检索结果-内蒙古大学图书馆

Feasibility study of Information Technology Investment (a case study of ODOO ERP: Project Management Module Implementation in Indonesia Based Company) 10

Feasibility study of Information Technology Investment (a ca...

引用

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

作者： Jingga, Fredy Kosala, Raymond Supangkat, Suhono Harso Ranti, Benny School of Information Systems Bina Nusantara University Computer Science Department BINUS Graduate Program-Doctor of Computer Science Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Computer Science Department BINUS Graduate Program-Doctor of Computer Science Jakarta11480 Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9781728148809

IT Governance are one of the needs in managing Enterprise Level IT. This study shows part of the decision domain of IT Governance Help, which are IT Investment and Prioritization. The purpose of this study is to determine feasibility of implementation open source ERP system Odoo project management module that replaces current system based on operational, economic, and technical aspects. The method used are Fit/Gap Analysis and Cost Benefit model using economic impact worksheet. The analysis is done by comparing the requirement with features owned by current system and Odoo ERP system. This research resulted that Odoo ERP System is feasible in the 3 aspects, the operational feasibility Odoo reaching 72% Fit, the economic feasibility with ROI of 84%, and technical feasibility indicating the technical requirement can be fulfilled by leasing AWS server. The conclusion is the open source ERP system Odoo project management module is feasible to be applied in terms of 3 aspects that have been studied, those are operational, economic, and technical. © 2019 IEEE.

关键词： Cost benefit analysis

来源：评论

学校读者我要写书评

暂无评论

Applying mobility business intelligence concept in analyzing oil palm plantation productivity

引用

AIP Conference Proceedings 2024年第1期3026卷

作者： Andreas Wahyu Krisdiarto Irya Wisnubhadra Anzaludin Samsinga Perbangsa Teddy Suparyanto Bens Pardamean Faculty of Agricultural Technology STIPER Institute of Agriculture Yogyakarta Indonesia Informatics Engineering Department Atma Jaya Yogyakarta University Yogyakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia BDSRC Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

The palm oil business employs almost 20 million people, generates USD 21 billion in revenue, and plays a vital role in Indonesia’s social economy. The Fresh Fruit Bunches (FFB) to Palm Oil Mills (POM) distribution system is one important aspect of fruit quality. Three steps are involved in getting Oil Palm FFB from the plantation to the POM. The first part of the procedure involves cutting FFB from the tree, the second stage involves gathering the fruit at a fruit collection point (FCP), and the third stage involves transporting the fruit to the palm oil mill (POM). As of now, the cost of the FFB transportation is still considerable, accounting for roughly 15% to 20% of the FFB pricing. The use of the Business Intelligence (BI) idea in the oil palm harvesting system is presented in this study as a foundation for creating web-based applications.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhancing Real-Time Motion Estimation through Approximate High-Level Synthesis

Enhancing Real-Time Motion Estimation through Approximate Hi...

引用

IEEE computer Society Annual Symposium on VLSI

作者： Marcos T. Leipnitz Murilo R. Perleberg Marcelo S. Porto Gabriel L. Nazar Informatics Institute Federal University of Rio Grande do Sul Porto Alegre Brazil Graduate Program in Computer Science Video Technology Research Group Federal University of Pelotas Pelotas Brazil

ISBN: (数字)9781728157757

ISBN: (纸本)9781728157764

As newer video encoding standards are proposed, higher compression rates are attained at the cost of increasing computational complexity. Thus, achieving real-time video encoding for higher resolutions and frame rates becomes challenging, especially when considering costs in area and energy. Approximate accelerators can be used to alleviate the costs of critical kernels, such as motion estimation, but their design becomes costly when maintaining a short time-to-market is desirable, especially in a scenario of standards that evolve frequently. In this work, we propose the use of High-Level Synthesis (HLS) to quickly generate Pareto-optimal motion estimation approximate accelerators. We propose a throughput-aware approximate HLS tool able to attain area savings between 6% and 55% or improve the frame rate by between 5.5% and 78%, with PSNR reductions varying from 0.32 dB to 3.4 dB, respectively.

