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Computationally designed proteins mimic antibody immune evasion in viral evolution

Immunity 2025年

作者： Youssef, Noor Gurev, Sarah Ghantous, Fadi Brock, Kelly P. Jaimes, Javier A. Thadani, Nicole N. Dauphin, Ann Sherman, Amy C. Yurkovetskiy, Leonid Soto, Daria Estanboulieh, Ralph Kotzen, Ben Notin, Pascal Kollasch, Aaron W. Cohen, Alexander A. Dross, Sandra E. Erasmus, Jesse Fuller, Deborah H. Bjorkman, Pamela J. Lemieux, Jacob E. Luban, Jeremy Seaman, Michael S. Marks, Debora S. Department of Systems Biology Harvard Medical School Boston 02115 MA United States Broad Institute of Harvard and MIT Cambridge 02139 MA United States Department of Electrical Engineering and Computer Science MIT Cambridge 02139 MA United States Center for Virology and Vaccine Research Beth Israel Deaconess Medical Center Boston 02215 MA United States Program in Molecular Medicine University of Massachusetts Chan Medical School Worcester 01655 MA United States Division of Infectious Diseases Department of Medicine Brigham and Women's Hospital Boston 02115 MA United States Massachusetts General Hospital Boston 02114 MA United States Division of Biology and Biological Engineering California Institute of Technology Pasadena 91125 CA United States Department of Microbiology University of Washington Seattle 98195 WA United States National Primate Research Center Seattle 98109 WA United States HDT Bio Seattle 98109 WA United States Massachusetts Consortium on Pathogen Readiness Boston 02115 MA United States Ragon Institute of MGH MIT and Harvard Cambridge 02139 MA United States

Recurrent waves of viral infection necessitate vaccines and therapeutics that remain effective against emerging viruses. Our ability to evaluate interventions is currently limited to assessments against past or circulating variants, which likely differ in their immune escape potential compared with future variants. To address this, we developed EVE-Vax, a computational method for designing antigens that foreshadow immune escape observed in future viral variants. We designed 83 SARS-CoV-2 spike proteins that transduced ACE2-positive cells and displayed neutralization resistance comparable to variants that emerged up to 12 months later in the COVID-19 pandemic. Designed spikes foretold antibody escape from B.1-BA.4/5 bivalent booster sera seen in later variants. The designed constructs also highlighted the increased neutralization breadth elicited by nanoparticle-based, compared with mRNA-based, boosters in non-human primates. Our approach offers targeted panels of synthetic proteins that map the immune landscape for early vaccine and therapeutic evaluation against future viral strains. © 2025 The Authors

关键词： antibody escape generative protein models immune landscape machine learning protein design vaccine evaluation viral evolution

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Deep learning-based frozen section to FFPE translation

arXiv

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arXiv 2021年

作者： Ozyoruk, Kutsev Bengisu Can, Sermet Gokceler, Guliz Irem Başak, Kayhan Demir, Derya Serin, Gurdeniz Hacisalihoglu, Uguray Payam Kurtuluş, Emirhan Darbaz, Berkan Lu, Ming Y. Chen, Tiffany Y. Williamson, Drew F.K. Yilmaz, Funda Mahmood, Faisal Turan, Mehmet Institute of Biomedical Engineering Bogazici University Istanbul Turkey Uk Biocentre Tilbrook United Kingdom Department of Pathology Brigham and Women's Hospital Harvard Medical School BostonMA United States Cancer Data Science Program Dana-Farber Cancer Institute BostonMA United States Cancer Program Broad Institute of Harvard and Mit CambradgeMA United States Saǧlik Bilimleri University Kartal Dr.Lütfi Kirdar City Hospital Department of Pathology Istanbul Turkey Faculty of Medicine Department of Pathology Ege University Izmir Turkey Istanbul Yeni Yuzyil University Medical Faculty Gaziosmanpasa Hospital Pathology Department Turkey

