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Nuclear cardiology image enhancement using wavelet transform analysis

Physica Medica 2014年 30卷 e30-e30页

作者： A. Poulakis S. Angelakis G. Fountos G.S. Panagiotakis I. Asteriadis I. Valais Department of Biomedical Engineering Technological Educational Institute of Athens 12210 Athens Greece Biomedical Informatics and Technology Graduate Program School of Computer Science University of Crete Department of Medical Physics Medical School University of Patras 265 00 Patras Greece Department of Nuclear Medicine General Hospital of Kavala 65 500 Kavala Greece

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Personal health records - Potential one-stop approach in polypharmacy

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Computer Methods and programs in Biomedicine Update 2022年 2卷

作者： Chen, Ya-Lin Satria, Fauzi Budi Jamshed, Shazia Babar, Zaheer-Ud-Din Mukherjee, Nilesh Yang, Hsuan-Chia Li, Yu-Chuan Iqbal, Usman Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan PhD Program in Global Health & Health Security College of Public Health Taipei Medical University Taipei Taiwan Department of Pharmacy Practice Faculty of Pharmacy UniSZA Kuala Terengganu Malaysia Qualitative Research-Methodological Application in Health Sciences Research Group Kulliyyah of Pharmacy International Islamic University Malaysia Kuantan Malaysia Centre for Pharmaceutical Policy and Practice Research Department of Pharmacy University of Huddersfield Huddersfield United Kingdom Master of Science in Systems Biology Manipal School of Life Sciences Manipal Academy of Higher Education Manipal India International Center for Health Information Technology (ICHIT) Taipei Medical University Taipei Taiwan

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Inferring spatial transcriptomics markers from whole slide images to characterize metastasis-related spatial heterogeneity of colorectal tumors: A pilot study

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Journal of Pathology informatics 2023年 14卷 100308-100308页

作者： Fatemi, Michael Feng, Eric Sharma, Cyril Azher, Zarif Goel, Tarushii Ramwala, Ojas Palisoul, Scott M. Barney, Rachael E. Perreard, Laurent Kolling, Fred W. Salas, Lucas A. Christensen, Brock C. Tsongalis, Gregory J. Vaickus, Louis J. Levy, Joshua J. Department of Computer Science University of Virginia Charlottesville VA United States Thomas Jefferson High School for Science and Technology Alexandria VA United States Department of Computer Science Purdue University West Lafayette IN United States Department of Computer Science Massachusetts Institute of Technology Cambridge MA United States Department of Biomedical Informatics and Medical Education University of Washington Seattle WA United States Emerging Diagnostic and Investigative Technologies Department of Pathology and Laboratory Medicine Dartmouth Health NH United States Dartmouth Cancer Center NH United States Department of Epidemiology Dartmouth College Geisel School of Medicine Hanover NH United States Department of Molecular and Systems Biology Dartmouth College Geisel School of Medicine Hanover NH United States Integrative Neuroscience at Dartmouth (IND) graduate program Dartmouth College Geisel School of Medicine Hanover NH United States Department of Community and Family Medicine Dartmouth College Geisel School of Medicine Hanover NH United States Department of Dermatology Dartmouth Health NH United States Program in Quantitative Biomedical Sciences Dartmouth College Geisel School of Medicine Hanover NH United States

