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

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

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

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

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

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Author Correction: Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery

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Nature neuroscience 2025年第1期28卷 216页

作者： Boyan Bonev Gonçalo Castelo-Branco Fei Chen Simone Codeluppi M Ryan Corces Jean Fan Myriam Heiman Kenneth Harris Fumitaka Inoue Manolis Kellis Ariel Levine Mo Lotfollahi Chongyuan Luo Kristen R Maynard Mor Nitzan Vijay Ramani Rahul Satijia Lucas Schirmer Yin Shen Na Sun Gilad S Green Fabian Theis Xiao Wang Joshua D Welch Ozgun Gokce Genevieve Konopka Shane Liddelow Evan Macosko Omer Ali Bayraktar Naomi Habib Tomasz J Nowakowski Helmholtz Pioneer Campus Helmholtz Zentrum München Neuherberg Germany. Physiological Genomics Biomedical Center Ludwig-Maximilians-Universität München Munich Germany. Laboratory of Molecular Neurobiology Department of Medical Biochemistry and Biophysics Karolinska Institutet Stockholm Sweden. The Broad Institute of MIT and Harvard Cambridge MA USA. Moleculent Stockholm Sweden. Gladstone Institute of Neurological Disease San Francisco CA USA. Gladstone Institute of Data Science and Biotechnology San Francisco CA USA. Department of Neurology University of California San Francisco San Francisco CA USA. Department of Biomedical Engineering Johns Hopkins University Baltimore MD USA. Department of Brain and Cognitive Sciences MIT Cambridge MA USA. The Picower Institute for Learning and Memory MIT Cambridge MA USA. UCL Queen Square Institute of Neurology University College London London UK. Institute for the Advanced Study of Human Biology (WPI-ASHBi) Kyoto University Kyoto Japan. Computer Science and Artificial Intelligence Lab Massachusetts Institute of Technology Cambridge MA USA. Spinal Circuits and Plasticity Unit National Institute of Neurological Disorders and Stroke Bethesda MD USA. Institute of Computational Biology Helmholtz Center Munich - German Research Center for Environmental Health Neuherberg Germany. Wellcome Sanger Institute Wellcome Genome Campus Cambridge UK. Department of Human Genetics University of California Los Angeles Los Angeles CA USA. Lieber Institute for Brain Development Baltimore MD USA. Department of Psychiatry Johns Hopkins School of Medicine Baltimore MD USA. Department of Neuroscience Johns Hopkins School of Medicine Baltimore MD USA. School of Computer Science and Engineering The Hebrew University of Jerusalem Jerusalem Israel. Racah Institute of Physics The Hebrew University of Jerusalem Jerusalem Israel. Faculty of Medicine The Hebrew University of Jerusalem Jerusalem Israel. Department of Biochemis

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DCFH: A dynamic clustering approach based on fire hawk optimizer in flying ad hoc networks

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Vehicular Communications 2024年 47卷

作者： Hosseinzadeh, Mehdi Ali, Saqib Ahmad, Husham Jawad Alanazi, Faisal Yousefpoor, Mohammad Sadegh Yousefpoor, Efat Darwesh, Aso Rahmani, Amir Masoud Lee, Sang-Woong Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Department of Information Systems College of Economics and Political Science Sultan Qaboos University Al Khoudh Muscat Oman Department of Communication and Computer Engineering Cihan University-Erbil Kurdistan Region Iraq Department of Electrical Engineering College of Engineering Prince Sattam bin Abdulaziz University Al-Kharj 11942 Saudi Arabia Center of Research and Strategic Studies Lebanese French University Kurdistan Region Iraq Department of Information Technology University of Human Development Kurdistan Region Sulaymaniyah Iraq Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam 13120 South Korea