Frozen sectioning (FS) is the preparation method of choice for microscopic evaluation of tissues during surgical operations. The high speed of procedure allows pathologists to rapidly assess the key microscopic features, such as tumor margins and malignant status to guide surgical decision-making and minimise disruptions to the course of the operation. However, FS is prone to introducing many misleading artificial structures (histological artefacts), such as nuclear ice crystals, compression, and cutting artefacts, hindering timely and accurate diagnostic judgement of the pathologist. Additional training and prolonged experience is often required to make highly effective and time critical diagnosis on frozen sections. On the other hand, the gold standard tissue preparation technique of formalin-fixation and paraffin-embedding (FFPE) provides significantly superior image quality, but is a very time-consuming process (12-48 hours), making it unsuitable for intra-operative use. In this paper, we propose an artificial intelligence (AI) method that improves FS image quality by computationally transforming frozen-sectioned whole-slide images (FS-WSIs) into whole-slide FFPE-style images in minutes. AI-FFPE rectifies FS artefacts with the guidance of an attention-mechanism that puts a particular emphasis on artefacts while utilising a self-regularization mechanism established between FS input image and synthesized FFPE-style image that preserves clinically relevant features. As a result, AI-FFPE method successfully generates FFPE-style images without significantly extending tissue processing time and consequently improves diagnostic accuracy. We demonstrate the efficacy of AI-FFPE on lung and brain frozen sections using a variety of different qualitative and quantitative metrics including visual Turing tests from 20 board certified pathologists. Additionally, we demonstrate that deep models trained on FFPE tissue can adapt to frozen sections which a much higher efficacy whe

关键词： Surgery

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Tissue engineered vascular grafts are resistant to the formation of dystrophic calcification

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Nature Communications 2024年第1期15卷 2187页

作者： Turner, Mackenzie E. Blum, Kevin M. Watanabe, Tatsuya Schwarz, Erica L. Nabavinia, Mahboubeh Leland, Joseph T. Villarreal, Delaney J. Schwartzman, William E. Chou, Ting-Heng Baker, Peter B. Matsumura, Goki Krishnamurthy, Rajesh Yates, Andrew R. Hor, Kan N. Humphrey, Jay D. Marsden, Alison L. Stacy, Mitchel R. Shinoka, Toshiharu Breuer, Christopher K. Center for Regenerative Medicine Research Institute at Nationwide Children’s Hospital Columbus OH United States Molecular Cellular and Developmental Biology Graduate Program The Ohio State University Columbus OH United States The Ohio State University College of Medicine Columbus OH United States Department of Biomedical Engineering Yale University New Haven CT United States Biomedical Sciences Graduate Program The Ohio State University College of Medicine Columbus OH United States Department of Exercise and Health Science National Taipei University of Nursing and Health Sciences Taipei City Taiwan Pathology Department at Nationwide Children’s Hospital and The Ohio State University Columbus OH United States Department of Medical Safety Management Tokyo Women’s Medical University Tokyo Japan Department of Radiology Nationwide Children’s Hospital and The Ohio State University Columbus OH United States Department of Pediatrics The Ohio State University College of Medicine Columbus OH United States The Heart Center Nationwide Children’s Hospital Columbus OH United States Departments of Pediatrics and Bioengineering Stanford University Stanford CA United States Department of Surgery The Ohio State University College of Medicine Columbus OH United States Interdisciplinary Biophysics Graduate Program The Ohio State University Columbus OH United States Department of Cardiothoracic Surgery Nationwide Children’s Hospital Columbus OH United States Department of Surgery Nationwide Children’s Hospital Columbus OH United States

Advancements in congenital heart surgery have heightened the importance of durable biomaterials for adult survivors. Dystrophic calcification poses a significant risk to the long-term viability of prosthetic biomaterials in these procedures. Herein, we describe the natural history of calcification in the most frequently used vascular conduits, expanded polytetrafluoroethylene grafts. Through a retrospective clinical study and an ovine model, we compare the degree of calcification between tissue-engineered vascular grafts and polytetrafluoroethylene grafts. Results indicate superior durability in tissue-engineered vascular grafts, displaying reduced late-term calcification in both clinical studies (p < 0.001) and animal models (p < 0.0001). Further assessments of graft compliance reveal that tissue-engineered vascular grafts maintain greater compliance (p < 0.0001) and distensibility (p < 0.001) than polytetrafluoroethylene grafts. These properties improve graft hemodynamic performance, as validated through computational fluid dynamics simulations. We demonstrate the promise of tissue engineered vascular grafts, remaining compliant and distensible while resisting long-term calcification, to enhance the long-term success of congenital heart surgeries. © The Author(s) 2024.