Over 150 000 Americans are diagnosed with colorectal cancer (CRC) every year, and annually over 50 000 individuals will die from CRC, necessitating improvements in screening, prognostication, disease management, and therapeutic options. Tumor metastasis is the primary factor related to the risk of recurrence and mortality. Yet, screening for nodal and distant metastasis is costly, and invasive and incomplete resection may hamper adequate assessment. Signatures of the tumor-immune microenvironment (TIME) at the primary site can provide valuable insights into the aggressiveness of the tumor and the effectiveness of various treatment options. Spatially resolved transcriptomics technologies offer an unprecedented characterization of TIME through high multiplexing, yet their scope is constrained by cost. Meanwhile, it has long been suspected that histological, cytological, and macroarchitectural tissue characteristics correlate well with molecular information (e.g., gene expression). Thus, a method for predicting transcriptomics data through inference of RNA patterns from whole slide images (WSI) is a key step in studying metastasis at scale. In this work, we collected tissue from 4 stage-III (pT3) matched colorectal cancer patients for spatial transcriptomics profiling. The Visium spatial transcriptomics (ST) assay was used to measure transcript abundance for 17 943 genes at up to 5000 55-micron (i.e., 1–10 cells) spots per patient sampled in a honeycomb pattern, co-registered with hematoxylin and eosin (H&E) stained WSI. The Visium ST assay can measure expression at these spots through tissue permeabilization of mRNAs, which are captured through spatially (i.e., x–y positional coordinates) barcoded, gene specific oligo probes. WSI subimages were extracted around each co-registered Visium spot and were used to predict the expression at these spots using machine learning models. We prototyped and compared several convolutional, transformer, and graph convolutional neural

关键词： Colorectal cancer Deep learning Graph neural network Histomorphology Spatial transcriptomics Transformers

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Changing life expectancy in European countries 1990–2021: a subanalysis of causes and risk factors from the Global Burden of Disease Study 2021

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The Lancet Public Health 2025年第3期10卷 e172-e188页

作者： Steel, Nicholas Bauer-Staeb, Clarissa Maria Mercedes Ford, John A. Abbafati, Cristiana Abdalla, Mohammed Altigani Abdelkader, Atef Abdi, Parsa Zuñiga, Roberto Ariel Abeldaño Abiodun, Olugbenga Olusola Abolhassani, Hassan Abu-Gharbieh, Eman Abukhadijah, Hana J. Abu-Zaid, Ahmed Addo, Isaac Yeboah Addolorato, Giovanni Adekanmbi, Victor Adetunji, Juliana Bunmi Adeyeoluwa, Temitayo Esther Agardh, Emilie E. Agyemang-Duah, Williams Ahmad, Danish Ahmed, Ayman Ahmed, Syed Anees Akinosoglou, Karolina Akkaif, Mohammed Ahmed Al Awaidy, Salah Al Hasan, Syed Mahfuz Al Zaabi, Omar Ali Mohammed Aldridge, Robert W. Algammal, Abdelazeem M. Al-Gheethi, Adel Ali Saeed Ali, Abid Ali, Mohammed Usman Ali, Syed Shujait Ali, Waad Alicandro, Gianfranco Alif, Sheikh Mohammad Al-Jumaily, Adel Allebeck, Peter Alrawashdeh, Ahmad Al-Rifai, Rami H. Alsabri, Mohammed A. Alshahrani, Najim Z. Aluh, Deborah Oyine Al-Wardat, Mohammad Al-Zyoud, Walid A. Amiri, Sohrab Anderlini, Deanna Andrei, Catalina Liliana Anil, Abhishek Anvari, Saeid Anyasodor, Anayochukwu Edward Appiah, Seth Christopher Yaw Aquilano, Michele Arabloo, Jalal Arafat, Mosab Areda, Demelash Aremu, Abdulfatai Armani, Keivan Armocida, Benedetta Ärnlöv, Johan Asaduzzaman, Muhammad Astell-Burt, Thomas Aujayeb, Avinash Ausloos, Marcel Azadnajafabad, Sina Aziz, Shahkaar Azzam, Ahmed Y. Babu, Giridhara Rathnaiah Badache, Andreea Corina Badiye, Ashish D. Bahramian, Saeed Baig, Atif Amin Baker, Jennifer L. Bansal, Hansi Bärnighausen, Till Winfried Barone, Mark Thomaz Ugliara Barrow, Amadou Barteit, Sandra Bashir, Shahid Bashiru, Hameed Akande Basso, João Diogo Bastan, Mohammad-Mahdi Basu, Sanjay Batra, Kavita Bauckneht, Matteo Baune, Bernhard T. Beghi, Massimiliano Beiranvand, Maryam Béjot, Yannick Bell, Michelle L. Bello, Olorunjuwon Omolaja Belo, Luis Beloukas, Apostolos Beneke, Alice A. Bettencourt, Paulo J.G. Bhagavathula, Akshaya Srikanth Bhala, Neeraj Bhaskar, Sonu Bisulli, Francesca Bjørge, Tone Bodunrin, Aadam Olalekan Carvajal, Alejandro Botero Bouaoud, Souad Brayne, Carol Brenner, H Department of Primary Care and Public Health University of East Anglia Norwich United Kingdom Office for Health Improvement and Disparities Department of Health and Social Care London United Kingdom Department of Legal and Economic Studies La Sapienza University Rome Italy Rome Italy Hull York Medical School University of Hull Hull United Kingdom Department of Mathematics and Sciences Ajman University Ajman United Arab Emirates Department of Medicine Memorial University St. John's NL Canada Postgraduate Department University of Sierra Sur Miahuatlan de Porfirio Diaz Mexico Yhteiskuntadatatieteen keskus (Centre for Social Data Science) Helsinki Finland Department of Internal Medicine Federal Medical Centre Abuja Nigeria Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Department of Medical Biochemistry and Biophysics Karolinska Institutet (Karolinska Institute) Stockholm Sweden Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Department of Biopharmaceutics and Clinical Pharmacy University of Jordan Amman Jordan Medical Research Center Hamad Medical Corporation Doha Qatar Department of Biochemistry and Molecular Medicine Alfaisal University Riyadh Saudi Arabia College of Graduate Health Sciences University of Tennessee Memphis TN United States School of Medicine Sydney NSW Australia Centre for Social Research in Health University of New South Wales Sydney NSW Australia Internal Medicine and Alcohol Related Disease Unit Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy Department of Medical and Surgical Sciences Università Cattolica di Roma (Catholic University of Rome) Rome Italy Department of Obstetrics and Gynecology University of Texas Medical Branch Galveston TX United States Department of Biochemistry Osun State University Osogbo Nigeria Department of Pharmacology and Therapeutics University of Medical Sciences Ondo Ondo Nigeria Department of Veteri