In flying ad hoc networks (FANETs), unmanned aerial vehicles (UAVs) communicate with each other without any fixed infrastructure. Because of frequent topological changes, instability of wireless communication, three-dimensional movement of UAVs, and limited resources, especially energy, FANETs deal with many challenges, especially the instability of UAV swarms. One solution to address these problems is clustering because it maintains network performance and increases scalability. In this paper, a dynamic clustering scheme based on fire hawk optimizer (DCFH) is proposed for FANETs. In DCFH, each cluster head calculates the period of hello messages in its cluster based on its velocity. Then, a fire hawk optimizer (FHO)-based dynamic clustering operation is carried out to determine the role of each UAV (cluster head (CH) or cluster member (CM)) in the network. To calculate the fitness value of each fire hawk, a fitness function is suggested based on four elements, namely the balance of energy consumption, the number of isolated clusters, the distribution of CHs, and the neighbor degree. To improve cluster stability, each CH manages the movement of its CMs and adjusts it based on its movement in the network. In the last phase, DCFH defines a greedy routing process to determine the next-hop node based on a score, which consists of distance between CHs, energy, and buffer capacity. Finally, DCFH is simulated using the network simulator version 2 (NS2), and its performance is compared with three methods, including the mobility-based weighted cluster routing scheme (MWCRSF), the dynamic clustering mechanism (DCM), and the Grey wolf optimization (GWO)-based clustering protocol. The simulation results show that DCFH well manages the number of clusters in the network. It improves the cluster construction time (about 55.51%), cluster lifetime (approximately 11.13%), energy consumption (about 15.16%), network lifetime (about 2.6%), throughput (approximately 3.9%), packet deliver

关键词： Artificial intelligence (AI) Clustering Flying ad hoc networks (FANETs) Optimization algorithms Unmanned aerial vehicles (UAVs)

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MammoFL: Mammographic Breast Density Estimation using Federated learning

arXiv

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arXiv 2022年

作者： Muthukrishnan, Ramya Heyler, Angelina Katti, Keshava Pati, Sarthak Mankowski, Walter Alahari, Aprupa Sanborn, Michael Conant, Emily F. Scott, Christopher Winham, Stacey Vachon, Celine Chaudhari, Pratik Kontos, Despina Bakas, Spyridon Department of Computer and Information Science University of Pennsylvania PhiladelphiaPA United States Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA United States Massachusetts Institute of Technology CambridgeMA United States Research and Development GSK CambridgeMA United States Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine Indiana IN United States Research Center for Federated Learning in Medicine Indiana University School of Medicine Indiana IN United States University of Pennsylvania PhiladelphiaPA United States Department of Pathology & Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Health Sciences Research Mayo Clinic RochesterMN United States

In this study, we automate quantitative mammographic breast density estimation with neural networks and show that this tool is a strong use case for federated learning on multi-institutional datasets. Our dataset included bilateral CC-view and MLO-view mammographic images from two separate institutions. Two U-Nets were separately trained on algorithm-generated labels to perform segmentation of the breast and dense tissue from these images and subsequently calculate breast percent density (PD). The networks were trained with federated learning and compared to three non-federated baselines, one trained on each single-institution dataset and one trained on the aggregated multi-institution dataset. We demonstrate that training on multi-institution datasets is critical to algorithm generalizability. We further show that federated learning on multi-institutional datasets improves model generalization to unseen data at nearly the same level as centralized training on multi-institutional datasets, indicating that federated learning can be applied to our method to improve algorithm generalizability while maintaining patient privacy. Copyright © 2022, The Authors. All rights reserved.

关键词： Image segmentation

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Surveillance of avian influenza through bird guano in remote regions of the global south to uncover transmission dynamics

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Nature communications 2025年第1期16卷 4900页

