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arXiv 2024年

作者： Das, Sudeshna Ge, Yao Guo, Yuting Rajwal, Swati Hairston, JaMor Powell, Jeanne Walker, Drew Peddireddy, Snigdha Lakamana, Sahithi Bozkurt, Selen Reyna, Matthew Sameni, Reza Xiao, Yunyu Kim, Sangmi Chandler, Rasheeta Hernandez, Natalie Mowery, Danielle Wightman, Rachel Love, Jennifer Spadaro, Anthony Perrone, Jeanmarie Sarker, Abeed Department of Biomedical Informatics School of Medicine Emory University AtlantaGA United States Department of Computer Science and Informatics Emory University AtlantaGA United States Department of Behavioral Social & Health Education Sciences Rollins School of Public Health Emory University AtlantaGA United States Department of Biomedical Engineering Georgia Institute of Technology Emory University AtlantaGA United States Department of Population Health Sciences Weill Cornell Medicine New YorkNY United States Nell Hodgson Woodruff School of Nursing Emory University AtlantaGA United States Center for Maternal Health Equity Morehouse School of Medicine AtlantaGA United States Department of Biostatistics Epidemiology and Informatics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Emergency Medicine Warren Alpert Medical School of Brown University ProvidenceRI United States Department of Emergency Medicine Icahn School of Medicine at Mount Sinai New YorkNY United States Department of Emergency Medicine Rutgers New Jersey Medical School NewarkNJ United States Department of Emergency Medicine Perelman School of Medicine the University of Pennsylvania PhiladelphiaPA United States

Background: The increasing use of social media to share lived and living experiences of substance use presents a unique opportunity to obtain information on side effects, use patterns, and opinions on novel psychoactive substances. However, due to the large volume of data, obtaining useful insights through natural language processing technologies such as large language models is challenging. Objective: This paper aims to develop a retrieval-augmented generation (RAG) architecture for medical question answering pertaining to clinicians’ queries on emerging issues associated with health-related topics, using user-generated medical information on social media. Methods: We proposed a two-layer RAG framework for query-focused answer generation and evaluated a proof of concept for the framework in the context of query-focused summary generation from social media forums, focusing on emerging drug-related information. Our modular framework generates individual summaries followed by an aggregated summary to answer medical queries from large amounts of user-generated social media data in an efficient manner. We compared the performance of a quantized large language model (Nous-Hermes-2-7B-DPO), deployable in low-resource settings, with GPT-4. For this proof-of-concept study, we used user-generated data from Reddit to answer clinicians’ questions on the use of xylazine and ketamine. Results: Our framework achieves comparable median scores in terms of relevance, length, hallucination, coverage, and coherence when evaluated using GPT-4 and Nous-Hermes-2-7B-DPO, evaluated for 20 queries with 76 samples. There was no statistically significant difference between GPT-4 and Nous-Hermes-2-7B-DPO for coverage (Mann-Whitney U=733.0;n1=37;n2=39;P=.89 two-tailed), coherence (U=670.0;n1=37;n2=39;P=.49 two-tailed), relevance (U=662.0;n1=37;n2=39;P=.15 two-tailed), length (U=672.0;n1=37;n2=39;P=.55 two-tailed), and hallucination (U=859.0;n1=37;n2=39;P=.01 two-tailed). A statistically signifi

关键词： Natural language processing systems

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Factors considered for the success of the Massive Open Online Course in the era of Smart Education: Systematic Literature Review 10

Factors considered for the success of the Massive Open Onlin...

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10th International Conference on ICT for Smart Society, ICISS 2019

作者： Suwita, Jaka Kosala, Raymond Ranti, Benny Supangkat, Suhono Harso Bina Nusantara University Department of Computer Science Jakarta11480 Indonesia Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bandung Institute of Technology Electrical Engineering and Informatics School Bandung Indonesia

ISBN: (纸本)9781728148809

The Internet of Things has a big influence on the appearance of new technologies in learning. Smart education is a learning concept that is developed by intelligent technology development. Therefore smart education must be able to provide an update of learning methods. Smart education must make it easier for users to interact and collaborate to make, organize, and share knowledge from anywhere and at any time accessible. In this paper, we conduct a Systematic Literature Review (SLR) research method to find out the features that exist in MOOCs in the era of intelligent education. This paper presents a comprehensive view of the development of MOOC features in the era of smart education in higher education. Our contribution is comprehensive information about MOOC features in the era of intelligent education in higher education, to improve the quality of learning, and possible use in practice. © 2019 IEEE.

