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arXiv 2024年

作者： Kyrimi, Evangelia McLachlan, Scott Wohlgemut, Jared M. Perkins, Zane B. Lagnado, David A. Marsh, William Gimson, Alexander Shafti, Ali Ercole, Ari Banerjee, Amitava Glocker, Ben Schafer, Burkhard Gatsonis, Constantine Grosan, Crina Sent, Danielle Berman, David S. Glass, David O'Regan, Declan P. Letsios, Dimitrios Morrissey, Dylan Pisirir, Erhan Leofante, Francesco Soyel, Hamit Williamson, Jon Grieman, Keri Dube, Kudakwashe Mardsen, Max Nagendran, Myura Tai, Nigel Kostopoulou, Olga Jones, Owain Curzon, Paul Stoner, Rebecca S. Tandle, Sankalp Joshi, Shalmali Mossadegh, Somayyeh Buijsman, Stefan Miller, Tim Madai, Vince Istvan School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom School of Nursing Midwifery and Palliative Care King's College London London United Kingdom Centre for Trauma Sciences Blizard Institute Queen Mary University of London London United Kingdom Royal London Hospital Barts Health NHS Trust London United Kingdom Department of Experiment Psychology University College London London United Kingdom Cambridge Liver Unit Addenbrooke's Hospital Cambridge University Hospitals Hills Road Box 210 Cambridge United Kingdom University of Cambridge United Kingdom Cambridge Consultants Ltd. United Kingdom Division of Anaesthesia Cambridge Centre for AI in Medicine University of Cambridge Cambridge United Kingdom Magdelene College University of Cambridge United Kingdom Institute of Health Informatics University College London London United Kingdom Department of Computing Imperial College London London United Kingdom SCRIPT Centre for IT and IP Law School of Law University of Edinburgh United Kingdom Center for Statistical Sciences School of Public Health Brown University Providence United States Research Group Artificial Intelligence HU University of Applied Sciences Utrecht Netherlands Jheronimus Academy of Data Science Tilburg University Eindhoven University of Technology s-Hertogenbosch Netherlands Centre for Theoretical Physics Queen Mary University of London London United Kingdom School of Computing Ulster University Belfast United Kingdom MRC Laboratory of Medical Sciences Imperial College London London United Kingdom Department of Informatics King's College London United Kingdom Physiotherapy Department Barts Health NHS Trust London United Kingdom Sport and Exercise Medicine Queen Mary University of London London United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Computing Imperial College London United Kingdom Departmen

Proposals of artificial intelligence (AI) solutions based on increasingly complex and accurate predictive models are becoming ubiquitous across many disciplines. As the complexity of these models grows, transparency and users' understanding often diminish. This suggests that accurate prediction alone is insufficient for making an AI-based solution truly useful. In the development of healthcare systems, this introduces new issues related to accountability and safety. Understanding how and why an AI system makes a recommendation may require complex explanations of its inner workings and reasoning processes. Although research on explainable AI (XAI) has significantly increased in recent years and there is high demand for XAI in medicine, defining what constitutes a good explanation remains ad hoc, and providing adequate explanations continues to be challenging. To fully realize the potential of AI, it is critical to address two fundamental questions about explanations for safety-critical AI applications, such as health-AI: (1) What is an explanation in health-AI? and (2) What are the attributes of a good explanation in health-AI? In this study, we examined published literature and gathered expert opinions through a two-round Delphi study. The research outputs include (1) a definition of what constitutes an explanation in health-AI and (2) a comprehensive list of attributes that characterize a good explanation in health-AI. © 2024, CC BY.

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Challenges and Best Practices in Ethical Review of Human and Organizational Factors Studies in Health Technology: a Synthesis of Testimonies

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Yearbook of Medical informatics 2020年第1期29卷 58-70页