Background: Decades of steady improvements in life expectancy in Europe slowed down from around 2011, well before the COVID-19 pandemic, for reasons which remain disputed. We aimed to assess how changes in risk factors and cause-specific death rates in different European countries related to changes in life expectancy in those countries before and during the COVID-19 pandemic. Methods: We used data and methods from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to compare changes in life expectancy at birth, causes of death, and population exposure to risk factors in 16 European Economic Area countries (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, and Sweden) and the four UK nations (England, Northern Ireland, Scotland, and Wales) for three time periods: 1990–2011, 2011–19, and 2019–21. Changes in life expectancy and causes of death were estimated with an established life expectancy cause-specific decomposition method, and compared with summary exposure values of risk factors for the major causes of death influencing life expectancy. Findings: All countries showed mean annual improvements in life expectancy in both 1990–2011 (overall mean 0·23 years [95% uncertainty interval [UI] 0·23 to 0·24]) and 2011–19 (overall mean 0·15 years [0·13 to 0·16]). The rate of improvement was lower in 2011–19 than in 1990–2011 in all countries except for Norway, where the mean annual increase in life expectancy rose from 0·21 years (95% UI 0·20 to 0·22) in 1990–2011 to 0·23 years (0·21 to 0·26) in 2011–19 (difference of 0·03 years). In other countries, the difference in mean annual improvement between these periods ranged from –0·01 years in Iceland (0·19 years [95% UI 0·16 to 0·21] vs 0·18 years [0·09 to 0·26]), to –0·18 years in England (0·25 years [0·24 to 0·25] vs 0·07 years [0·06 to 0·08]). In 2019–21, there was an overall decrease in mean annual life expectancy a

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Large-scale plasma proteomic profiling unveils diagnostic biomarkers and pathways for Alzheimer’s disease