关键词： Learning systems

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The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue

arXiv

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arXiv 2023年

作者： Weitz, Philippe Valkonen, Masi Solorzano, Leslie Carr, Circe Kartasalo, Kimmo Boissin, Constance Koivukoski, Sonja Kuusela, Aino Rasic, Dusan Feng, Yanbo Pouplier, Sandra Sinius Sharma, Abhinav Eriksson, Kajsa Ledesma Robertson, Stephanie Marzahl, Christian Gatenbee, Chandler D. Anderson, Alexander R.A. Wodzinski, Marek Jurgas, Artur Marini, Niccolò Atzori, Manfredo Müller, Henning Budelmann, Daniel Weiss, Nick Heldmann, Stefan Lotz, Johannes Wolterink, Jelmer M. De Santi, Bruno Patil, Abhijeet Sethi, Amit Kondo, Satoshi Kasai, Satoshi Hirasawa, Kousuke Farrokh, Mahtab Kumar, Neeraj Greiner, Russell Latonen, Leena Laenkholm, Anne-Vibeke Hartman, Johan Ruusuvuori, Pekka Rantalainen, Mattias Department of Medical Epidemiology and Biostatistics Karolinska Insitutet Stockholm Sweden Institute of Biomedicine University of Turku Turku Finland Institute of Biomedicine University of Eastern Finland Kuopio Finland Department of Surgical Pathology Zealand University Hospital Roskilde Denmark Department of Oncology and Pathology Karolinska Institutet Stockholm Sweden Gestalt Diagnostics Spokane United States Department of Integrated Mathematical Oncology Moffitt Cancer Center Tampa United States Informatics Institute University of Applied Sciences Western Switzerland Switzerland Department of Measurement and Electronics Agh University of Kraków Poland Department of Computer Science University of Geneva Geneva Switzerland Department of Neuroscience University of Padova Italy Medical Faculty University of Geneva Switzerland Fraunhofer Institute for Digital Medicine Mevis Lübeck Germany Department of Applied Mathematics Technical Medical Centre University of Twente Enschede Netherlands Multimodality Medical Imaging Technical Medical Centre University of Twente Enschede Netherlands Department of Electrical Engineering Indian Institute of Technology Bombay India Graduate School of Engineering Muroran Institute of Technology Hokkaido Japan Faculty of Medical Technology Niigata University of Health and Welfare Niigata Japan Forxai Business Operations Konica Minolta Inc. Osaka Japan Department of Computing Science University of Alberta EdmontonAB Canada Alberta Machine Intelligence Institute Edmonton Canada MedTechLabs BioClinicum Karolinska University Hospital Stockholm Sweden Faculty of Medicine and Health Technology Tampere University Tampere Finland

The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability of large WSI datasets have revolutionised WSI analysis. Therefore, the current state-of-the-art in WSI registration is unclear. To address this, we conducted the ACROBAT challenge, based on the largest WSI registration dataset to date, including 4,212 WSIs from 1,152 breast cancer patients. The challenge objective was to align WSIs of tissue that was stained with routine diagnostic immunohistochemistry to its H&E-stained counterpart. We compare the performance of eight WSI registration algorithms, including an investigation of the impact of different WSI properties and clinical covariates. We find that conceptually distinct WSI registration methods can lead to highly accurate registration performances and identify covariates that impact performances across methods. These results establish the current state-of-the-art in WSI registration and guide researchers in selecting and developing methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Tissue

