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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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DPb-MOPSO: A Novel Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization Algorithm

TechRxiv

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TechRxiv 2021年

作者： Aboud, Ahlem Rokbani, Nizar Fdhila, Raja Qahtani, Abdulrahman M. Almutiry, Omar Dhahri, Habib Hussain, Amir Alimi, Adel University of Sousse ISITCom Sousse4011 Tunisia BP 1173 Sfax3038 Tunisia BP 1173 Sfax3038 Tunisia Department of Computer Science College of Computers and Information Technology Taif University P.O.Box. 11099 Taif21944 Saudi Arabia College of Applied Computer Science King Saud University Riyadh Saudi Arabia Edinburgh Napier University School of Computing EdinburghEH10 5DT United Kingdom Department of Electrical and Electronic Engineering Science Faculty of Engineering and the Built Environment University of Johannesburg South Africa

Particle swarm optimization system based on the distributed architecture has shown its efficiency for static optimization and has not been studied to solve dynamic multiobjective problems (DMOPs). When solving DMOP, tracking the best solutions over time and ensuring good exploitation and exploration are the main challenging tasks. This study proposes a novel Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization (DPb-MOPSO) algorithm including two parallel optimization levels. At the first level, all solutions are managed in a single search space. When a dynamic change is successfully detected, the Pareto ranking operator is used to enable a multi-swarm subdivisions and processing which drives the second level of enhanced exploitation. A dynamic handling strategy based on random detectors is used to track the changes of the objective function due to time-varying parameters. A response strategy consisting in re-evaluate all unimproved solutions and replacing them with newly generated ones is also implemented. Inverted generational distance, mean inverted generational distance, and hypervolume difference metrics are used to assess the DPb-MOPSO performances. All quantitative results are analyzed using Friedman's analysis while the Lyapunov theorem is used for stability analysis. Compared with several multi-objective evolutionary algorithms, the DPb-MOPSO is robust in solving 21 complex problems over a range of changes in both the Pareto optimal set and Pareto optimal front. For 13 UDF and ZJZ functions, DPb-MOPSO can solve 8/13 and 7/13 on IGD and HVD with moderate changes. For the 8 FDA and dMOP benchmarks, DPb-MOPSO was able to resolve 4/8 with severe change on MIGD, and 5/8 for moderate and slight changes. However, for the 3 kind of environmental changes, DPb-MOPSO assumes 4/8 of the solving function on IGD and HVD. © 2021, CC BY.

关键词： Particle swarm optimization (PSO)

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Blended learning and media centers: A bibliometric analysis

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Social Sciences and Humanities Open 2024年 10卷

作者： Challco, Geiser Chalco Silva, Wesley Vieira da Setton Gonçalves, Bruno de Almeida Levino, Natallya Bittencourt, Ibsen Mateus Kaczam, Fabíola de Oliveira, Elaine Harada Teixeira Martins, Flavio Pinheiro de Lima Filho, Walter Araujo de Araújo, Ronaldo Ferreira Bittencourt, Ig Ibert Paiva, Ranilson Oscar Araújo Department of Engineering and Technology (DETEC) Federal Rural University of Semi-Árido (UFERSA) RN Pau dos Ferros 59900-000 Brazil Scholarship CNPq Research Productivity (D Level) and Professor Faculty of Economics Administration and Accounting Federal University of Alagoas (UFAL) Maceió 57072-900 Brazil Faculty of Economics Administration and Accounting Federal University of Alagoas (UFAL) Maceió 57072-900 Brazil Leader of the Laboratory of Applied Public Administration (LAPA) of the Public Administration Course Campus Arapirca Federal University of Alagoas Arapiraca 57309-005 Brazil Faculty of Economics Business and Accounting Federal University of Alagoas Maceio 57072-900 Brazil Postgraduate Program in Administration Federal University Santa Maria (UFSM) Building 74C 2nd floor Room 4209 97105-900 Brazil Institute of Computing Federal University of Amazonas Amazonas Manaus Brazil The Bartlett School of Sustainable Construction University College London—UCL London WC1E 6BT United Kingdom Visiting Researcher at Leiden University ou Pesquisador Visitante na Universidade de Leiden Institute of Human Sciences Communication and Arts Federal University of Alagoas Alagoas Maceio 57072-900 Brazil Harvard Graduate School of Education Harvard University Cambridge MA United States Center of Excellence for Social Technologies Federal University of Alagoas Brazil Universidade Federal de Alagoas - Instituto de Computação Brazil

