graduate Center CUNY New YorkNY10016 United States

Post-starburst galaxies are crucial to disentangling the effect of star formation and quenching on galaxy demographics. They comprise, however, a heterogeneous population of objects, described in numerous ways. To obtain a well-defined and uncontaminated sample, we take advantage of spatially-resolved spectroscopy to construct an unambiguous sample of E+A galaxies - post-starburst systems with no observed ongoing star formation. Using data from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) Survey, in the fourth generation of the Sloan Digital Sky Survey (SDSS-IV), we have identified 30 E+A galaxies that lie within the green valley of color–stellar mass space. We first identified E+A candidates by their central, single-fiber spectra and (u-r) color from SDSS DR15, and then further required each galaxy to exhibit E+A properties throughout the entirety of the system to 3 effective radii. We describe our selection criteria in detail, note common pitfalls in E+A identification, and introduce the basic characteristics of the sample. We will use this E+A sample, which has been assembled with stringent criteria and thus re-establishes a well-defined sub-population within the broader category of post-starburst galaxies, to study the evolution of galaxies and their stellar populations in the time just after star formation within them is fully quenched. Copyright © 2021, The Authors. All rights reserved.

关键词： Galaxies

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OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

arXiv

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arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, Philip Wang, Yuanqing Zhang, Ivy Chodera, John D. De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Recent Advances in Systems and Network Medicine: Meeting Report from the First International Conference in Systems and Network Medicine

Recent Advances in Systems and Network Medicine: Meeting Rep...

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作者： Kurnat-Thoma, Emma Baranova, Ancha Baird, Pat Brodsky, Elia Butte, Atul J. Cheema, Amrita K. Cheng, Feixiong Dutta, Shuchismita Grant, Christina Giordano, James Maitland-Van Der Zee, Anke H. Fridsma, Douglas B. Jarrin, Robert Kann, Maricel G. Keeney, Jonathon Loscalzo, Joseph Madhavan, Guru Maron, Bradley A. Mcbride, Dennis K. Mckean, Maeve Mun, Seong K. Palmer, James C. Patel, Bakul Parakh, Kapil Pariser, Anne R. Pristipino, Christian Radstake, Timothy R.D.J. Rajasimha, Harsha K. Rouse, William B. Rozman, Damjana Saleh, Alif Schmidt, Harald H.H.W. Schultz, Nikolaus Sethi, Tavpritesh Silverman, Edwin K. Skopac, Jessica Svab, Igor Trujillo, Sylvia Valentine, James E. Verma, Dinesh West, Bruce J. Vasudevan, Sona BethesdaMD United States Genetics and Genomics Georgetown University School of Nursing and Health Studies WashingtonDC United States School of Systems Biology George Mason University FairfaxVA United States Global Software Standards Philips Pleasant PrairieWI United States Pine Biotech New OrleansLA United States San FranciscoCA United States Department of Biochemistry Molecular and Cellular Biology Georgetown University Medical Center WashingtonDC United States Cleveland Clinic Lerner College of Medicine Case Western Reserve University Cleveland Clinic ClevelandOH United States Institute for Quantitative Biomedicine Rutgers University The State University of New Jersey PiscatawayNJ United States Rare Disease Institute Division of Genetics and Metabolism Children's National Medical Center WashingtonDC United States Department of Neurology Neuroethics Studies Program-Pellegrino Center for Clinical Bioethics O'Neill-Pellegrino Program in Brain Sciences and Global Law and Policy Georgetown University Medical Center WashingtonDC United States Department of Biochemistry Neuroethics Studies Program-Pellegrino Center for Clinical Bioethics O'Neill-Pellegrino Program in Brain Sciences and Global Law and Policy Georgetown University Medical Center WashingtonDC United States Department of Respiratory Medicine Amsterdam University Medical Centers University of Amsterdam Amsterdam Netherlands American Medical Informatics Association WashingtonDC United States Department of Biological Sciences University of Maryland Baltimore County BaltimoreMD United States Department of Biochemistry and Molecular Medicine George Washington University WashingtonDC United States Department of Medicine Brigham and Women's Hospital Harvard Medical School BostonMA United States National Academy of Engineering The National Academies of Sciences Engineering and Medicine WashingtonDC United States WashingtonDC United States Georgetown University Global Health