Nature Aging

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Nature Aging 2025年 1-18页

作者： Heo, Gyujin Xu, Ying Wang, Erming Ali, Muhammad Oh, Hamilton Se-Hwee Moran-Losada, Patricia Anastasi, Federica González Escalante, Armand Puerta, Raquel Song, Soomin Timsina, Jigyasha Liu, Menghan Western, Daniel Gong, Katherine Chen, Yike Kohlfeld, Pat Flynn, Allison Thomas, Alvin G. Lowery, Joseph Morris, John C. Holtzman, David M. Perlmutter, Joel S. Schindler, Suzanne E. Vilor-Tejedor, Natalia Suárez-Calvet, Marc García-González, Pablo Marquié, Marta Fernández, Maria Victoria Boada, Mercè Cano, Amanda Ruiz, Agustín Zhang, Bin Bennett, David A. Benzinger, Tammie Wyss-Coray, Tony Ibanez, Laura Sung, Yun Ju Cruchaga, Carlos Department of Psychiatry Washington University School of Medicine St. Louis MO United States NeuroGenomics and Informatics Center Washington University St. Louis MO United States Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New York NY United States Mount Sinai Center for Transformative Disease Modeling Icahn School of Medicine at Mount Sinai New York NY United States Graduate Program in Stem Cell and Regenerative Medicine Stanford University Stanford CA United States The Phil and Penny Knight Initiative for Brain Resilience Stanford University Stanford CA United States Wu Tsai Neurosciences Institute Stanford University Stanford CA United States Department of Neurology and Neurological Sciences Stanford University School of Medicine Stanford CA United States Barcelonaβeta Brain Research Center (BBRC) Pasqual Maragall Foundation Barcelona Spain Hospital del Mar Research Institute Barcelona Spain Centre for Genomic Regulation (CRG) Barcelona Institute of Science and Technology (BIST) Barcelona Spain Department of Medicine and Life Sciences Universitat Pompeu Fabra Barcelona Spain Ace Alzheimer Center Barcelona Universitat Internacional de Catalunya Barcelona Spain PhD Program in Biotechnology Faculty of Pharmacy and Food Sciences University of Barcelona Barcelona Spain Department of Neurology Washington University School of Medicine St. Louis MO United States Hope Center for Neurologic Diseases Washington University St. Louis MO United States Knight Alzheimer’s Disease Research Center Washington University St. Louis MO United States Department of Radiology Washington University School of Medicine St. Louis MO United States Department of Genetics Radboud UMC Nijmegen Netherlands Servei de Neurologia Hospital del Mar Barcelona Spain Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED) National Institute of Health Carlos III Madrid Spain Glenn Biggs Institute for Alzheimer’s & Ne

Proteomic studies have been instrumental in identifying brain, cerebrospinal fluid and plasma proteins associated with Alzheimer’s disease (AD). Here, we comprehensively examined 6,905 aptamers corresponding to 6,106 unique proteins in plasma in more than 3,300 well-characterized individuals to identify new proteins, pathways and predictive models for AD. We identified 416 proteins (294 new) associated with clinical AD status and validated the findings in two external datasets representing more than 7,000 samples. AD-related proteins reflected blood–brain barrier disruption and other processes implicated in AD, such as lipid dysregulation or immune responses. A machine learning model was used to identify a set of seven proteins that were highly predictive of both clinical AD (area under the curve (AUC) of >0.72) and biomarker-defined AD status (AUC of >0.88), which were replicated in multiple external cohorts and orthogonal platforms. These findings underscore the potential of using plasma proteins as biomarkers for the early detection and monitoring of AD and for guiding treatment decisions. © The Author(s), under exclusive licence to Springer Nature America, Inc. 2025.