Blended learning can be considered an approach that combines face-to-face and online periods in education through the integration of some technological resources, such as media centers. The present research sought to investigate the panorama of scientific production on blended learning and identify evidence of how media centers are used to support this type of teaching. This is a bibliometric study carried out based on the principles of the Systematic Literature Review (SLR). Although the results indicate an increase in production, a high rate of authors with only one published work was identified, which can be interpreted as an expanding topic. The United States stood out as the country that contributed the most with research and the areas of Computer Science and Education among the fields of research with the highest number of articles. © 2024 The Author(s)

关键词： Blended learning Hybrid education Media centers Systematic review

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LIMIT THEOREMS FOR A STABLE SAUSAGE

arXiv

引用

arXiv 2020年

作者： Cygan, Wojciech Sandrić, Nikola Šebek, Stjepan Institut für Mathematische Stochastik Technische Universität Dresden Dresden Germany & Instytut Matematyczny Uniwersytet Wroc lawski Wroc law Poland Department of Mathematics University of Zagreb Zagreb Croatia Institute of Discrete Mathematics Graz University of Technology Graz Austria & Department of Applied Mathematics Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

In this article, we study fluctuations of the volume of a stable sausage defined via a d-dimensional rotationally invariant α-stable process with d > 3α/2, and a closed unit ball. As the main results, we establish a functional central limit theorem with a standard one-dimensional Brownian motion in the limit, and an almost sure invariance principle for the process of the volume of a stable sausage. As a consequence, we obtain Khintchine’s and Chung’s laws of the iterated logarithm for this *** Codes 60F05, 60G52, 60F17 Copyright © 2020, The Authors. All rights reserved.

关键词： Stability criteria

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The role of Artificial Intelligence (AI) in Adaptive eLearning System (AES) content formation: Risks and opportunities involved

arXiv

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arXiv 2019年

作者： Adamu, Suleiman Awwalu, Jamilu Information and Communications Technology Unit Sule Lamido University Kafin Hausa Jigawa State Nigeria Department of Computer Science Faculty of Computing and Applied Sciences Baze University Abuja Nigeria

Artificial Intelligence (AI) plays varying roles in supporting both existing and emerging technologies. In the area of Learning and Tutoring, it plays key role in Intelligent Tutoring Systems (ITS). The fusion of ITS with Adaptive Hypermedia and Multimedia (AHAM) form the backbone of Adaptive eLearning Systems (AES) which provides personalized experiences to learners. This experience is important because it facilitates the accurate delivery of the learning modules in specific to the learner's capacity and readiness. AES types vary, with Adaptive Web Based eLearning Systems (AWBES) being the popular type because of wider access offered by the web *** retrieval and aggregation ofcontents for any eLearning system is critical whichis determined by the relevance of learning material to the needs of the *** this paper, we discuss components of AES, role of AI in AES content aggregation, possible risks and available opportunities. Copyright © 2019, The Authors. All rights reserved.

关键词： Computer aided instruction

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Monitoring the daily variation of Sun-Earth magnetic fields using galactic cosmic rays

arXiv

引用

arXiv 2024年

作者： Cao, Zhen Aharonian, F. Axikegu Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Bian, W. Bukevich, A.V. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, H.X. Chen, Liang Chen, Lin Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, J.H. Fang, K. Feng, C.F. Feng, H. Feng, L. Feng, S.H. Feng, X.T. Feng, Y. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. Hasan, M. He, H.H. He, H.N. He, J.Y. He, Y. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S.C. Huang, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Ji, X.L. Jia, H.Y. Jia, K. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Karpikov, I. Kuleshov, D. Kurinov, K. Li, B.B. Li, C.M. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, Jian Li, Jie Li, K. Li, S.D. Li, W.L. Li, W.L. Li, X.R. Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, D.B. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Luo, Q. Luo, Y. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mu, H.J. Nan, Y.C. Neronov, A. Ou, L.J. Pattarakijwanich, P. Pei, Z.Y. Qi, J.C. Qi, M.Y. Qiao, B.Q. Qin, J.J. Raza, A. Ruffolo, D. S'aiz, A. Saeed, M. Semikoz, D. Shao, L. Shchegolev, O. Sheng, X.D. Shu, F.W. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, D.X. University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China Dublin Institute for Advanced Studies 31 Fitzwilliam Place 2 Dublin Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Institute for Nuclear Research Russian Academy of Sciences Moscow117312 Russia Hebei Normal University Hebei Shijiazhuang050024 China University of Science and Technology of China Anhui Hefei230026 China State Key Laboratory of Particle Detection and Electronics China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Research Center for Astronomical Computing Zhejiang Laboratory Zhejiang Hangzhou311121 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Key Laboratory of Cosmic Rays Tibet University Ministry of Education Tibet Lhasa850000 China Key Laboratory of Radio Astronomy and Technology National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University Guangdong Zhuhai51900 China Sun Yat-sen University Guangdong Guangzhou510275 China Institute of Frontier and Interdisciplinary Science Shandong University Shandong Qingdao266237 China APC Universit'e Paris Cit'e CNRS IN2P3 CEA IRFU Observatoire de Paris Paris11