作者： Peute, Linda W. Lichtner, Valentina Baysari, Melissa T. Hägglund, Maria Homco, Juell Jansen-Kosterink, Stephanie Jauregui, Ignacio Kaipio, Johanna Kuziemsky, Craig E. Lehnbom, Elin Christina Leite, Francisca Lesselroth, Blake Luna, Daniel Otero, Carlos Pedersen, Rune Pelayo, Sylvia Santos, Raquel Silva, Nuno-André Tyllinen, Mari Van Velsen, Lex Zheng, Wu Yi Jaspers, Monique Marcilly, Romaric Centre for Human Factor Engineering of Health Information technology - Amsterdam UMC University of Amsterdam department of Medical Informatics Amsterdam Netherlands Centre for Medication Safety and Service Quality UCL School of Pharmacy United Kingdom University of Sydney Faculty of Medicine and Health Sydney Australia Beth Israel Deaconess Medical Center Harvard Medical School Boston MA USA Department of Women's and Children's Health Uppsala University Uppsala Sweden Department of Medical Informatics University of Oklahoma - Tulsa School of Community Medicine United States eHealth group Roessingh Research and Development Enschede Netherlands Health Informatics Department Hospital Italiano de Buenos Aires Argentina Department of Computer Science Aalto University Finland MacEwan University AB Edmonton Canada Department of Pharmacy Faculty of Health Sciences UiT The Arctic University of Norway Norway Department of Health and Caring Sciences Faculty of Health and Life Sciences Linnaeus University Sweden Hospital da Luz Learning Health Portugal Norwegian Centre for E-health Research University Hospital of North Norway HF Norway Department of Clinical Medicine Faculty of Health Sciences UiT The Arctic University of Norway Norway Univ. Lille ULR 2694 - METRICS: Évaluation des technologies de santé et des pratiques médicales CHU Lille Lille France

Objective: Human and Organizational Factors (HOF) studies in health technology involve human beings and thus require Institutional Review Board (IRB) approval. Yet HOF studies have specific constraints and methods that may not fit standard regulations and IRB practices. Gaining IRB approval may pose difficulties for HOF researchers. This paper aims to provide a first overview of HOF study challenges to get IRB review by exploring differences and best practices across different countries. Methods: HOF researchers were contacted by email to provide a testimony about their experience with IRB review and approval. Testimonies were thematically analyzed and synthesized to identify and discuss shared themes. Results: Researchers from seven European countries, Argentina, Canada, Australia, and the United States answered the call. Four themes emerged that indicate shared challenges in legislation, IRB inefficiencies and inconsistencies, general regulation and costs, and lack of HOF study knowledge by IRB members. We propose a model for IRB review of HOF studies based on best practices. Conclusion: International criteria are needed that define low and high-risk HOF studies, to allow identification of studies that can undergo an expedited (or exempted) process from those that need full IRB review. Enhancing IRB processes in such a way would be beneficial to the conduct of HOF studies. Greater knowledge and promotion of HOF methods and evidence-based HOF study designs may support the evolving discipline. Based on these insights, training and guidance to IRB members may be developed to support them in ensuring that appropriate ethical issues for HOF studies are considered. © 2020 IMIA and Georg Thieme Verlag KG.

关键词： ergonomics Ethical review human factors organizational factors social sciences

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Evaluating deep transfer learning for whole-brain cognitive decoding

arXiv

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arXiv 2021年

作者： Thomas, Armin W. Lindenberger, Ulman Samek, Wojciech Müller, Klaus-Robert Machine Learning Group Dept. of Computer Science and Electrical Engineering Technische Universität Berlin Berlin Germany Center for Lifespan Psychology Max Planck Institute for Human Development Berlin Germany Stanford Data Science Stanford University StanfordCA United States Dept. of Psychology Stanford University StanfordCA United States Max Planck UCL Centre for Computational Psychiatry and Ageing Research Berlin Germany Dept. of Artificial Intelligence Fraunhofer Heinrich Hertz Institute Berlin Germany BIFOLD – Berlin Institute for the Foundations of Learning and Data Berlin Germany Dept. of Artificial Intelligence Korea University Seoul Korea Republic of Max Planck Institute for Informatics Saarbrücken Germany

Research in many fields has shown that transfer learning (TL) is well-suited to improve the performance of deep learning (DL) models in datasets with small numbers of samples. This empirical success has triggered interest in the application of TL to cognitive decoding analyses with functional neuroimaging data. Here, we systematically evaluate TL for the application of DL models to the decoding of cognitive states (e.g., viewing images of faces or houses) from whole-brain functional Magnetic Resonance Imaging (fMRI) data. We first pre-train two DL architectures on a large, public fMRI dataset and subsequently evaluate their performance in an independent experimental task and a fully independent dataset. The pre-trained models consistently achieve higher decoding accuracies and generally require less training time and data than model variants that were not pre-trained, clearly underlining the benefits of pre-training. We demonstrate that these benefits arise from the ability of the pre-trained models to reuse many of their learned features when training with new data, providing deeper insights into the mechanisms giving rise to the benefits of pre-training. Yet, we also surface nuanced challenges for whole-brain cognitive decoding with DL models when interpreting the decoding decisions of the pre-trained models, as these have learned to utilize the fMRI data in unforeseen and counterintuitive ways to identify individual cognitive states. © 2021, CC BY.