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Mesoscopic ultrafast nonlinear optics—The emergence of multimode quantum non-Gaussian physics

arXiv

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arXiv 2023年

作者： Yanagimoto, Ryotatsu Ng, Edwin Jankowski, Marc Nehra, Rajveer McKenna, Timothy P. Onodera, Tatsuhiro Wright, Logan G. Hamerly, Ryan Marandi, Alireza Fejer, M.M. Mabuchi, Hideo Physics & Informatics Laboratories NTT Research Inc. SunnyvaleCA94085 United States E. L. Ginzton Laboratory Stanford University StanfordCA94305 United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States Department of Electrical Engineering California Institute of Technology PasadenaCA91125 United States Department of Electrical and Computer Engineering University of Massachusetts-Amherst AmherstMA01003 United States Department of Physics University of Massachusetts-Amherst AmherstMA01003 United States Departments of Applied Physics Yale University New HavenCT06511 United States Research Laboratory of Electronics MIT 50 Vassar Street CambridgeMA02139 United States

Over the last few decades, nonlinear optics has become significantly more nonlinear, traversing nearly a billionfold improvement in energy efficiency, with ultrafast nonlinear nanophotonics in particular emerging as a frontier for combining both spatial and temporal engineering. At present, cutting-edge experiments in nonlinear nanophotonics place us just above the mesoscopic regime, where a few hundred photons suffice to trigger nonlinear saturation. In contrast to classical or deep-quantum optics, the mesoscale is characterized by dynamical interactions between mean-field, Gaussian, and non-Gaussian quantum features, all within a close hierarchy of scales. When combined with the inherent multimode complexity of optical fields, such hybrid quantum-classical dynamics present theoretical, experimental, and engineering challenges to the contemporary framework of quantum optics. In this review, we highlight the unique physics that emerges in multimode nonlinear optics at the mesoscale and outline key principles for exploiting both classical and quantum features to engineer novel functionalities. We briefly survey the experimental landscape and draw attention to outstanding technical challenges in materials, dispersion engineering, and device design for accessing mesoscopic operation. Finally, we speculate on how these capabilities might usher in some new paradigms in quantum photonics, from quantum-augmented information processing to nonclassical-light-driven dynamics and phenomena to all-optical non-Gaussian measurement and sensing. The physics unlocked at the mesoscale present significant challenges and opportunities in theory and experiment alike, and this review is intended to serve as a guidepost as we begin to navigate this new frontier in ultrafast quantum nonlinear optics. Copyright © 2023, The Authors. All rights reserved.

关键词： Quantum optics

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A comprehensive review of computer-aided whole-slide image analysis: From datasets to feature extraction, segmentation, classification and detection approaches

arXiv

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arXiv 2021年

作者： Li, Chen Li, Xintong Rahaman, Md Li, Xiaoyan Sun, Hongzan Zhang, Hong Zhang, Yong Li, Xiaoqi Wu, Jian Yao, Yudong Grzegorzek, Marcin Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang110169 China China Medical University Shenyang110122 China Department of Electrical and Computer Engineering Stevens Institute of Technology Hobo-kenNJ07030 United States Institute of Medical Informatics University of Luebeck Luebeck Germany

With the development of computer-aided diagnosis (CAD) and image scanning technology, Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis. Therefore, WSI analysis has become the key to modern digital pathology. Since 2004, WSI has been used more and more in CAD. Since machine vision methods are usually based on semi-automatic or fully automatic computers, they are highly efficient and labor-saving. The combination of WSI and CAD technologies for segmentation, classification, and detection helps histopathologists obtain more stable and quantitative analysis results, save labor costs and improve diagnosis objectivity. This paper reviews the methods of WSI analysis based on machine learning. Firstly, the development status of WSI and CAD methods are introduced. Secondly, we discuss publicly available WSI datasets and evaluation metrics for segmentation, classification, and detection tasks. Then, the latest development of machine learning in WSI segmentation, classification, and detection are reviewed continuously. Finally, the existing methods are studied, the applicability of the analysis methods are analyzed, and the application prospects of the analysis methods in this field are forecasted. © 2021, CC BY.