作者： Dhammika Leshan Wannigama Mohan Amarasiri Phatthranit Phattharapornjaroen Cameron Hurst Charin Modchang John Jefferson V Besa Kazuhiko Miyanaga Longzhu Cui Stefan Fernandez Angkana T Huang Puey Ounjai W K C P Werawatte Ali Hosseini Rad S M Porames Vatanaprasan Dylan John Jay Thammakorn Saethang Sirirat Luk-In Phitsanuruk Kanthawee Wanwara Thuptimdang Ratana Tacharoenmuang Bernadina Cynthia S P H Spencer Vitharana Natharin Ngamwongsatit Hitoshi Ishikawa Takashi Furukawa Yangzhong Wang Andrew C Singer Naveen Kumar Devanga Ragupathi Tanittha Chatsuwan Kazunari Sei Asuka Nanbo Asada Leelahavanichkul Talerngsak Kanjanabuch Hiroshi Hamamoto Paul G Higgins Daisuke Sano Anthony Kicic José O Valdebenito Jonas Bonnedahl Sam Trowsdale Parichart Hongsing Aisha Khatib Kenji Shibuya Shuichi Abe Department of Infectious Diseases Faculty of Medicine Yamagata University Yamagata Japan. leshanwannigama@med.id.yamagata-u.ac.jp. Department of Infectious Diseases and Infection Control Yamagata Prefectural Central Hospital Yamagata Japan. leshanwannigama@med.id.yamagata-u.ac.jp. Pathogen Hunter's Research Collaborative Team Department of Infectious Diseases and Infection Control Yamagata Prefectural Central Hospital Yamagata Japan. leshanwannigama@med.id.yamagata-u.ac.jp. Yamagata Prefectural University of Health Sciences Kamiyanagi Yamagata Japan. leshanwannigama@med.id.yamagata-u.ac.jp. The Lygodium Ceylon Health and Environmental Policy Research Center Colombo Sri Lanka. leshanwannigama@med.id.yamagata-u.ac.jp. Biofilms and Antimicrobial Resistance Consortium of ODA receiving countries The University of Sheffield Sheffield UK. leshanwannigama@med.id.yamagata-u.ac.jp. Pathogen Hunter's Research Collaborative Team Department of Infectious Diseases and Infection Control Yamagata Prefectural Central Hospital Yamagata Japan. Department of Civil and Environmental Engineering Graduate School of Engineering Tohoku University Sendai Miyagi Japan. Faculty of Health Science Technology Chulabhorn Royal Academy Bangkok Thailand. HRH Princess Chulabhorn Disaster and Emergency Medicine Center Chulabhorn Royal Academy Bangkok Thailand. Department of Clinical Epidemiology Faculty of Medicine Thammasat University Rangsit Thailand. Center of Excellence in Applied Epidemiology Thammasat University Rangsit Thailand. Mater Research Institute University of Queensland Brisbane QLD Australia. Biophysics Group Department of Physics Faculty of Science Mahidol University Bangkok Thailand. Centre of Excellence in Mathematics MHESI Bangkok Thailand. Thailand Center of Excellence in Physics Ministry of Higher Education Science Research and Innovation Bangkok Thailand. Department of Infectious Diseases and Infection Control Yamagata Prefectural Central Hospital Yamagata Japan. Co

Avian influenza viruses (AIVs) pose a growing global health threat, particularly in low- and middle-income countries (LMICs), where limited surveillance capacity and under-resourced healthcare systems hinder timely detection and response. Migratory birds play a significant role in the transboundary spread of AIVs, yet data from key regions along migratory flyways remain sparse. To address these surveillance gaps, we conducted a study between December 2021 and February 2023 using fresh bird guano collected across 10 countries in the Global South. Here, we show that remote, uninhabited regions in previously unsampled areas harbor a high diversity of AIV strains, with H5N1 emerging as the most prevalent. Some of these H5N1 samples also carry mutations that may make them less responsive to the antiviral drug oseltamivir. Our findings documented the presence of AIVs in several underrepresented regions and highlighted critical transmission hotspots where viral evolution may be accelerating. These results underscore the urgent need for geographically targeted surveillance to detect emerging variants, inform public health interventions, and reduce the risk of zoonotic spillover.