关键词： Standards Encoding Real-time systems Delays Tools Throughput Motion estimation

来源：评论

学校读者我要写书评

暂无评论

Big Data Governance for Building A Smart Cities 10

Big Data Governance for Building A Smart Cities

引用

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

作者： Zulkarnain, Novan Kosala, Raymond Ranti, Benny Supangkat, Suhono Harso Bina Nusantara University Computer Science Department BINUS Graduate Program Doctor of Computer Science Information Systems Department Jakarta11480 Indonesia Universitas Indonesia Computer Science Department Depok16424 Indonesia Institute of Technology Bandung School of Electrical and Informatics Engineering Bandung40132 Indonesia

ISBN: (纸本)9781728148809

There have been many studies conducted related to Smart City, IT Governance and Big Data. In this study aims to find out how the relationship between the three and how to form a framework to explain it. The methodology used is qualitative by looking for some literature on smart city framework, IT Governance framework, and a Big Data framework. From these results, an overall picture of the relationship between the three is concluded, where Big Data has a role in IT Governance. and also the relationship of IT Governance to the realization of Smart City. And the final results of this study produce a framework to explain the relationship between Smart City, IT Governance and Big Data. © 2019 IEEE.

关键词： Big data

来源：评论

学校读者我要写书评

暂无评论

Development of Educational Game for Indonesian History Lesson Focusing on Indonesia Pre-Literacy Relics “Prehistoria” for Grade X High School Student

Development of Educational Game for Indonesian History Lesso...

引用

International Conference on computer Engineering, Network and Intelligent Multimedia (CENIM)

作者： Supeno Mardi Susiki Nugroho Muhammad Daffa Abiyyu Rahman Mochamad Hariadi Muhammad Arief Faculty of Intelligent Electrical and Informatics Technology Sepuluh Nopember Institute of Technology Surabaya Indonesia Media Science Program Graduate School of Bionics Computer and Media Sciences Tokyo University of Technology Tokyo Japan

ISBN: (数字)9781728182834

ISBN: (纸本)9781728182841

The development of game technology has opened up new possibilities in learning methods through educational games. However, in Indonesia, the application of this concept is lacking. One lesson that can benefit from educational games is Indonesian History in class X, with a focus on history in the pre-literacy period. This study aims to create a puzzle-adventure type of digital game that can help educate students about the pre-literacy period, developed with Unity, and uses WebGL as a target device while referring to Indonesia's revised 2013 national curriculum. The result of this research is the creation of a digital game that can be played on average of 254.36 seconds, with potential for educational use. A survey result shows that many of the ancient objects and some of the ages are well-recognized. However, the survey also shows many of the gameplay aspects, such as UI, gameplay features, and adventure elements, need improvements.