关键词： Decoding

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Eye tracking: empirical foundations for a minimal reporting guideline

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Behavior research methods 2023年第1期55卷 364-416页

作者： Kenneth Holmqvist Saga Lee Örbom Ignace T C Hooge Diederick C Niehorster Robert G Alexander Richard Andersson Jeroen S Benjamins Pieter Blignaut Anne-Marie Brouwer Lewis L Chuang Kirsten A Dalrymple Denis Drieghe Matt J Dunn Ulrich Ettinger Susann Fiedler Tom Foulsham Jos N van der Geest Dan Witzner Hansen Samuel B Hutton Enkelejda Kasneci Alan Kingstone Paul C Knox Ellen M Kok Helena Lee Joy Yeonjoo Lee Jukka M Leppänen Stephen Macknik Päivi Majaranta Susana Martinez-Conde Antje Nuthmann Marcus Nyström Jacob L Orquin Jorge Otero-Millan Soon Young Park Stanislav Popelka Frank Proudlock Frank Renkewitz Austin Roorda Michael Schulte-Mecklenbeck Bonita Sharif Frederick Shic Mark Shovman Mervyn G Thomas Ward Venrooij Raimondas Zemblys Roy S Hessels Department of Psychology Nicolaus Copernicus University Torun Poland. kenneth.holmqvist@ur.de. Department of Computer Science and Informatics University of the Free State Bloemfontein South Africa. kenneth.holmqvist@ur.de. Department of Psychology Regensburg University Regensburg Germany. kenneth.holmqvist@ur.de. Department of Psychology Regensburg University Regensburg Germany. Experimental Psychology Helmholtz Institute Utrecht University Utrecht The Netherlands. Lund University Humanities Lab and Department of Psychology Lund University Lund Sweden. Department of Ophthalmology SUNY Downstate Health Sciences University Brooklyn NY USA. Tobii Pro AB Danderyd Sweden. Social Health and Organizational Psychology Utrecht University Utrecht The Netherlands. Department of Computer Science and Informatics University of the Free State Bloemfontein South Africa. TNO Soesterberg The Netherlands. Department of Ergonomics Leibniz Institute for Working Environments and Human Factors Dortmund Germany. Institute of Informatics LMU Munich Munich Germany. Institute of Child Development University of Minnesota Minneapolis USA. School of Psychology University of Southampton Southampton UK. School of Optometry and Vision Sciences Cardiff University Cardiff UK. Department of Psychology University of Bonn Bonn Germany. Vienna University of Economics and Business Vienna Austria. Department of Psychology University of Essex Essex UK. Department of Neuroscience Erasmus MC Rotterdam The Netherlands. Machine Learning Group Department of Computer Science IT University of Copenhagen Copenhagen Denmark. SR Research Ltd Ottawa Canada. Human-Computer Interaction University of Tübingen Tübingen Germany. University of British Columbia Columbia Canada. Department of Eye and Vision Science Institute of Life Course and Medical Sciences University of Liverpool Liverpool UK. Department of Education and Pedagogy Division Education Faculty of Social and Behavioral Sciences Ut

In this paper, we present a review of how the various aspects of any study using an eye tracker (such as the instrument, methodology, environment, participant, etc.) affect the quality of the recorded eye-tracking data and the obtained eye-movement and gaze measures. We take this review to represent the empirical foundation for reporting guidelines of any study involving an eye tracker. We compare this empirical foundation to five existing reporting guidelines and to a database of 207 published eye-tracking studies. We find that reporting guidelines vary substantially and do not match with actual reporting practices. We end by deriving a minimal, flexible reporting guideline based on empirical research (Section "An empirically based minimal reporting guideline").

