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arXiv 2023年

作者： Ning, Yilin Teixayavong, Salinelat Shang, Yuqing Savulescu, Julian Nagaraj, Vaishaanth Miao, Di Mertens, Mayli Wei Ting, Daniel Shu Ling Ong, Jasmine Chiat Liu, Mingxuan Cao, Jiuwen Dunn, Michael Vaughan, Roger Hock Ong, Marcus Eng Sung, Joseph Jao-Yiu Topol, Eric J. Liu, Nan Centre for Quantitative Medicine Duke-NUS Medical School Singapore Singapore Centre for Biomedical Ethics National University of Singapore Singapore Singapore Wellcome Centre for Ethics and Humanities University of Oxford Oxford United Kingdom School of Medicine Imperial College London London United Kingdom Centre for Ethics Department of Philosophy University of Antwerp Antwerp Belgium Antwerp Center on Responsible AI University of Antwerp Antwerp Belgium Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore SingHealth AI Office Singapore Health Services Singapore Singapore Division of Pharmacy Singapore General Hospital Singapore Singapore Machine Learning and I-Health International Cooperation Base of Zhejiang Province Hangzhou Dianzi University Zhejiang China Artificial Intelligence Institute Hangzhou Dianzi University Zhejiang China Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Singapore Department of Emergency Medicine Singapore General Hospital Singapore Singapore Lee Kong Chian School of Medicine Nanyang Technological University Singapore Singapore State Key Laboratory of Digestive Disease The Chinese University of Hong Kong Hong Kong Scripps Research Translational Institute Scripps Research La Jolla CA United States Institute of Data Science National University of Singapore Singapore Singapore Centre for Quantitative Medicine Duke-NUS Medical School 8 College Road Singapore169857 Singapore

The widespread use of ChatGPT and other emerging technology powered by generative artificial intelligence (GenAI) has drawn much attention to potential ethical issues, especially in high-stakes applications such as healthcare, but ethical discussions are yet to translate into operationalisable solutions. Furthermore, ongoing ethical discussions often neglect other types of GenAI that have been used to synthesise data (e.g., images) for research and practical purposes, which resolved some ethical issues and exposed others. We conduct a scoping review of ethical discussions on GenAI in healthcare to comprehensively analyse gaps in the current research, and further propose to reduce the gaps by developing a checklist for comprehensive assessment and transparent documentation of ethical discussions in GenAI research. The checklist can be readily integrated into the current peer review and publication system to enhance GenAI research, and may be used for ethics-related disclosures for GenAI-powered products, healthcare applications of such products and beyond. © 2023, CC BY.

关键词： Artificial intelligence

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Feature extraction for hyperspectral imagery: The evolution from shallow to deep

arXiv

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arXiv 2020年

作者： Rasti, Behnood Hong, Danfeng Hang, Renlong Ghamisi, Pedram Kang, Xudong Chanussot, Jocelyn Benediktsson, Jon Atli Machine Learning Group Exploration Division Helmholtz Institute Freiberg for Resource Technology Helmholtz-Zentrum Dresden-Rossendorf Freiberg09599 Germany Univ. Grenoble Alpes CNRS Grenoble INP GIPSAlab Grenoble38000 France Jiangsu Key Laboratory of Big Data Analysis Technology School of Automation Nanjing University of Information Science and Technology Nanjing210044 China Machine Learning Group Exploration Division Helmholtz Institute Freiberg for Resource Technology Helmholtz-Zentrum Dresden-Rossendorf Freiberg09599 Germany College of Electrical and Information Engineering Hunan University Changsha410082 China Key Laboratory of Visual Perception and Artificial Intelligence of Hunan Province Changsha410082 China Univ. Grenoble Alpes Inria CNRS Grenoble INP LJK GrenobleF-38000 France Faculty of Electrical and Computer Engineering University of Iceland Reykjavik101 Iceland Faculty of Electrical and Computer Engineering University of Iceland Reykjavik107 Iceland