关键词： Translational research Cardiac device therapy Cardiovascular models

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Three-dimensional bioprinting of gelatin methacryloyl (GelMA)

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Bio-Design and Manufacturing 2018年第4期1卷 215-224页

作者： Guoliang Ying Nan Jiang Cunjiang Yu Yu Shrike Zhang Division of Engineering in Medicine Department of MedicineBrigham and Women's HospitalHarvard Medical SchoolCambridgeMA 02139USA School of Materials Science and Engineering Wuhan Institute of TechnologyWuhan 430205People's Republic of China School of Engineering and Applied Sciences Harvard UniversityCambridgeMA 02139USA Departments of Mechanical Engineering Electrical and Computer EngineeringBiomedical EngineeringMaterials Science and Engineering ProgramThe Texas Center for SuperconductivityUniversity of HoustonHoustonTX 77204USA

The three-dimensional (3D)bioprinting technology has progressed tremendously over the past *** controlling the size, shape,and architecture of the bioprinted constructs,3D bioprinting allows for the fabrication of tissue/organ-like constructs with strong structural-functional similarity with their in vivo counterparts at high *** bioink,a blend of biomaterials and living cells possessing both high biocompatibility and printability,is a critical component of *** particular, gelatin methacryloyl (GelMA)has shown its potential as a viable bioink material due to its suitable biocompatibility and readily tunable physicochemical *** GelMA-based bioinks and relevant bioprinting strategies for GelMA bioprinting are briefly reviewed.

关键词： Bioprinting Bioink Gelatin methacryloyl (GelMA) Biofabrication -Tissue engineering Tissue model

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The IDEAL framework for surgical robotics: development, comparative evaluation and long-term monitoring (vol 30, pg 61, 2024)

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NATURE MEDICINE 2024年第4期30卷 1213-1213页

作者： Marcus, Hani J. Ramirez, Pedro T. Khan, Danyal Z. Horsfall, Hugo Layard Hanrahan, John G. Williams, Simon C. Beard, David J. Bhat, Rani Catchpole, Ken Cook, Andrew Hutchison, Katrina Martin, Janet Melvin, Tom Stoyanov, Danail Rovers, Maroeska Raison, Nicholas Dasgupta, Prokar Noonan, David Stocken, Deborah Sturt, Georgia Vanhoestenberghe, Anne Vasey, Baptiste McCulloch, Peter Department of Neurosurgery National Hospital of Neurology and Neurosurgery London UK Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS) London UK Department of Obstetrics and Gynaecology Houston Methodist Hospital Neal Cancer Center Houston TX USA RCS Surgical Interventional Trials Unit (SITU) & Robotic and Digital Surgery Initiative (RADAR) Nuffield Dept Orthopaedics Rheumatology and Musculo-skeletal Sciences University of Oxford Oxford UK Department of Gynaecological Oncology Apollo Hospital Bengaluru India Department of Anaesthesia and Perioperative Medicine Medical University of South Carolina Charleston SC USA NIHR Coordinating Centre and Clinical Trials Unit University of Southampton Southampton UK Department of Philosophy Macquarie University Sydney Australia Department of Anesthesia & Perioperative Medicine University of Western Ontario Ontario Canada Department of Medical Gerontology School of Medicine Trinity College Dublin Dublin Republic of Ireland Department of Medical Imaging Radboudumc Nijmegen the Netherlands Department of Biomedical Engineering and Imaging Sciences King’s College London London UK King’s Health Partners Academic Surgery King’s College London London UK Moon Surgical Paris France RCSEng Surgical Trials Centre Leeds Institute of Clinical Trials Research University of Leeds Leeds UK Bowel Research UK London UK School of Biomedical Engineering & Imaging Sciences King’s College London London UK Department of Surgery Geneva University Hospital Geneva Switzerland Nuffield Department of Surgical Sciences University of Oxford John Radcliffe Hospital Oxford UK PRISMA Lab University of Naples Federico II Naples Italy Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich Switzerland School of Computer Science University of Birmingham Birmingham UK Division of Cardiac Surgery Brigham and Women’s Hospital Harvard Medical Schoo