关键词： Games History Education Flowcharts informatics Bars Tutorials

来源：评论

学校读者我要写书评

暂无评论

The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net

引用

European Radiology 2023年第9期33卷 6157-6167页

作者： Li, Ya-Hui Lin, Shao-Chieh Chung, Hsiao-Wen Chang, Chia-Ching Peng, Hsu-Hsia Huang, Teng-Yi Shen, Wu-Chung Tsai, Chon-Haw Lo, Yu-Chien Lee, Tung-Yang Juan, Cheng-Hsuan Juan, Cheng-En Chang, Hing-Chiu Liu, Yi-Jui Juan, Chun-Jung Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Department of Medical Imaging China Medical University Hsinchu Hospital No. 199 Sec. 1 Xinglong Rd. Zhubei City Hsinchu County 302 Hsinchu Taiwan Ph.D. Program in Electrical and Communication Engineering Feng Chia University Taichung Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan Department of Management Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Radiology School of Medicine College of Medicine China Medical University Taichung Taiwan Department of Medical Imaging Medical University Hospital Taichung Taiwan Department of Neurology China Medical University Hospital Taichung Taiwan Cheng Ching Hospital Taichung Taiwan Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Department of Automatic Control Engineering Feng Chia University No. 100 Wenhwa Rd. Seatwen Taichung 40724 Taiwan Department of Biomedical Engineering The Chinese University of Hong Kong ERB1112 11/F William M.W. Mong Engineering Building N.T Shatin Hong Kong Multi-Scale Medical Robotics Center The Chinese University of Hong Kong N.T Shatin Hong Kong Department of Biomedical Engineering National Defense Medical Center Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Background: To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic stroke (AIS) lesion. Methods: This study retrospectively enrolled a total of 212 patients having AIS. Four combos, including ADC-ADC-ADC (AAA), DWI-ADC-ADC (DAA), DWI-DWI-ADC (DDA), and DWI-DWI-DWI (DDD), were used as input images, respectively. Three ADC thresholds including 0.6, 0.8 and 1.8 × 10^–3 mm²/s were applied. Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of U-Nets. Nonparametric Kruskal–Wallis test with Tukey–Kramer post-hoc tests were used for comparison. A p <.05 was considered statistically significant. Results: The DSC significantly varied among different combos of images and different ADC thresholds. Hybrid U-Nets outperformed uniform U-Nets at ADC thresholds of 0.6 × 10^–3 mm²/s and 0.8 × 10^–3 mm²/s (p <.001). The U-Net with imaging combo of DDD had segmentation performance similar to hybrid U-Nets at an ADC threshold of 1.8 × 10^–3 mm²/s (p =.062 to 1). The U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10^–3 mm²/s achieved the highest DSC in the segmentation of AIS lesion. Conclusions: The segmentation performance of U-Net for AIS varies among the input imaging combos and ADC thresholds. The U-Net is optimized by choosing the imaging combo of DAA at an ADC threshold of 0.6 × 10^–3 mm²/s in segmentating AIS lesion with highest DSC. Key Points: • Segmentation performance of U-Net for AIS differs among input imaging combos. • Segmentation performance of U-Net for AIS differs among ADC thresholds. • U-Net is optimized using DAA with ADC = 0.6 × 10^–3mm²/s. © 2023, The Author(s), under exclusive licence to European Society of Radiology.

关键词： Deep Learning Diffusion Magnetic Resonance Imaging Ischemic Stroke Neural Networks, computer Retrospective Study

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

引用

Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Step Towards a Reinforcement Learning De Novo Genome Assembler

Research Square

引用

Research Square 2021年

作者： Padovani, Kleber Xavier, Roberto Carvalho, André Reali, Anna Chateau, Annie Alves, Ronnie Federal University of Pará Computer Science Graduate Program Belém66.075-110 Brazil University of the State of Amazonas Itacoatiara-AM 69.101-416 Brazil Vale Technology Institute Sustainable Development Belém66.055-090 Brazil Institute of Mathematics and Computer Sciences University of São Paulo São Carlos-SP 13566-590 Brazil Polytechnic School University of São Paulo São Paulo-SP 05508-010 Brazil LIRMM-Montpellier Laboratory of Informatics Robotics and Microelectronics Univ Montpellier CNRS Montpellier France

Genome assembly is one of the most relevant and computationally complex tasks in genomics projects. It aims to reconstruct a genome through the analysis of several small textual fragments of such genome — named reads. Ideally, besides ignoring any errors contained in reads, the reconstructed genome should also optimally combine these reads, thus reaching the original genome. The quality of the genome assembly is relevant because the more reliable the genomes, the more accurate the understanding of the characteristics and functions of living beings, and it allows generating many positive impacts on society, including the prevention and treatment of diseases. The assembly becomes even more complex (and it is termed de novo in this case) when the assembler software is not supplied with a similar genome to be used as a reference. Current assemblers have predominantly used heuristic strategies on computational graphs. Despite being widely used in genomics projects, there is still no irrefutably best assembler for any genome, and the proper choice of these assemblers and their configurations depends on Bioinformatics experts. The use of reinforcement learning has proven to be very promising for solving complex activities without human supervision during their learning process. However, their successful applications are predominantly focused on fictional and entertainment problems - such as games. Based on the above, this work aims to shed light on the application of reinforcement learning to solve this relevant real-world problem, the genome assembly. By expanding the only approach found in the literature that addresses this problem, we carefully explored the aspects of intelligent agent learning, performed by the Q-learning algorithm, to understand its suitability to be applied in scenarios whose characteristics are more similar to those faced by real genome projects. The improvements proposed here include changing the previously proposed reward system and including stat

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

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

arXiv

引用

arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：