The final version of the paper can be found in IEEE Geoscience and Remote Sensing Magazine. Hyperspectral images provide detailed spectral information through hundreds of (narrow) spectral channels (also known as dimensionality or bands) with continuous spectral information that can accurately classify diverse materials of interest. The increased dimensionality of such data makes it possible to significantly improve data information content but provides a challenge to the conventional techniques (the so-called curse of dimensionality) for accurate analysis of hyperspectral images. Feature extraction, as a vibrant field of research in the hyperspectral community, evolved through decades of research to address this issue and extract informative features suitable for data representation and classification. The advances in feature extraction have been inspired by two fields of research, including the popularization of image and signal processing as well as machine (deep) learning, leading to two types of feature extraction approaches named shallow and deep techniques. This article outlines the advances in feature extraction approaches for hyperspectral imagery by providing a technical overview of the state-of-the-art techniques, providing useful entry points for researchers at different levels, including students, researchers, and senior researchers, willing to explore novel investigations on this challenging topic. In more detail, this paper provides a bird's eye view over shallow (both supervised and unsupervised) and deep feature extraction approaches specifically dedicated to the topic of hyperspectral feature extraction and its application on hyperspectral image classification. Additionally, this paper compares 15 advanced techniques with an emphasis on their methodological foundations in terms of classification accuracies. Furthermore, to push this vibrant field of research forward an impressive amount of codes and libraries is shared at: https://***/Behnoo

关键词： Feature extraction

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Sketch-based Medical Image Retrieval

arXiv

引用

arXiv 2023年

作者： Kobayashi, Kazuma Gu, Lin Hataya, Ryuichiro Mizuno, Takaaki Miyake, Mototaka Watanabe, Hirokazu Takahashi, Masamichi Takamizawa, Yasuyuki Yoshida, Yukihiro Nakamura, Satoshi Kouno, Nobuji Bolatkan, Amina Kurose, Yusuke Harada, Tatsuya Hamamoto, Ryuji Division of Medical AI Research and Development National Cancer Center Research Institute 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Cancer Translational Research Team RIKEN Center for Advanced Intelligence Project 1-4-1 Nihonbashi Chuo-ku Tokyo103-0027 Japan Machine Intelligence for Medical Engineering Team RIKEN Center for Advanced Intelligence Project 1-4-1 Nihonbashi Chuo-ku Tokyo103-0027 Japan Research Center for Advanced Science and Technology The University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo153-8904 Japan Medical Data Deep Learning Team Advanced Data Science Project RIKEN Information R&D and Strategy Headquarters 1-4-1 Nihonbashi Chuo-ku Tokyo103-0027 Japan Department of Experimental Therapeutics National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Department of Diagnostic Radiology National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Department of Neurosurgery and Neuro-Oncology National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Department of Colorectal Surgery National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Department of Thoracic Surgery National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Radiation Safety and Quality Assurance Division National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Division of Research and Development for Boron Neutron Capture Therapy National Cancer Center Exploratory Oncology Research & Clinical Trial Center 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Medical Physics Laboratory Division of Health Science Graduate School of Medicine Osaka University Yamadaoka 1-7 Osaka Suita-shi565-0871 Japan Department of Surgery Kyoto University Graduate School of Medicine 54 Shogoin Kawahara-cho Sakyo-ku Kyoto606-8507 Japan

The amount of medical images stored in hospitals is increasing faster than ever;however, utilizing the accumulated medical images has been limited. This is because existing content-based medical image retrieval (CBMIR) systems usually require example images to construct query vectors;nevertheless, example images cannot always be prepared. Besides, there can be images with rare characteristics that make it difficult to find similar example images, which we call isolated samples. Here, we introduce a novel sketch-based medical image retrieval (SBMIR) system that enables users to find images of interest without example images. The key idea lies in feature decomposition of medical images, whereby the entire feature of a medical image can be decomposed into and reconstructed from normal and abnormal features. By extending this idea, our SBMIR system provides an easy-to-use two-step graphical user interface: users first select a template image to specify a normal feature and then draw a semantic sketch of the disease on the template image to represent an abnormal feature. Subsequently, it integrates the two kinds of input to construct a query vector and retrieves reference images with the closest reference vectors. Using two datasets, ten healthcare professionals with various clinical backgrounds participated in the user test for evaluation. As a result, our SBMIR system enabled users to overcome previous challenges, including image retrieval based on fine-grained image characteristics, image retrieval without example images, and image retrieval for isolated samples. Our SBMIR system achieves flexible medical image retrieval on demand, thereby expanding the utility of medical image databases. Copyright © 2023, The Authors. All rights reserved.

关键词： Search engines

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Why is the Winner the Best?

Why is the Winner the Best?