The next generation of surgical robotics is poised to disrupt healthcare systems worldwide, requiring new frameworks for evaluation. However, evaluation during a surgical robot’s development is challenging due to their complex evolving nature, potential for wider system disruption and integration with complementary technologies like artificial intelligence. Comparative clinical studies require attention to intervention context, learning curves and standardized outcomes. Long-term monitoring needs to transition toward collaborative, transparent and inclusive consortiums for real-world data collection. Here, the Idea, Development, Exploration, Assessment and Long-term monitoring (IDEAL) Robotics Colloquium proposes recommendations for evaluation during development, comparative study and clinical monitoring of surgical robots—providing practical recommendations for developers, clinicians, patients and healthcare systems. Multiple perspectives are considered, including economics, surgical training, human factors, ethics, patient perspectives and sustainability. Further work is needed on standardized metrics, health economic assessment models and global applicability of recommendations.

关键词： Medical research Surgery

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Learn-Morph-Infer: a new way of solving the inverse problem for brain tumor modeling

arXiv

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arXiv 2021年

作者： Ezhov, Ivan Scibilia, Kevin Franitza, Katharina Steinbauer, Felix Shit, Suprosanna Zimmer, Lucas Lipkova, Jana Kofler, Florian Paetzold, Johannes Canalini, Luca Waldmannstetter, Diana Menten, Martin J. Metz, Marie Wiestler, Benedikt Menze, Bjoern Department of Informatics TUM Munich Germany TranslaTUM - Central Institute for Translational Cancer Research TUM Munich Germany Department of Mechanical Engineering TUM Munich Germany Department of Quantitative Biomedicine UZH Zurich Switzerland Department of Pathology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Broad Institute of Harvard MIT CambridgeMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Fraunhofer MEVIS Bremen Germany Neuroradiology Department of Klinikum Rechts der Isar TUM Munich Germany

Current treatment planning of patients diagnosed with a brain tumor, such as glioma, could significantly benefit by accessing the spatial distribution of tumor cell concentration. Existing diagnostic modalities, e.g. magnetic resonance imaging (MRI), contrast sufficiently well areas of high cell density. In gliomas, however, they do not portray areas of low cell concentration, which can often serve as a source for the secondary appearance of the tumor after treatment. To estimate tumor cell densities beyond the visible boundaries of the lesion, numerical simulations of tumor growth could complement imaging information by providing estimates of full spatial distributions of tumor cells. Over recent years a corpus of literature on medical image-based tumor modeling was published. It includes different mathematical formalisms describing the forward tumor growth model. Alongside, various parametric inference schemes were developed to perform an efficient tumor model personalization, i.e. solving the inverse problem. However, the unifying drawback of all existing approaches is the time complexity of the model personalization which prohibits a potential integration of the modeling into clinical settings. In this work, we introduce a deep learning based methodology for inferring the patient-specific spatial distribution of brain tumors from T1Gd and FLAIR MRI medical scans. Coined as Learn-Morph-Infer, the method achieves real-time performance in the order of minutes on widely available hardware and the compute time is stable across tumor models of different complexity, such as reaction-diffusion and reaction-advection-diffusion models. We believe the proposed inverse solution approach not only bridges the way for clinical translation of brain tumor personalization but can also be adopted to other scientific and engineering domains. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Brain MRI before and at term equivalent age predicts motor and cognitive outcomes in very preterm infants

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NeuroImage: Reports 2025年第2期5卷 100262-100262页

作者： Pagnozzi, Alex M. Pannek, Kerstin Boyd, Roslyn N. van Eijk, Liza George, Joanne M. Bora, Samudragupta Bradford, DanaKai Fahey, Michael Ditchfield, Michael Malhotra, Atul Colditz, Paul B. Fripp, Jurgen The Australian e-Health Research Centre CSIRO Brisbane Australia The University of Queensland School of Electrical Engineering and Computer Science Brisbane Australia Child Health Research Centre Queensland Cerebral Palsy and Rehabilitation Research Centre Faculty of Medicine The University of Queensland Brisbane QLD Australia Department of Psychology James Cook University Townsville QLD Australia Physiotherapy Department Queensland Children's Hospital Children's Health Queensland Hospital and Health Service Brisbane Australia Mothers Babies and Women's Health Program Mater Research Institute Faculty of Medicine The University of Queensland Brisbane QLD Australia Monash Health Paediatric Neurology Unit and Department of Paediatrics School of Clinical Sciences Monash University Clayton Victoria Australia Monash Imaging Monash Health Melbourne Victoria Australia Department of Medicine Monash University Melbourne Victoria Australia Department of Paediatrics Monash University Melbourne Victoria Australia Monash Newborn Monash Children's Hospital Melbourne Victoria Australia Perinatal Research Centre The University of Queensland Centre for Clinical Research Faculty of Medicine The University of Queensland Brisbane QLD Australia