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Microbiota analysis in the hemodialysis population - Focusing on Enterobacteriaceae

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Journal of microbiology, immunology, and infection = Wei mian yu gan ran za zhi 2023年第2期56卷 311-323页

作者： Hon-Yen Wu Yi-Tsung Lin Wan-Chuan Tsai Yen-Ling Chiu Mei-Ju Ko Ju-Yeh Yang Mei-Fen Pai Hsin-Sui Hsu Shih-Ping Hsu Yu-Sen Peng Chun-Hsing Liao Department of Internal Medicine Far Eastern Memorial Hospital New Taipei City Taiwan School of Medicine College of Medicine National Yang Ming Chiao Tung University Taipei City Taiwan Institute of Epidemiology and Preventive Medicine College of Public Health National Taiwan University Taipei City Taiwan Department of Internal Medicine National Taiwan University Hospital and College of Medicine Taipei City Taiwan. Division of Infectious Diseases Department of Medicine Taipei Veterans General Hospital Taipei City Taiwan Institute of Emergency and Critical Care Medicine National Yang Ming Chiao Tung University Taipei City Taiwan. Center for General Education Lee-Ming Institute of Technology New Taipei City Taiwan. Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Graduate Institute of Medicine Yuan Ze University Taoyuan City Taiwan. Department of Dermatology Taipei City Hospital Taipei City Taiwan Department of Dermatology National Taiwan University Hospital and College of Medicine Taipei City Taiwan. Department of Internal Medicine Far Eastern Memorial Hospital New Taipei City Taiwan. School of Life Science National Taiwan Normal University Taipei City Taiwan. Department of Applied Cosmetology Lee-Ming Institute of Technology New Taipei City Taiwan Department of Healthcare Administration Asia Eastern University of Science and Technology New Taipei City Taiwan. School of Medicine College of Medicine National Yang Ming Chiao Tung University Taipei City Taiwan. Electronic address: liaoch@femh.org.tw.

BACKGROUND:Infection is a recognized risk factor for mortality among hemodialysis (HD) population, including infection caused by Enterobacteriaceae. We aimed to investigate Enterobacteriaceae in gut microbiota among HD patients and to analyze associations between microbiota and clinical parameters. METHODS:This prospective study of microbiota analysis in HD patients was conducted in April-May 2019. A control group without recent antibiotic use or hospitalization was used for comparison. Stool samples underwent 16S rRNA sequencing, using Greengenes 16S rRNA database for microbiota analysis. RESULTS:Among 96 hemodialysis (HD) patients, mean age was 61.9 ± 0.8 years and mean duration of HD was 6.5 ± 0.7 years. No significant differences were found in alpha diversity between HD and control groups (HD group 949.5, controls 898; p = 0.16) although significant between-group differences were found in beta diversity (p < 0.001). At phylum level, HD group had a higher abundance of Firmicutes and Proteobacteria, but lower abundance of Bacteriodetes. At genus level, Escherichia-Shigella complex increased among HD patients who had hospitalization with 1 year (median 0.024 vs 0.004, p = 0.054) and Klebsiella was associated with emergency room visit within 1 year among HD patients (p = 0.002). CONCLUSIONS:Alpha diversity in HD patients is not lower than that in healthy controls but significant between-group differences are found in microbiota composition according to beta diversity, due to decreased Bacteriodetes and increased Firmicutes and Proteobacteria. Deeper microbiota analyses for Enterobacteriaceae are necessary. Whether change in dietary components can help to decrease mortality among dialysis population warrants further research.

关键词： Chronic Enterobacteriaceae Gastrointestinal microbiome Hemodialysis Kidney failure Microbiota Prognosis

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Exploring disparities of teleconsultation readiness: A comparative analysis of healthcare facilities in Indonesia

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DIGITAL HEALTH 2024年 10卷

作者： Daniel Chriswinanto Adityo Nugroho Katherina Adisaputro Mitra Andini Sigilipoe Ida Ayu Triastuti Suryani Hutomo Ardhinta Bellano Septarda Aryososepti Yuwono Caraka Randi Yusuf Dedik Sulistiawan Balqis Gusbela Jason C Hsu Emily Chia-Yu Su Faculty of Medicine Universitas Kristen Duta Wacana Yogyakarta IndonesiaGraduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Faculty of Medicine Universitas Kristen Duta Wacana Yogyakarta Indonesia Faculty of Public Health Universitas Ahmad Dahlan Yogyakarta IndonesiaSchool of Public Health Taipei Medical University Taipei International PhD Program in Biotech and Healthcare Management College of Management Taipei Medical University Taipei International PhD Program in Biotech and Healthcare Management College of Management Taipei Medical University TaipeiClinical Data Center Office of Data Science Taipei Medical University TaipeiClinical Big Data Research Center Taipei Medical University Hospital TaipeiResearch Center of Health Care Industry Data Science College of Management Taipei Medical University Taipei Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University TaipeiClinical Big Data Research Center Taipei Medical University Hospital TaipeiInstitute of Biomedical Informatics College of Medicine National Yang Ming Chiao Tung University Taipei