关键词： Data quality Eye movements Eye tracking Replicability Reporting guidelines Reporting practices Reporting standards Reproducibility

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Challenges in cybersecurity: Lessons from biological defense systems

arXiv

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arXiv 2021年

作者： Schrom, Edward Kinzig, Ann Forrest, Stephanie Graham, Andrea L. Levin, Simon A. Bergstrom, Carl T. Castillo-Chavez, Carlos Collins, James P. De Boer, Rob J. Doupé, Adam Ensafi, Roya Feldman, Stuart Grenfell, Bryan T. Halderman, J. Alex Huijben, Silvie Maley, Carlo Moses, Melanie Perelson, Alan S. Perrings, Charles Plotkin, Joshua Rexford, Jennifer Tiwari, Mohit Department of Ecology and Evolutionary Biology Princeton University 106A Guyot Hall PrincetonNJ08544 United States School of Life Sciences Arizona State University TempeAZ85287 United States Julie Ann Wrigley Global Institute of Sustainability Arizona State University TempeAZ85287 United States Biodesign Center for Biocomputation Security and Society Arizona State University TempeAZ85287 United States School of Computing Informatics and Decision Sciences Engineering Arizona State University TempeAZ85287 United States Santa Fe Institute Santa FeNM87501 United States Department of Biology University of Washington SeattleWA98195 United States Arizona State University TempeAZ85287 United States Theoretical Biology and Bioinformatics Utrecht University Utrecht3584 CH Netherlands Center for Cybersecurity and Digital Forensics Global Security Initiative Arizona State University TempeAZ85287 United States Department of Electrical Engineering and Computer Science Computer Science and Engineering Division University of Michigan Ann ArborMI48109 United States Schmidt Futures New YorkNY10011 United States Princeton School of Public and International Affairs Princeton University PrincetonNJ08544 United States Center for Computer Security and Society University of Michigan Ann ArborMI48109 United States Center for Evolution and Medicine School of Life Sciences Arizona State University TempeAZ85287 United States Arizona Cancer Evolution Center Arizona State University TempeAZ85287 United States Department of Computer Science University of New Mexico AlbuquerqueNM87131 United States University of New Mexico Department of Computer Science AlbuquerqueNM87131 United States Department of Biology University of New Mexico AlbuquerqueNM87131 United States Theoretical Biology and Biophysics Group Los Alamos National Laboratory Los AlamosNM87545 United States Department of Biology University of Pennsylvania PhiladelphiaPA19104 Unite

We explore the commonalities between methods for assuring the security of computer systems (cybersecurity) and the mechanisms that have evolved through natural selection to protect vertebrates against pathogens, and how insights derived from studying the evolution of natural defenses can inform the design of more effective cybersecurity systems. More generally, security challenges are crucial for the maintenance of a wide range of complex adaptive systems, including financial systems, and again lessons learned from the study of the evolution of natural defenses can provide guidance for the protection of such *** Codes A.0. Copyright © 2021, The Authors. All rights reserved.

关键词： Cybersecurity

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Approved trends and product characteristics of digital therapeutics in four countries

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NPJ digital medicine 2025年第1期8卷 308页

作者： Jun Liang Qichuan Fang Xiaoyi Jiao Peng Xiang Junhao Ma Zijiao Zhang Yongcheng Liu Yunfan He Yingjun Li Zhixu He Jianbo Lei Department of AI and IT Second Affiliated Hospital School of Medicine Zhejiang University Hangzhou China. School of Medical Technology and Information Engineering Zhejiang Chinese Medical University Hangzhou China. Center for Health Policy Studies School of Public Health Zhejiang University Hangzhou China. National Key Laboratory of Transvascular Implantable Devices Second Affiliated Hospital School of Medicine Zhejiang University Hangzhou China. Intelligent Medical Research Center Zhejiang University Institute of Computer Innovation Technology Hangzhou China. Asian Section Digital Therapeutics Alliance Hangzhou China. Department of Epidemiology and Health Statistics School of Public Health Hangzhou Medical College Hangzhou China. School of Public Health Southwest Medical University Luzhou China. NetEase ThunderFire UX User Experience Center Netease Group Hangzhou China. School of International Relations and Public Affairs Fudan University Shanghai China. Center of Tissue Engineering and Stem Cell Research Guizhou Medical University Department of Pediatric Hematology The Affiliated Hospital of Guizhou Medical University Guiyang China. hezhixu2024@***. Clinical Research Center Affiliated Hospital of Southwest Medical University Luzhou China. jblei@***. School of Medical Information and Engineering Southwest Medical University Luzhou China. jblei@***. Center for Medical Informatics Institute of Advanced Clinical Medicine Peking University Beijing China. jblei@***.