关键词： Image segmentation

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NTIRE 2023 Quality Assessment of Video Enhancement Challenge

arXiv

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arXiv 2023年

作者： Liu, Xiaohong Min, Xiongkuo Sun, Wei Zhang, Yulun Zhang, Kai Timofte, Radu Zhai, Guangtao Gao, Yixuan Cao, Yuqin Kou, Tengchuan Dong, Yunlong Jia, Ziheng Li, Yilin Wu, Wei Hu, Shuming Deng, Sibin Xiao, Pengxiang Chen, Ying Li, Kai Zhao, Kai Yuan, Kun Sun, Ming Cong, Heng Wang, Hao Fu, Lingzhi Zhang, Yusheng Zhang, Rongyu Shi, Hang Xu, Qihang Xiao, Longan Ma, Zhiliang Agarla, Mirko Celona, Luigi Rota, Claudio Schettini, Raimondo Huang, Zhiwei Li, Yanan Wang, Xiaotao Lei, Lei Liu, Hongye Hong, Wei Chuang, Ironhead Lin, Allen Guan, Drake Chen, Iris Lou, Kae Huang, Willy Tasi, Yachun Kao, Yvonne Fan, Haotian Kong, Fangyuan Zhou, Shiqi Liu, Hao Lai, Yu Chen, Shanshan Wang, Wenqi Wu, Haoning Chen, Chaofeng Zhu, Chunzheng Guo, Zekun Zhao, Shiling Yin, Haibing Wang, Hongkui Meftah, Hanene Brachemi Fezza, Sid Ahmed Hamidouche, Wassim Déforges, Olivier Shi, Tengfei Mansouri, Azadeh Motamednia, Hossein Bakhtiari, Amir Hossein Aznaveh, Ahmad Mahmoudi Shanghai Jiao Tong University China ETH Zürich Switzerland University of Würzburg Germany Department of Tao Technology Alibaba Group China Kuaishou Technology China Interactive Entertainment Group of Netease Inc Guangzhou China Transsion China Vrobotit Lab Beijing University of Posts and Telecommunications China Hefei University of Technology China Department of Informatics Systems and Communication University of Milano - Bicocca Italy Xiaomi Inc. China China Ji Liang University China FreeTech China Advanced Research Center KKCompany Taiwan ByteDance China MGTV China Fuzhou University China Shopee Information Technology Co. Ltd. China S-Lab Nanyang Technological University Singapore Hunan University China Hangzhou Dianzi University China INSA Rennes CNRS IETR - UMR 6164 Rennes France National Higher School of Telecommunications and ICT Oran Algeria BUAA China Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Cyberspace Research Institute Shahid Beheshti University Tehran Iran

This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This challenge is to address a major challenge in the field of video processing, namely, video quality assessment (VQA) for enhanced videos. The challenge uses the VQA Dataset for Perceptual Video Enhancement (VDPVE), which has a total of 1211 enhanced videos, including 600 videos with color, brightness, and contrast enhancements, 310 videos with deblurring, and 301 deshaked videos. The challenge has a total of 167 registered participants. 61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions. A total of 176 submissions were submitted by 37 participating teams during the final testing phase. Finally, 19 participating teams submitted their models and fact sheets, and detailed the methods they used. Some methods have achieved better results than baseline methods, and the winning methods have demonstrated superior prediction performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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Structured graph learning for clustering and semi-supervised classification

arXiv

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arXiv 2020年

作者： Kang, Zhao Peng, Chong Cheng, Qiang Liu, Xinwang Peng, Xi Xu, Zenglin Tian, Ling School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China College of Computer Science and Technology Qingdao University Qingdao266071 China Institute of Biomedical Informatics and Department of Computer Science University of Kentucky LexingtonKY40506 United States School of Computer Science National University of Defense Technology Changsha410073 China College of Computer Science Sichuan Univerisity Chengdu610064 China Department of Computer Science and Technology Harbin Institute of Technology Shenzhen518055 China

Graphs have become increasingly popular in modeling structures and interactions in a wide variety of problems during the last decade. Graph-based clustering and semi-supervised classification techniques have shown impressive performance. This paper proposes a graph learning framework to preserve both the local and global structure of data. Specifically, our method uses the self-expressiveness of samples to capture the global structure and adaptive neighbor approach to respect the local structure. Furthermore, most existing graph-based methods conduct clustering and semi-supervised classification on the graph learned from the original data matrix, which doesn’t have explicit cluster structure, thus they might not achieve the optimal performance. By considering rank constraint, the achieved graph will have exactly c connected components if there are c clusters or classes. As a byproduct of this, graph learning and label inference are jointly and iteratively implemented in a principled way. Theoretically, we show that our model is equivalent to a combination of kernel k-means and k-means methods under certain condition. Extensive experiments on clustering and semi-supervised classification demonstrate that the proposed method outperforms other state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Graphic methods

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Data Partition Optimization for High Energy Efficiency by Decoupling Local Dependence in Holographic Video Decoder

Data Partition Optimization for High Energy Efficiency by De...