The interplanetary magnetic field (IMF) between the Sun and Earth is an extension of the solar magnetic field carried by the solar wind into interplanetary space. Monitoring variations in the IMF upstream of the Earth would provide very important information for the prediction of space weather effects, such as effects of solar storms and the solar wind, on human activity. In this study, the IMF between the Sun and Earth was measured daily for the first time using a cosmic-ray observatory. Cosmic rays mainly consist of charged particles that are deflected as they pass through a magnetic field. Therefore, the cosmic-ray Sun shadow, caused by high-energy charged cosmic rays blocked by the Sun and deflected by the magnetic field, can be used to explore the transverse IMF between the Sun and Earth. By employing the powerful kilometer-square array at the Large High Altitude Air Shower Observatory, the cosmic-ray Sun shadows were observed daily with high significance for the first time. The displacement of the Sun shadow measured in 2021 correlates well with the transverse IMF component measured in situ by spacecraft near the Earth, with a time lag of 3:31 ± 0:12 days. The displacement of the Sun shadow was also simulated using Parker’s classic IMF model, yielding a time lag of 2:06 ± 0:04 days. This deviation may provide valuable insights into the magnetic field structure, which can improve space weather research. Copyright © 2024, The Authors. All rights reserved.

关键词： Cosmology

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Historical tree phenology data reveal the seasonal rhythms of the Congo Basin rainforest

Plant-Environment Interactions

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Plant-Environment Interactions 2024年第2期5卷 e10136页

作者： Kearsley, Elizabeth Verbeeck, Hans Stoffelen, Piet Janssens, Steven B. Yakusu, Emmanuel Kasongo Kosmala, Margaret De Mil, Tom Bauters, Marijn Kitima, Elasi Ramanzani Ndiapo, José Mbifo Chuda, Adelard Lonema Richardson, Andrew D. Wingate, Lisa Ilondea, Bhély Angoboy Beeckman, Hans van den Bulcke, Jan Boeckx, Pascal Hufkens, Koen Computational and Applied Vegetation Ecology Lab Department of Environment Faculty of Bioscience Engineering Ghent University Gent Belgium BlueGreen Labs Melsele Belgium Meise Botanic Garden Meise Belgium Department of Biology Leuven Plant Institute KULeuven Leuven Belgium UGent-Woodlab (Laboratory of Wood Technology) Department of Environment Faculty of Bioscience Engineering Ghent University Gent Belgium Service of Wood Biology Royal Museum for Central Africa Tervuren Belgium Faculté de gestion des ressources naturelles renouvelables Université de Kisangani Kisangani Democratic Republic Congo Department of Organismic and Evolutionary Biology Harvard University Cambridge MA United States CIBO Technologies Cambridge MA United States Forest is Life TERRA Teaching and Research Centre Gembloux Agro Bio-Tech University of Liège Gembloux Belgium Isotope Bioscience Laboratory - ISOFYS Department of Green Chemistry and Technology Ghent University Gent Belgium Research Group of Plants and Ecosystems (PLECO) Department of Biology University of Antwerp Wilrijk Belgium Institut National pour l'Etude et la Recherche Agronomiques-INERA Yangambi Democratic Republic Congo Center for Ecosystem Science and Society Northern Arizona University Flagstaff AZ United States School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff AZ United States INRAE UMR ISPA Villenave d'Ornon France Institut National pour l'Étude et la Recherche Agronomiques Kinshasa Democratic Republic Congo

Tropical forest phenology directly affects regional carbon cycles, but the relation between species-specific and whole-canopy phenology remains largely uncharacterized. We present a unique analysis of historical tropical tree phenology collected in the central Congo Basin, before large-scale impacts of human-induced climate change. Ground-based long-term (1937–1956) phenological observations of 140 tropical tree species are recovered, species-specific phenological patterns analyzed and related to historical meteorological records, and scaled to characterize stand-level canopy dynamics. High phenological variability within and across species and in climate–phenology relationships is observed. The onset of leaf phenophases in deciduous species was triggered by drought and light availability for a subset of species and showed a species-specific decoupling in time along a bi-modal seasonality. The majority of the species remain evergreen, although central African forests experience relatively low rainfall. Annually a maximum of 1.5% of the canopy is in leaf senescence or leaf turnover, with overall phenological variability dominated by a few deciduous species, while substantial variability is attributed to asynchronous events of large and/or abundant trees. Our results underscore the importance of accounting for constituent signals in canopy-wide scaling and the interpretation of remotely sensed phenology signals. © 2024 The Authors. Plant-Environment Interactions published by New Phytologist Foundation and John Wiley & Sons Ltd.