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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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Correction: How Can Artificial Intelligence Make Medicine More Preemptive?

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Journal of medical Internet research 2020年第8期22卷 e23645页

作者： Usman Iqbal Leo Anthony Celi Yu-Chuan Jack Li Master Program in Global Health & Development PhD Program in Global Health & Health Security College of Public Health Taipei Medical University Taipei Taiwan. International Center for Health Information Technology Taipei Medical University Taipei Taiwan. Laboratory for Computational Physiology Massachusetts Institute of Technology Cambridge MA United States. Division of Pulmonary Critical Care and Sleep Medicine Beth Israel Deaconess Medical Center Boston MA United States. Department of Biostatistics Harvard TH Chan School of Public Health Harvard University Boston MA United States. Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan. Department of Dermatology Taipei Municipal Wan-Fang Hospital Taipei Taiwan. International Medical Informatics Association Geneva Switzerland.

[This corrects the article DOI: .].

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Author Correction: Rare variant analyses in 51,256 type 2 diabetes cases and 370,487 controls reveal the pathogenicity spectrum of monogenic diabetes genes

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Nature genetics 2025年第4期57卷 1060页

作者： Alicia Huerta-Chagoya Philip Schroeder Ravi Mandla Jiang Li Lowri Morris Maheak Vora Ahmed Alkanaq Dorka Nagy Lukasz Szczerbinski Jesper G S Madsen Silvia Bonàs-Guarch Fanny Mollandin Joanne B Cole Bianca Porneala Kenneth Westerman Josephine H Li Toni I Pollin Jose C Florez Anna L Gloyn David J Carey Inês Cebola Uyenlinh L Mirshahi Alisa K Manning Aaron Leong Miriam Udler Josep M Mercader Programs in Metabolism and Medical & Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA. Center for Genomic Medicine Massachusetts General Hospital Boston MA USA. Diabetes Unit Massachusetts General Hospital Boston MA USA. Graduate Program in Genomics and Computational Biology University of Pennsylvania Philadelphia PA USA. Department of Genomic Health Geisinger Danville PA USA. Section of Genetics and Genomics Department of Metabolism Digestion and Reproduction Imperial College London London UK. National Heart and Lung Institute Faculty of Medicine London UK. Department of Endocrinology Diabetology and Internal Medicine Medical University of Bialystok Bialystok Poland. Clinical Research Centre Medical University of Bialystok Bialystok Poland. Institute of Mathematics and Computer Science University of Southern Denmark Odense Denmark. The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease Broad Institute of MIT and Harvard Cambridge MA USA. Department of Biochemistry and Molecular Biology University of Southern Denmark Odense Denmark. Centre for Genomic Regulation The Barcelona Institute of Science and Technology Barcelona Spain. Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas Madrid Spain. Department of Metabolism Digestion and Reproduction Imperial College London London UK. Department of Medicine Harvard Medical School Boston MA USA. Division of Endocrinology Boston Children's Hospital Boston MA USA. Department of Biomedical Informatics University of Colorado School of Medicine Aurora CO USA. Division of General Internal Medicine Massachusetts General Hospital Boston MA USA. Department of Medicine Massachusetts General Hospital Boston MA USA. University of Maryland School of Medicine Baltimore MD USA. Endocrine Division Massachusetts General Hospital Boston MA USA. Department of Pediatrics Division of Endocrinology Stanford School of Medicine Stanford

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