Digital therapeutics (DTx) provide innovative treatment options. In this study, we identified and compared approved DTx in China, the US, Germany, and Belgium. In total, 507 DTx applications were identified (US, 192; China, 235; Germany, 55; Belgium, 25). China has approved the most, whereas the US initiated approval as early as 2017. In China, most DTx applications focus on disease treatment (88.09%), particularly for neurological (45.96%) and ophthalmic (21.28%) diseases. The US and Belgium emphasize disease management (US: 52.60%, Belgium: 72.00%), whereas Germany focuses on the treatment of mental and behavioral disorders (47.27%). Regarding therapeutic approaches, China's DTx predominantly utilizes computerized cognitive correction (26.38%), whereas the US and Belgium primarily rely on health status monitoring (US, 44.27%; Belgium, 80.00%). Germany's DTx primarily employs cognitive behavioral therapy (69.09%). An online database was established to ensure ease of access. Future studies should focus on generating further evidence to support the broader adoption of DTx.

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Healthy Vessel Wall Detection Using U-Net in Optical Coherence Tomography 1st

Healthy Vessel Wall Detection Using U-Net in Optical Coheren...

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1st International Workshop on Machine Learning and Medical engineering for Cardiovasvular Healthcare, MLMECH 2019, and the 8th International Joint Workshops on Computing and Visualization for Intravascular Imaging and computer Assisted Stenting, CVII-STENT 2019, held in conjunction with 22nd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2019

作者： Liu, Shengnan Shamonin, Denis P. Zahnd, Guillaume van der Steen, A.F.W. van Walsum, Theo van Soest, Gijs Department of Biomedical Engineering Erasmus MC Dr. Molewaterplein 40 Rotterdam3015 GD Netherlands Medical Image Processing Department Leiden University Medical Center Leiden Netherlands Computer Aided Medical Procedures Technische Universität München Boltzmannstraße 3 Garching bei München85748 Germany Biomedical Imaging Group Rotterdam Department of Medical Informatics and Radiology Erasmus MC Dr. Molewaterplein 40 Rotterdam3015 GD Netherlands

ISBN: (纸本)9783030333263

Intravascular optical coherence tomography can be applied for high-resolution imaging in the coronary arteries with ischemic heart disease. The differentiation of the healthy and diseased vessel wall can be used to assess the extent and severity of coronary artery disease, and to guide therapeutic interventions. The aim of this study is to develop a recognition framework that can be potentially used for real-time intraoperative application. Structures in an image were labeled into five categories: diseased, healthy, luminal, guide-wire, and others. A U-net was implemented to directly take Cartesian images as input without any additional processing steps. A sigmoid activation and binary cross-entropy loss were applied to perform multi-labeling segmentation. Three transformations were specifically proposed in the polar domain for data augmentation. For evaluation of the proposed framework, 200 images from 20 patients were used and a triple-leave-2-out cross validation was carried out. Performance was evaluated using the average loss in the validation dataset, and the Dice scores were reported as well. Results showed that the proposed framework can perform the segmentation generally with an average performance of 0.88 ± 0.02 in Dice scores. These preliminary results suggest that the proposed framework can be potentially applied for assisting diagnosis in real-time. In the future, we intend to include more data, also take into consideration artifacts such as bad flushing, more deformed lumen, and side-branches. © 2019, Springer Nature Switzerland AG.

关键词： Optical tomography

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Benchmarking quality-dependent and cost-sensitive score-level multimodal biometric fusion algorithms