The First International Conference in Systems and Network Medicine gathered together 200 global thought leaders, scientists, clinicians, academicians, industry and government experts, medical and graduate students, postdoctoral scholars and policymakers. Held at Georgetown University Conference Center in Washington D.C. on September 11-13, 2019, the event featured a day of pre-conference lectures and hands-on bioinformatic computational workshops followed by two days of deep and diverse scientific talks, panel discussions with eminent thought leaders, and scientific poster presentations. Topics ranged from: Systems and Network Medicine in Clinical Practice;the role of -omics technologies in Health Care;the role of Education and Ethics in Clinical Practice, Systems Thinking, and Rare Diseases;and the role of Artificial Intelligence in Medicine. The conference served as a unique nexus for interdisciplinary discovery and dialogue and fostered formation of new insights and possibilities for health care systems advances. © Emma Kurnat-Thoma et al. 2020 Published by Mary Ann Liebert, Inc. 2020.

关键词： Artificial intelligence

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The MR neuroimaging protocol for the Accelerating Medicines Partnership® Schizophrenia program

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Schizophrenia 2025年第1期11卷 1-14页

作者： Harms, Michael P. Cho, Kang-Ik K. Anticevic, Alan Bolo, Nicolas R. Bouix, Sylvain Campbell, Dylan Cannon, Tyrone D. Cecchi, Guillermo Goncalves, Mathias Haidar, Anastasia Hughes, Dylan E. Izyurov, Igor John, Omar Kapur, Tina Kim, Nicholas Kotler, Elana Kubicki, Marek Kuperman, Joshua M. Laulette, Kristen Lindberg, Ulrich Markiewicz, Christopher Ning, Lipeng Poldrack, Russell A. Rathi, Yogesh Romo, Paul A. Tamayo, Zailyn Wannan, Cassandra Wickham, Alana Yassin, Walid Zhou, Juan Helen Addington, Jean Alameda, Luis Arango, Celso Breitborde, Nicholas J. K. Broome, Matthew R. Cadenhead, Kristin S. Calkins, Monica E. Chen, Eric Yu Hai Choi, Jimmy Conus, Philippe Corcoran, Cheryl M. Cornblatt, Barbara A. Diaz-Caneja, Covadonga M. Ellman, Lauren M. Fusar-Poli, Paolo Gaspar, Pablo A. Gerber, Carla Glenthøj, Louise Birkedal Horton, Leslie E. Hui, Christy Lai Ming Kambeitz, Joseph Kambeitz-Ilankovic, Lana Keshavan, Matcheri S. Kim, Sung-Wan Koutsouleris, Nikolaos Kwon, Jun Soo Langbein, Kerstin Mamah, Daniel Mathalon, Daniel H. Mittal, Vijay A. Nordentoft, Merete Pearlson, Godfrey D. Perez, Jesus Perkins, Diana O. Powers, Albert R. Rogers, Jack Sabb, Fred W. Schiffman, Jason Shah, Jai L. Silverstein, Steven M. Smesny, Stefan Stone, William S. Strauss, Gregory P. Thompson, Judy L. Upthegrove, Rachel Verma, Swapna K. Wang, Jijun Wolf, Daniel H. Kahn, Rene S. Kane, John M. McGorry, Patrick D. Nelson, Barnaby Woods, Scott W. Shenton, Martha E. Wood, Stephen J. Bearden, Carrie E. Pasternak, Ofer Department of Psychiatry Washington University School of Medicine St. Louis MO United States Department of Psychiatry Brigham and Women’s Hospital Harvard Medical School Boston MA United States Department of Psychiatry Yale University School of Medicine New Haven CT United States Department of Psychiatry Beth Israel Deaconess Medical Center Harvard Medical School Boston MA United States Department of Software Engineering and Information Technology École de technologie supérieure Montréal QC Canada Department of Psychology Yale University New Haven CT United States T.J. Watson Research Laboratory IBM Research Yorktown Heights NY United States Department of Psychology Stanford University Stanford CA United States Department of Psychology University of California Los Angeles CA United States Department of Psychiatry and Psychotherapy Jena University Hospital Jena Germany Massachusetts General Hospital Harvard Medical School Boston MA United States Department of Radiology Brigham and Women’s Hospital Harvard Medical School Boston MA United States Department of Radiology University of California San Diego CA United States Department of Clinical Physiology and Nuclear Medicine Copenhagen University Hospital - Rigshospitalet Glostrup Denmark Seaman Family MR Research Centre Calgary AB Canada Orygen Parkville VIC Australia Centre for Sleep and Cognition and Centre for Translational MR Research Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore Department of Psychiatry Hotchkiss Brain Institute University of Calgary Calgary AB Canada General Psychiatry Service Treatment and Early Intervention in Psychosis Program Lausanne University Hospital and University of Lausanne Lausanne Switzerland Department of Child and Adolescent Psychiatry Institute of Psychiatry and Mental Health Hospital General Universitario Gregorio Marañón Instituto de Salud Carlos III School of Medicine Universidad Complutense Madrid Spain