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Charting brain growth in tandem with brain templates at school age

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science Bulletin 2020年第22期65卷 1924-1934,M0004页

作者： Hao-Ming Dong F.Xavier Castellanos Ning Yang Zhe Zhang Quan Zhou Ye He Lei Zhang Ting Xu Avram J.Holmes B.T.Thomas Yeo Feiyan Chen Bin Wang Christian Beckmann Tonya White Olaf Sporns Jiang Qiu Tingyong Feng Antao Chen Xun Liu Xu Chen Xuchu Weng Michael P.Milham Xi-Nian Zuo Research Center for Lifespan Development of Brain and Mind Institute of PsychologyChinese Academy of Sciences(CAS)Beijing 100101China State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal UniversityBeijing 100875China Department of Child and Adolescent Psychiatry New York University School of MedicineNew YorkNY 10016USA Nathan Kline Institute for Psychiatric Research OrangeburgNew YorkNY 10962USA Department of Psychology University of CASBeijing 100049China Department of Psychological and Brain Sciences Indiana UniversityBloomingtonIN 47405USA Child Mind Institute New YorkNY 10022USA Department of Psychology Yale UniversityNew HavenCT 06511USA Department of Electrical and Computer Engineering Clinical Imaging Research CentreN.1 Institute for Health and Memory Networks ProgramNational University of SingaporeSingapore 117574Singapore Department of Physics Zhejiang UniversityHangzhou 310058China Binzhou Medical University Yantai 264100China Donders Institute Radboud University Medical CentreNijmegen 6525 ENNetherlands Department of Child and Adolescent Psychiatry Erasmus UniversityRotterdam 3000 CBNetherlands Department of Radiology Erasmus University Medical CentreRotterdam 3000 CANetherlands School of Psychology Southwest UniversityChongqing 400715China Institute for Brain Research and Rehabilitation South China Normal UniversityGuangzhou 510631China Institute of Children Health Changzhou Children’s HospitalChangzhou 213003China Key Laboratory of Brain and Education School of Education ScienceNanning Normal UniversityNanning 530001China CAS Key Laboratory of Behavioral Science Institute of PsychologyBeijing 100101China IDG/McGovern Institute for Brain Research Beijing Normal UniversityBeijing 100875China

Brain growth charts and age-normed brain templates are essential resources for researchers to eventually contribute to the care of individuals with atypical developmental *** present work generates age-normed brain templates for children and adolescents at one-year intervals and the corresponding growth charts to investigate the influences of age and ethnicity using a common pediatric neuroimaging *** accelerated longitudinal cohorts with the identical experimental design were implemented in the United States and *** magnetic resonance imaging(MRI)of typically developing school-age children(TDC)was obtained up to three times at nominal intervals of1.25 *** protocol generated and compared population-and age-specific brain templates and growth charts,respectively.A total of 674 Chinese pediatric MRI scans were obtained from 457 Chinese TDC and 190 American pediatric MRI scans were obtained from 133 American ***-and age-specific brain templates were used to quantify warp cost,the differences between individual brains and brain *** growth charts for labeled brain network areas were *** analyses of cost functions supported the necessity of age-specific and ethnicitymatched brain templates,which was confirmed by growth chart *** analyses revealed volumetric growth differences between the two ethnicities primarily in lateral frontal and parietal areas,regions which are most variable across individuals in regard to their structure and ***-and ethnicity-specific brain templates facilitate establishing unbiased pediatric brain growth charts,indicating the necessity of the brain charts and brain templates generated in *** templates and growth charts as well as related codes have been made freely available to the public for open neuroscience(https://***/zuoxinian/CCS/tree/master/H3/Growth Charts).