arXiv

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arXiv 2021年

作者： Poh, Norman Bourlai, Thirimachos Kittler, Josef Allano, Lorene Alonso-Fernandez, Fernando Ambekar, Onkar Baker, John Dorizzi, Bernadette Fatukasi, Omolara Fierrez, Julian Ganster, Harald Ortega-Garcia, Javier Maurer, Donald Salah, Albert Ali Scheidat, Tobias Vielhauer, Claus Centre for Vision Speech and Signal Processing School of Electronics and Physical Sciences University of Surrey Guildford SurreyGU2 7XH United Kingdom Biometrics Center Lane Department of Computer Science and Electrical Engineering College of Engineering and Mineral Resources West Virginia University MorgantownWV26506-6109 United States Institut Telecom Telecom and Management SudParis Evry91011 France Biometric Recognition Group – ATVS Escuela Politecnica Superior Universidad Autonoma de Madrid Madrid28049 Spain Amsterdam1098 XG Netherlands Applied Physics Laboratory Johns Hopkins University LaurelMD20723 United States Electronics and Physics Department Institut Telecom Telecom and Management SudParis Evry91011 France Institute of Digital Image Processing Joanneum Research Graz8010 Austria ISLA-ISIS University of Amsterdam Amsterdam1098 XG Netherlands Department of Informatics and Media Brandenburg University of Applied Sciences Brandenburg an der HavelD-14770 Germany Research Group Multimedia and Security Department of Technical and Business Information Systems Faculty of Computer Science Otto-von-Guericke-University of Magdeburg MagdeburgD-39106 Germany

Automatically verifying the identity of a person by means of biometrics (e.g., face and fingerprint) is an important application in our day-to-day activities such as accessing banking services and security control in airports. To increase the system reliability, several biometric devices are often used. Such a combined system is known as a multimodal biometric system. This paper reports a benchmarking study carried out within the framework of the BioSecure DS2 (Access Control) evaluation campaign organized by the University of Surrey, involving face, fingerprint, and iris biometrics for person authentication, targeting the application of physical access control in a medium-size establishment with some 500 persons. While multimodal biometrics is a well-investigated subject in the literature, there exists no benchmark for a fusion algorithm comparison. Working towards this goal, we designed two sets of experiments: quality-dependent and cost-sensitive evaluation. The quality-dependent evaluation aims at assessing how well fusion algorithms can perform under changing quality of raw biometric images principally due to change of devices. The cost-sensitive evaluation, on the other hand, investigates how well a fusion algorithm can perform given restricted computation and in the presence of software and hardware failures, resulting in errors such as failure-to-acquire and failure-to-match. Since multiple capturing devices are available, a fusion algorithm should be able to handle this nonideal but nevertheless realistic scenario. In both evaluations, each fusion algorithm is provided with scores from each biometric comparison subsystem as well as the quality measures of both the template and the query data. The response to the call of the evaluation campaign proved very encouraging, with the submission of 22 fusion systems. To the best of our knowledge, this campaign is the first attempt to benchmark quality-based multimodal fusion algorithms. In the presence of changing

关键词： Benchmarking

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Development of mobile-based application for practical wood identification

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IOP Conference Series: Earth and Environmental Science 2020年第1期572卷

作者： M R Arifin E P B Sugiarto R Wardoyo Y Rianto Computer Vision Research Group Research Center for Informatics Indonesian Institute of Sciences (LIPI) Bandung Indonesia Department of Informatics Faculty of Engineering Universitas Sangga Buana YPKP Bandung Indonesia Center for The Utilization and Innovation of Science and Technology Indonesian Institute of Sciences (LIPI) Cibinong Indonesia

Wood Identification is an important task, especially for wood anatomist. The identification process is required in many fields, such as custom ports, forest survey, and wood industries. However, expertise-in wood identification is limited due to a few wood anatomists. To enable wood identification can be performed in a broader area, a mobile-based application is proposed to realise these purposes. The paper presents the development of a mobile-based application for wood identification. A dataset of Indonesian commercial wood images of cross-section surface was collected from Xylarium Bogoriense, The Ministry of Environment and Forestry of The Republic of Indonesia. The images were collected using a smartphone camera at optical magnification level around 200 times and a minimum resolution of 12 megapixels. The collected images were then used to develop a deep learning-based algorithm to classify wood species. These images are considered as the training datasets, whereas testing dataset is collected in another session. The final model obtained through the training process is then stored in a cloud-based server at Research center for informatics, Indonesian Institute of Sciences. A mobile application, namely AIKO stands of Indonesian words-Alat Identifikasi Kayu Otomatis (Automatic Tool for Wood Identification), is developed to enable image acquisition and images transferring to the server. AIKO application sends the acquired image to the trained identification model in the server. The output of the identification model is the species name of the observed images. Besides species/botanical name, AIKO also provides information on trade names, specific gravity and density, durability class, strength class, commercial timber classification, and recommended utilization.

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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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