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

关键词：

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People flow prediction technology for crowd navigation

引用

NTT Technical Review 2018年第8期16卷 47-52页

作者： Sato, Daisuke Shiohara, Hisako Miyamoto, Masaru Ueda, Naonori Proactive Navigation Project NTT Service Evolution Laboratories Japan Service Innovation Laboratory NTT Service Evolution Laboratories Japan Ueda Research Laboratory Japan Machine Learning and Data Science Center NTT Communication Science Laboratories Japan

We are investigating the use of incomplete observation data in order to predict the large-scale flow of people for major events such as the Olympic Games and to derive guidance measures in advance to prevent the occurrence of congestion. In this article, we introduce a spatio-temporal variable online prediction algorithm for forecasting congestion risk, and we explain our research and development efforts to utilize the algorithm. © 2018 Nippon Telegraph and Telephone Corp. All rights reserved.

关键词： Risk assessment

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Uplink-downlink duality between multiple-access and broadcast channels with compressing relays

arXiv

引用

arXiv 2020年

作者： Liu, Liang Liu, Ya-Feng Patil, Pratik Yu, Wei the Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong the State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Edward S. Rogers Sr. Department of Electrical and Computer Engineering the University of Toronto the Department of Statistics and Data Science and the Machine Learning Department Carnegie Mellon University PittsburghPA15213 United States The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto 10 King’s College Road TorontoONM5S3G4 Canada

—Uplink-downlink duality refers to the fact that under a sum-power constraint, the capacity regions of a Gaussian multiple-access channel and a Gaussian broadcast channel with Hermitian transposed channel matrices are identical. This paper generalizes this result to a cooperative cellular network, in which remote access-points are deployed as relays in serving the users under the coordination of a central processor (CP). In this model, the users and the relays are connected over noisy wireless links, while the relays and the CP are connected over noiseless but rate-limited fronthaul links. Based on a Lagrangian technique, this paper establishes a duality relationship between such a multiple-access relay channel and broadcast relay channel, under the assumption that the relays use compression-based strategies. Specifically, we show that under the same total transmit power constraint and individual fronthaul rate constraints, the achievable rate regions of the Gaussian multiple-access and broadcast relay channels are identical, when either independent compression or Wyner-Ziv and multivariate compression strategies are used. The key observations are that if the beamforming vectors at the relays are fixed, the sum-power minimization problems under the achievable rate and fronthaul constraints in both the uplink and the downlink can be transformed into either a linear programming or a semidefinite programming problem depending on the compression technique, and that the uplink and downlink problems are Lagrangian duals of each other. Moreover, the dual variables corresponding to the downlink rate constraints become the uplink powers;the dual variables corresponding to the downlink fronthaul constraints become the uplink quantization noises. This duality relationship enables an efficient algorithm for optimizing the downlink transmission and relaying strategies based on the uplink. Copyright © 2020, The Authors. All rights reserved.

关键词： Linear programming

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Constructing Impactful machine learning Research for Astronomy: Best Practices for Researchers and Reviewers

arXiv

引用

arXiv 2023年

作者： Huppenkothen, Daniela Ntampaka, Michelle Ho, Matthew Fouesneau, Morgan Nord, Brian Peek, J.E.G. Walmsley, Mike Wu, John F. Avestruz, C. Buck, Tobias Brescia, Massimo Finkbeiner, Douglas P. Goulding, Andy D. Kacprzak, T. Melchior, Peter Pasquato, Mario Ramachandra, Nesar Ting, Yuan-Sen van de Ven, Glenn Villar, Soledad Villar, V.A. Zinger, Elad SRON Netherlands Institute for Space Research Niels Bohrweg 4 Leiden2333CA Netherlands Anton Pannekoek Institute for Astronomy University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands Space Telescope Science Institute BaltimoreMD21218 United States Department of Physics & Astronomy Johns Hopkins University BaltimoreMD21218 United States UMR 7095 98 bis bd Arago ParisF-75014 France Königstuhl 17 HeidelbergD-69117 Germany Fermi National Accelerator Laboratory P. O. Box 500 BataviaIL60510 United States Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States Department of Astronomy and Astrophysics University of Chicago ChicagoIL60637 United States Jodrell Bank Centre for Astrophysics Department of Physics & Astronomy University of Manchester ManchesterM13 9PL United Kingdom Dunlap Institute for Astronomy & Astrophysics University of Toronto 50 St. George Street TorontoONM5S 3H4 Canada Leinweber Center for Theoretical Physics University of Michigan Ann ArborMI48109 United States Department of Physics University of Michigan Ann ArborMI48109 United States Universität Heidelberg Interdisziplinäres Zentrum für Wissenschaftliches Rechnen Im Neuenheimer Feld 205 Heidelberg69120 Germany Universität Heidelberg Zentrum für Astronomie Institut für Theoretische Astrophysik Albert-Ueberle-Straße 2 Heidelberg69120 Germany Department of Physics "E. Pancini " University Federico II of Napoli Via Cinthia 21 NapoliI-80126 Italy INAF Astronomical Observatory of Capodimonte Salita Moiariello 16 NapoliI-80131 Italy Department of Physics Harvard University 17 Oxford St. CambridgeMA02138 United States Harvard-Smithsonian Center for Astrophysics 60 Garden St. CambridgeMA02138 United States Department of Astrophysical Sciences Princeton University PrincetonNJ08544 United States Swiss Data Science Center Paul Scherrer Institute Villigen5303 Switzerland Center for Statistics & Machine Learn

machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of transients to neural network emulators of cosmological simulations, and is shifting paradigms about how we generate and report scientific results. At the same time, this class of method comes with its own set of best practices, challenges, and drawbacks, which, at present, are often reported on incompletely in the astrophysical literature. With this paper, we aim to provide a primer to the astronomical community, including authors, reviewers, and editors, on how to implement machine learning models and report their results in a way that ensures the accuracy of the results, reproducibility of the findings, and usefulness of the method. Copyright © 2023, The Authors. All rights reserved.

关键词： machine learning

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167-PFlops deep learning for electron microscopy: From learning physics to atomic manipulation

167-PFlops deep learning for electron microscopy: From learn...

引用

2018 International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018

作者： Patton, Robert M. Travis Johnston, J. Young, Steven R. Schuman, Catherine D. March, Don D. Potok, Thomas E. Rose, Derek C. Lim, Seung-Hwan Karnowski, Thomas P. Ziatdinov, Maxim A. Kalinin, Sergei V. Oak Ridge National Laboratory Oak RidgeTN37831-6085 United States Computational Data Analytics Group United States Geographic Information Science and Technology Group United States Imaging Signals and Machine Learning Group United States Institute for Functional Imaging of Materials United States Center for Nanophase Materials Sciences United States

ISBN: (纸本)9781538683842

An artificial intelligence system called MENNDL, which used 25,200 NVIDIA Volta GPUs on Oak Ridge National laboratory's Summit machine, automatically designed an optimal deep learning network in order to extract structural information from raw atomic-resolution microscopy data. In a few hours, MENNDL creates and evaluates millions of networks using a scalable, parallel, asynchronous genetic algorithm augmented with a support vector machine to automatically find a superior deep learning network topology and hyper-parameter set than a human expert can find in months. For the application of electron microscopy, the system furthers the goal of improving our understanding of the electron-beam-matter interactions and real-time image-based feedback, which enables a huge step beyond human capacity towards nanofabricating materials automatically. MENNDL has been scaled to the 4,200 available nodes of Summit achieving a measured 152.5 PFlops, with an estimated sustained performance of 167 PFlops when the entire machine is available. © 2018 IEEE.

关键词： Genetic algorithms

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Gradient descent finds global minima of deep neural networks

arXiv

引用

arXiv 2018年

作者： Du, Simon S. Lee, Jason D. Li, Haochuan Wang, Liwei Zhai, Xiyu Machine Learning Department Carnegie Mellon University Data Science and Operations Department University of Southern California School of Physics Peking University Center for Data Science Peking University Beijing Institute of Big Data Research Key Laboratory of Machine Perception Moe School of Eecs Peking University Department of Eecs Massachusetts Institute of Technology

Gradient descent finds a global minimum in training deep neural networks despite the objective function being non-convex. The current paper proves gradient descent achieves zero training loss in polynomial time for a deep over-parameterized neural network with residual connections (ResNet). Our analysis relies on the particular structure of the Gram matrix induced by the neural network architecture. This structure allows us to show the Gram matrix is stable throughout the training process and this stability implies the global optimality of the gradient descent algorithm. We further extend our analysis to deep residual convolutional neural networks and obtain a similar convergence result. Copyright © 2018, The Authors. All rights reserved.

关键词： Gradient methods

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

arXiv

引用

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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