关键词： Growth charts Neuroimaging Ethnicity Brain templates development

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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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An Open-Source Knowledge Graph Ecosystem for the Life sciences

arXiv

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arXiv 2023年

作者： Callahan, Tiffany J. Tripodi, Ignacio J. Stefanski, Adrianne L. Cappelletti, Luca Taneja, Sanya B. Wyrwa, Jordan M. Casiraghi, Elena Matentzoglu, Nicolas A. Reese, Justin Silverstein, Jonathan C. Hoyt, Charles Tapley Boyce, Richard D. Malec, Scott A. Unni, Deepak R. Joachimiak, Marcin P. Robinson, Peter N. Mungall, Christopher J. Cavalleri, Emanuele Fontana, Tommaso Valentini, Giorgio Mesiti, Marco Gillenwater, Lucas A. Santangelo, Brook Vasilevsky, Nicole A. Hoehndorf, Robert Bennett, Tellen D. Ryan, Patrick B. Hripcsak, George Kahn, Michael G. Bada, Michael Baumgartner, William A. Hunter, Lawrence E. Computational Bioscience Program University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY10032 United States Computer Science Department Interdisciplinary Quantitative Biology University of Colorado Boulder BoulderCO80301 United States AnacletoLab Computer Science Department University of Milan 20122 Italy Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Department of Physical Medicine and Rehabilitation School of Medicine University of Colorado Anschutz Medical Campus AuroraCO80045 United States Division of Environmental Genomics and Systems Biology Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Semanticly Ltd Athens Greece Department of Biomedical Informatics University of Pittsburgh School of Medicine PittsburghPA15206 United States Laboratory of Systems Pharmacology Harvard Medical School BostonMA02115 United States Division of Translational Informatics University of New Mexico School of Medicine AlbuquerqueNM87131 United States SIB Swiss Institute of Bioinformatics Basel Switzerland Berlin Institute of Health at Charité-Universitatsmedizin Berlin10117 Germany ELLIS European Laboratory for Learning and Intelligent Systems Germany Department of Biomedical Informatics University of Colorado School of Medicine AuroraCO80045 United States Data Collaboration Center Critical Path Institute 1840 E River Rd. Suite 100 TucsonAZ85718 United States Computer Electrical and Mathematical Sciences & Engineering Division Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Pediatrics University of Colorado School of Medicine AuroraCO80045 United States Janssen Research and Development RaritanNJ08869 United States Division of General Internal Medicine University of Colorado School of Medicine A

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoints and abstraction algorithms), and benchmarks (e.g., prebuilt KGs and embeddings). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability. © 2023, CC BY.

关键词： Ecosystems

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Eye tracking: empirical foundations for a minimal reporting guideline

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

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

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

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

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Supplemental educational program to heighten the impact of research - an opportunity for OA imaging

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Osteoarthritis imaging 2023年第2期3卷 100155页

作者： Deborah Burstein Rudolph Mitchell Emery Brown Martha L Gray Associate Professor Department of Radiology Beth Israel Deaconess Medical Center Harvard Medical School 330 Brookline Avenue Boston MA USA. Associate Director for Research and Evaluation in the Teaching + Learning Lab Massachusetts Institute of Technology Cambridge MA USA. Edward Hood Taplin Professor of Computational Neuroscience and Health Sciences and Technology Department of Brain and Cognitive Sciences and Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA USA. J.W. Kieckefer Professor of Medical and Electrical Engineering Department of Electrical Engineering and Computer Science and Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA USA.

Objective:To develop and evaluate a supplementary educational program ("IMPACT") centered on enabling participants to consider specifically and articulate explicitly the best path for and potential impact of their research. Design:Participants (trainees) and faculty mentors were from all areas of biomedical research. The group worked longitudinally in small, rotating groups, through a process of developing a written statement ("Impact Statement"), an overview ("Impact Storyline") and an oral presentation ("Impact Case") of their work. Results:One hundred and eighty-seven Fellows enrolled in the program. Of the 179 (96%) Fellows who completed the program, 159 (89%) responded to a post-program survey; 94% indicated that IMPACT was a significant learning experience, 89% indicated that they were more able to identify the long-term potential of their research, 95% felt more able to talk about their work to diverse audiences. Conclusion:This voluntary educational program was appreciated by the participants and led to increased confidence in their ability to drive their science towards a clear impact and communicating that potential to others. This type of program may aid in redirecting some of the efforts and resources of imaging in OA from the large focus on technical developments to more direct biological and clinical questions which might be resolved with current technology.

关键词： Communication Diversity Impact Research

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