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IEEE International Conference on Electronics, Circuits and Systems (ICECS)

作者： Xinzhe Liu Jianwen Luo David Blinder Fupeng Chen Heng Yu Peter Schelkens Francky Catthoor Yajun Ha School of Information Science and Technology ShanghaiTech University Shanghai China Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Department of Electronics and Informatics (ETRO) Vrije Universiteit Brussel (VUB) Belgium Interuniversity Microelectronics Centre (IMEC) Leuven Belgium Silicon Realization Group Synopsys Shanghai China School of Computer Science University of Nottingham Ningbo China Ningbo China Catholic University of Leuven (K.U.Leuven) Leuven Belgium Shanghai Engineering Research Center of Energy Efficient and Custom AI IC Shanghai China

Holography has attracted considerable attention from researchers due to its ability to store and recreate the wavefront emanating from a three-dimensional object. However, holographic video requires enormous resolution $(128 k \times 128 k)$ at the same frame rates(60fps) as normal video to achieve acceptable visual effects. Data compression is thus essential for its storage/transmission. When implementing its decompression pipeline on hardware for mobile scenarios, data dependency and energy consumption must be handled carefully. In this work, we present a novel design framework and a data partition optimization approach to optimize the overall energy consumption by tackling local dependence in the motion compensation module for holographic video codec, and exploring the design space of data partition layout. First, we propose a local data dependency propagation (LDDP) method that transforms one holographic frame with strong local dependence into multiple mutually independent virtual blocks without local dependence at all. Second, we formulate a model for the data partitioning problem, allowing us to analyze and optimize energy consumption by adjusting the layout of data partitions. Third, we provide a heuristic and efficient solution to the formulated model taking advantage of the target application scenarios. Experiment results in various scenarios show that our proposed optimization method achieves 2.94 ~ 3.91 $\times$ energy efficiency and 46.37% ~ 63.63% area efficiency compared to baseline approaches.

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Reconfigurable Intelligent Surfaces for Wireless Communications: Overview of Hardware Designs, Channel Models, and Estimation Techniques

arXiv

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arXiv 2022年

作者： Jian, Mengnan Alexandropoulos, George C. Basar, Ertugrul Huang, Chongwen Liu, Ruiqi Liu, Yuanwei Yuen, Chau Wireless Research Institute ZTE Corporation Beijing100029 China State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens15784 Greece Department of Electrical and Electronics Engineering Koç University Sariyer Istanbul34450 Turkey College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Pillar Singapore University of Technology and Design Singapore487372 Singapore

The demanding objectives for the future sixth generation (6G) of wireless communication networks have spurred recent research efforts on novel materials and radio-frequency front-end architectures for wireless connectivity, as well as revolutionary communication and computing paradigms. Among the pioneering candidate technologies for 6G belong the reconfigurable intelligent surfaces (RISs), which are artificial planar structures with integrated electronic circuits that can be programmed to manipulate the incoming electromagnetic field in a wide variety of functionalities. Incorporating RISs in wireless networks has been recently advocated as a revolutionary means to transform any wireless signal propagation environment to a dynamically programmable one, intended for various networking objectives, such as coverage extension and capacity boosting, spatiotemporal focusing with benefits in energy efficiency and secrecy, and low electromagnetic field exposure. Motivated by the recent increasing interests in the field of RISs and the consequent pioneering concept of the RIS-enabled smart wireless environments, in this paper, we overview and taxonomize the latest advances in RIS hardware architectures as well as the most recent developments in the modeling of RIS unit elements and RIS-empowered wireless signal propagation. We also present a thorough overview of the channel estimation approaches for RIS-empowered communications systems, which constitute a prerequisite step for the optimized incorporation of RISs in future wireless networks. Finally, we discuss the relevance of the RIS technology in the latest wireless communication standards, and highlight the current and future standardization activities for the RIS technology and the consequent RIS-empowered wireless networking approaches. © 2022, CC BY-NC-ND.

关键词： Channel estimation

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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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