Neuroimaging with MRI has been a frequent component of studies of individuals at clinical high risk (CHR) for developing psychosis, with goals of understanding potential brain regions and systems impacted in the CHR state and identifying prognostic or predictive biomarkers that can enhance our ability to forecast clinical outcomes. To date, most studies involving MRI in CHR are likely not sufficiently powered to generate robust and generalizable neuroimaging results. Here, we describe the prospective, advanced, and modern neuroimaging protocol that was implemented in a complex multi-site, multi-vendor environment, as part of the large-scale Accelerating Medicines Partnership® Schizophrenia program (AMP® SCZ), including the rationale for various choices. This protocol includes T1- and T2-weighted structural scans, resting-state fMRI, and diffusion-weighted imaging collected at two time points, approximately 2 months apart. We also present preliminary variance component analyses of several measures, such as signal- and contrast-to-noise ratio (SNR/CNR) and spatial smoothness, to provide quantitative data on the relative percentages of participant, site, and platform (i.e., scanner model) variance. Site-related variance is generally small (typically <10%). For the SNR/CNR measures from the structural and fMRI scans, participant variance is the largest component (as desired;40–76%). However, for SNR/CNR in the diffusion scans, there is substantial platform-related variance (>55%) due to differences in the diffusion imaging hardware capabilities of the different scanners. Also, spatial smoothness generally has a large platform-related variance due to inherent, difficult to control, differences between vendors in their acquisitions and reconstructions. These results illustrate some of the factors that will need to be considered in analyses of the AMP SCZ neuroimaging data, which will be the largest CHR cohort to date. Watch Dr. Harms discuss this article at https://vimeo.

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Safe food for infants: An EU-China project to enhance the control of safety risks raised by microbial and chemical hazards all along the infant food chains

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Global Pediatrics 2022年 2卷

作者： Engel, Erwan Rivière, Gilles Kemmer, Diana Deusch, Oliver Fuchsbauer, Norbert Biesterveld, Steven Krystalli, Evangelia Bondoux, Marion Li, Guang Yang, Weikang Hou, Jianbo Liang, Ying Yang, Hua Fang, Weihuan Pettoello-Mantovani, Massimo Flynn, Brian Rantsiou, Kalliopi Van der Burg, Bart Bover-Cid, Sara Zwietering, Marcel H. French National Research Institute for Agriculture Food and Environment (INRAE) UR QuaPA MASS Group Saint-Genès-Champanelle France French Agency for Food Environmental and Occupational Health & Safety (ANSES) Maisons-Alfort France Fraunhofer Institute for Process Engineering and Packaging (IVV) Freising Germany Computomics GmbH Tübingen Germany HiPP-Werk Georg Hipp OHG Pfaffenhofen Germany Friesland Campina Nederland BV Amersfoort Netherlands Yiotis Anonimos Emporiki & Viomixaniki Etaireia Athens Greece INRAE Transfert Clermont-Ferrand France Beingmate (Hangzhou) Food Research Institute Co. Ltd Hangzhou China Yangzhou Fangguang Food Co. Ltd. Yangzhou China Zhejiang Academy of Science & Technology for Inspection & Quarantine (ZAIQ Hangzhou China Jiangsu Academy of Agricultural Sciences (JAAS) Nanjing China Zhejiang Academy of Agricultural Sciences (ZAAS) Hangzhou China Zhejiang University (ZJU) Hangzhou China EPA-UNEPSA Berlin Germany Creme software LTD (Creme Global) Dublin Ireland University of Turin Department of Agricultural Forest and Food Sciences Turin Italy BioDetection Systems B.V. Amsterdam Netherlands Institut de Recerca i Tecnologia Agroalimentàries (IRTA) Food Safety and Functionality Program Monells Spain Wageningen University & Research Food Microbiology Wageningen Netherlands