关键词： leaf senescence leaf turnover phenology scaling tropical forest

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Transfer function analysis of dynamic cerebral autoregulation: A CARNet white paper 2022 update

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Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism 2023年第1期43卷 3-25页

作者： Ronney B Panerai Patrice Brassard Joel S Burma Pedro Castro Jurgen Ahr Claassen Johannes J van Lieshout Jia Liu Samuel Je Lucas Jatinder S Minhas Georgios D Mitsis Ricardo C Nogueira Shigehiko Ogoh Stephen J Payne Caroline A Rickards Andrew D Robertson Gabriel D Rodrigues Jonathan D Smirl David M Simpson Department of Cardiovascular Sciences University of Leicester and NIHR Biomedical Research Centre Leicester UK. Department of Kinesiology Faculty of Medicine and Research Center of the Institut universitaire de cardiologie et de pneumologie de Québec Université Laval Québec QC Canada. Faculty of Kinesiology University of Calgary Calgary AB Canada. Department of Neurology Centro Hospitalar Universitário de São João Faculty of Medicine University of Porto Porto Portugal. Department of Geriatric Medicine and Donders Institute for Brain Cognition and Behaviour Radboud University Nijmegen The Netherlands. Department of Internal Medicine Amsterdam UMC The Netherlands and Division of Physiology Pharmacology and Neuroscience School of Life Sciences University of Nottingham Medical School Queen's Medical Centre UK. Institute of Advanced Computing and Digital Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen University Town Shenzhen China. School of Sport Exercise and Rehabilitation Sciences and Centre for Human Brain Health University of Birmingham Birmingham UK. Department of Bioengineering McGill University Montreal Québec QC Canada. Neurology Department School of Medicine Hospital das Clinicas University of São Paulo São Paulo Brazil. Department of Biomedical Engineering Toyo University Kawagoe-Shi Saitama Japan. Institute of Applied Mechanics National Taiwan University Taipei. Department of Physiology & Anatomy University of North Texas Health Science Center Fort Worth Texas USA. Department of Kinesiology and Health Sciences University of Waterloo Waterloo ON Canada. Department of Clinical Sciences and Community Health University of Milan Milan Italy. Institute of Sound and Vibration Research University of Southampton Southampton UK.

Cerebral autoregulation (CA) refers to the control of cerebral tissue blood flow (CBF) in response to changes in perfusion pressure. Due to the challenges of measuring intracranial pressure, CA is often described as the relationship between mean arterial pressure (MAP) and CBF. Dynamic CA (dCA) can be assessed using multiple techniques, with transfer function analysis (TFA) being the most common. A 2016 white paper by members of an international Cerebrovascular Research Network (CARNet) that is focused on CA strove to improve TFA standardization by way of introducing data acquisition, analysis, and reporting guidelines. Since then, additional evidence has allowed for the improvement and refinement of the original recommendations, as well as for the inclusion of new guidelines to reflect recent advances in the field. This second edition of the white paper contains more robust, evidence-based recommendations, which have been expanded to address current streams of inquiry, including optimizing MAP variability, acquiring CBF estimates from alternative methods, estimating alternative dCA metrics, and incorporating dCA quantification into clinical trials. Implementation of these new and revised recommendations is important to improve the reliability and reproducibility of dCA studies, and to facilitate inter-institutional collaboration and the comparison of results between studies.

关键词： Cerebral hemodynamics consensus guidelines reference values transfer function analysis

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Correction to: Comparative analysis of metaheuristic load balancing algorithms for efficient load balancing in cloud computing

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Journal of Cloud computing 2025年第1期14卷 1-1页

作者： Zhou, Jincheng Lilhore, Umesh Kumar M, Poongodi Hai, Tao Simaiya, Sarita Abang Jawawi, Dayang Norhayati Alsekait, Deema Mohammed Ahuja, Sachin Biamba, Cresantus Hamdi, Mounir School of Computer and Information and Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Qiannan Normal University for Nationalities Duyun China Department of Computer Science and Engineering Chandigarh University Mohali India College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar School of Computing Faculty of Engineering Universiti Teknologi Malaysia (UTM) Johor Malaysia Department of Computer Science and Information Technology Princess Nourah Bint Abdul Rahman University Applied College Riyadh Saudi Arabia Department of Culture Studies Religious Studies and Educational Sciences University of Gävle Gävle Sweden

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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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