Brain Magnetic Resonance Imaging (MRI) of high-risk infants in the neonatal period (from 26 weeks postmenstrual age to Term Equivalent Age (TEA)) is increasingly being used for the detection of brain injuries, and the early prognostication of adverse outcomes such as Cerebral Palsy (CP). While most imaging is performed around TEA in clinical practice for infants born preterm (<37 weeks of gestation), this would often require families to return to hospital for imaging. In this work, we extract structural biomarkers from MRI acquired both before and at TEA in a cohort of very preterm infants from the PPREMO and PREBO studies (n = 100), to determine if either time-point, or both combined, are predictive of both Bayley Scales of Infant and Toddler Development – Third Edition (Bayley-III) and the Neuro-sensory Motor Developmental Assessment (NSMDA) at 2 years. Using multivariable regression, moderately strong and statistically significant associations were found between brain structure on both early and TEA MRIs with 2-year outcomes (r = 0.39–0.55 for early MRI, r = 0.37–0.49 for Term MRI, r = 0.37–0.56 for early and TEA MRI combined). Importantly, brain biomarkers associated with early childhood outcomes from MRIs were identified, including white and grey matter volumes, deep grey matter and cerebellar volumes, and gyrification and surface area across the whole cortex. Early MRI showed the best prognostic accuracy along with combining timepoints, indicating the potential clinical benefit of Early MRI in predicting adverse outcomes. © 2025

关键词： Biomarkers Cerebral palsy Magnetic resonance imaging Neurodevelopmental impairment Prediction Preterm infants Structural

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Retraction notice to “Multi-attribute optimization of sustainable aviation fuel production-process from microalgae source” [Fuel 324(Part C) (2022) 124759]

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Fuel 2025年 391卷

作者： Zafar Said Tuan Hai Nguyen Prabhakar Sharma Changhe Li Hafiz Muhammad Ali Van Nhanh Nguyen Van Viet Pham Shams Forruque Ahmed Dong Nguyen Van Thanh Hai Truong Department of Sustainable and Renewable Energy Engineering University of Sharjah Sharjah P. O. Box 27272 United Arab Emirates U.S.-Pakistan Center for Advanced Studies in Energy (USPCAS-E) National University of Sciences and Technology (NUST) Islamabad Pakistan Faculty of Technology Dong Nai Technology University Dong Nai 76000 Viet Nam Mechanical Engineering Department Delhi Skill and Entrepreneurship University Delhi 110089 India School of Mechanical and Automotive Engineering Qingdao University of Technology Qingdao 266520 China Mechanical Engineering Department King Fahd University of Petroleum & Minerals Dhahran 31261 Saudi Arabia Interdisciplinary Research Center for Renewable Energy and Power Systems (IRC-REPS) King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia Institute of Engineering HUTECH University Ho Chi Minh City Viet Nam Faculty of Marine Engineering Vietnam Maritime University Haiphong Viet Nam Science and Math Program Asian University for Women Chattogram 4000 Bangladesh University of Science and Technology The University of Danang Viet Nam PATET Research Group Ho Chi Minh City University of Transport Ho Chi Minh City Viet Nam

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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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Erratum to: Machine learning-based analysis of multi-omics data on the cloud for investigating gene regulations

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Briefings in bioinformatics 2021年第1期22卷 607页

作者： Minsik Oh Sungjoon Park Sun Kim Heejoon Chae Department of Computer Science and Engineering Seoul National University Seoul 08826 Korea. Interdisciplinary Program in Bioinformatics Seoul National University Seoul 08826 Korea. Bioinformatics Institute Seoul National University Seoul 08826 Korea. Division of Computer Science Sookmyung Women's University Seoul 04310 Korea.

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