The EU-project SAFFI targets food for EU's 15 million and China's 45 million children under the age of three. It aims at developing an integrated approach to enhance the identification, assessment, detection and mitigation of health risks raised by microbial and chemical hazards along EU and China infant food chains. SAFFI will benchmark the main risks through an extensive hazard identification system based on multiple data sources and a risk ranking procedure. It will also develop procedures to enhance top-down and bottom-up hazard control by combining management options with a panel of technologies for the detection and mitigation of priority hazards. Furthermore, it will explore unexpected contaminants by predictive toxicology and improve risk-based food safety management of biohazards by omics and predictive microbiology. SAFFI will co-develop with and deliver to stakeholders a decision-support system (DSS) to enhance safety control all along the food chain. This DSS will integrate the databases, procedures and methods described above and will be a framework for a generic DSS dedicated to other food. This overall methodology will also be implemented in a complementary Chinese side of the project, and exemplified for each side, with four case studies that were selected to cover priority hazards, main ingredients, processes and control steps of the infant food chain. Resulting databases, tools and procedures will be shared, cross-validated, concatenated, benchmarked and finally harmonized for further use in the EU and China. This EU-China multi-actor consortium of 20 partners involves academia, food safety authorities, infant food companies, a paediatrics association and technological and data-science SMEs. © 2022

关键词： Baby food Chemical contaminants Decision support system Food safety Foodborne hazards Hazard control Hazard detection Risk assessment

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Self-Awareness for Autonomous Systems

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PROCEEDINGS OF THE IEEE 2020年第7期108卷 971-975页

作者： Dutt, Nikil Regazzoni, Carlo S. Rinner, Bernhard Yao, Xin Nikil Dutt (Fellow IEEE) received the Ph.D. degree from the University of Illinois at Urbana–Champaign Champaign IL USA in 1989.""He is currently a Distinguished Professor of computer science (CS) cognitive sciences and electrical engineering and computer sciences (EECS) with the University of California at Irvine Irvine CA USA. He is a coauthor of seven books. His research interests include embedded systems electronic design automation (EDA) computer architecture distributed systems healthcare Internet of Things (IoT) and brain-inspired architectures and computing.""Dr. Dutt is a Fellow of ACM. He was a recipient of the IFIP Silver Core Award. He has received numerous best paper awards. He serves as the Steering Committee Chair of the IEEE/ACM Embedded Systems Week (ESWEEK). He is also on the steering organizing and program committees of several premier EDA and embedded system design conferences and workshops. He has served on the Editorial Boards for the IEEE Transactions on Very Large Scale Integration (VLSI) Systems and the ACM Transactions on Embedded Computing Systems and also previously served as the Editor-in-Chief (EiC) for the ACM Transactions on Design Automation of Electronic Systems. He served on the Advisory Boards of the IEEE Embedded Systems Letters the ACM Special Interest Group on Embedded Systems the ACM Special Interest Group on Design Automationt and the ACM Transactions on Embedded Computing Systems. Carlo S. Regazzoni (Senior Member IEEE) received the M.S. and Ph.D. degrees in electronic and telecommunications engineering from the University of Genoa Genoa Italy in 1987 and 1992 respectively.""He is currently a Full Professor of cognitive telecommunications systems with the Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN) University of Genoa and a Co-Ordinator of the Joint Doctorate on Interactive and Cognitive Environments (JDICE) international Ph.D. course started initially as EU Erasmus Mundus Project and

Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In computer systems, one can derive such behavior from the concept of a rational agent with autonomy (“control over its own actions”), reactivity (“react to events from the environment”), proactivity (“act on its own initiative”), and sociality (“interact with other agents”) as fundamental properties \n[1]\n. Autonomous systems will undoubtedly pervade into our everyday lives, and we will find them in a variety of domains and applications including robotics, transportation, health care, communications, and entertainment to name a few. \nThe articles in this month’s special issue cover concepts and fundamentals, architectures and techniques, and applications and case studies in the exciting area of self-awareness in autonomous systems.

关键词： Special issues and sections Autonomous systems Self-aware Robot sensing systems Computational modeling

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第Sup1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Kings County Hospital Center Brooklyn NY 11203 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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