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An artificial intelligence and blockchain technology-based data management framework for multicenter randomized controlled trials

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science Bulletin 2025年第6期70卷 856-860页

作者： Dongyuan Yun Xiaohang Wu Xi Chen Yahan Yang Yuanjun Shang Shaopeng Liu Dinesh Visva Gunasekeran Duoru Lin Lixue Liu Lanqin Zhao Wenben Chen Jingjing Chen Ling Jin Yuxuan Wu Jiaming Hong Weiling Hu Zhenzhe Lin Carol Y.Cheung Xiang Chen Peichen Xie Zhenzhen Liu Changhai Ding Patrick Yu-Wai-Man Keming Yu Daniel Shu Wei Ting Yizhi Liu Zibin Zheng Gansen Zhao Zhihua Xia Tien Yin Wong Haotian Lin on behalf of Advanced Technologies Improve Clinical Trial(ATICT)Group State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangdong Provincial Clinical Research Center for Ocular DiseasesGuangzhou 510000China School of Biomedical Engineering Sun Yat-sen UniversityGuangzhou 510000China School of Computer Science Guangdong Polytechnic Normal UniversityGuangzhou 510000China Singapore Eye Research Institute Singapore National Eye CenterSingapore999002Singapore School of Medical Information Engineering Guangzhou University of Chinese MedicineGuangzhou 510000China Department of Ophthalmology and Visual Sciences The Chinese University of Hong KongHong Kong 999077China Zhongshan School of Medicine Sun Yat-sen UniversityGuangzhou 510000China Clinical Research Center Zhujiang HospitalSouthern Medical UniversityGuangzhou 510000China Cambridge Center for Brain Repair and MRC Mitochondrial Biology Unit Department of Clinical NeurosciencesUniversity of CambridgeCambridgeCB20XYUnited Kingdom UCL Institute of Ophthalmology University College LondonLondonWC1E 6BTUnited Kingdom School of Data and Computer Science Sun Yat-sen UniversityGuangzhou 510000China School of Computer Science South China Normal UniversityGuangzhou 510000China College of Cyber Security Jinan UniversityGuangzhou 510000China Duke-NUS Medical School Singapore999002Singapore Tsinghua Medicine Tsinghua UniversityBeijing 100010China Hainan Eye Hospital and Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityHaikou 570100China Centre for Precision Medicine and Department of Genetics and Biomedical Informatics Zhongshan School of MedicineSun Yat-sen UniversityGuangzhou 510000China 不详

Randomized controlled trials(RCTs)provide optimal evidence of the effectiveness and safety of a new drug,a new medical device,or a new therapeutic strategy with the necessary scientific design[1].Traditional electronic data collection(EDC)systems for clinical trials primarily focus on data entry,validation,and compliance ***,their reliance on centralized architectures introduces vulnerabilities in data security and integrity。

关键词： Artificial Intelligence Multicenter Randomized Controlled Trials Data Security scientific design traditional Blockchain Technology clinical trials randomized controlled trials rcts provide centralized architectures

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TOWARDS NEXT-GENERATION MEDICAL AGENT: HOW o1 IS RESHAPING DECISION-MAKING IN MEDICAL SCENARIOS

arXiv

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arXiv 2024年

作者： Xu, Shaochen Zhou, Yifan Liu, Zhengliang Wu, Zihao Zhong, Tianyang Zhao, Huaqin Li, Yiwei Jiang, Hanqi Pan, Yi Chen, Junhao Lu, Jin Zhang, Wei Zhang, Tuo Zhang, Lu Zhu, Dajiang Li, Xiang Liu, Wei Li, Quanzheng Sikora, Andrea Zhai, Xiaoming Xiang, Zhen Liu, Tianming School of Computing University of Georgia AthensGA United States Department of Mathematical and Statistical Sciences University of Alberta Edmonton Canada School of Computer and Cyber Sciences Augusta University AugustaGA United States Department of Computer Science Indiana University IndianapolisIN United States Department of Computer Science and Engineering University of Texas ArlingtonTX United States Department of Radiology Massachusetts General Hospital Harvard Medical School BostonMA United States Department of Radiation Oncology Mayo Clinic ScottsdaleAZ United States Department of Biomedical Informatics University of Colorado AuroraCO United States Department of Mathematics Science and Social Studies Education University of Georgia AthensGA United States AI4STEM Education Center University of Georgia AthensGA United States National GENIUS Center University of Georgia AthensGA United States

Artificial Intelligence (AI) has become essential in modern healthcare, with large language models (LLMs) offering promising advances in clinical decision-making. Traditional model-based approaches, including those leveraging in-context demonstrations and those with specialized medical fine-tuning, have demonstrated strong performance in medical language processing but struggle with real-time adaptability, multi-step reasoning, and handling complex medical tasks. Agent-based AI systems address these limitations by incorporating reasoning traces, tool selection based on context, knowledge retrieval, and both short- and long-term memory. These additional features enable the medical AI agent to handle complex medical scenarios where decision-making should be built on real-time interaction with the environment. Therefore, unlike conventional model-based approaches that treat medical queries as isolated questions, medical AI agents approach them as complex tasks and behave more like human doctors. In this paper, we study the choice of the backbone LLM for medical AI agents, which is the foundation for the agent’s overall reasoning and action generation. In particular, we consider the emergent o1 model and examine its impact on agents’ reasoning, tool-use adaptability, and real-time information retrieval across diverse clinical scenarios, including high-stakes settings such as intensive care units (ICUs). Our findings demonstrate o1’s ability to enhance diagnostic accuracy and consistency, paving the way for smarter, more responsive AI tools that support better patient outcomes and decision-making efficacy in clinical practice. © 2024, CC BY.

关键词： Modeling languages

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Hard hat wearing detection based on head keypoint localization

arXiv

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arXiv 2021年

作者： Wójcik, Bartosz Zarski, Mateusz Ksiązek, Kamil Miszczak, Jaroslaw A. Skibniewski, Miroslaw J. Faculty of Civil Engineering Silesian University of Technology Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Poland Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Poland Department of Civil & Environmental Engineering University of Maryland College Park United States

In recent years, a lot of attention is paid to deep learning methods in the context of vision-based construction site safety systems. However, still there is no reliable way to establish the relationship between supervised construction workers and their basic personal protective equipment, like hard hats. To address this problem, a novel deep learning method combining object detection, keypoint localization, and simple rule-based reasoning is proposed in this article. In tests, this solution surpassed the previous methods based on the relative bounding box position of different instances, as well as direct detection of hard hat wearers and non-wearers. Achieving MS COCO style overall AP of 67.5% compared to 66.4% and 66.3% achieved by the above-mentioned approaches, with class specific AP for hard hat non-wearers of 64.1% compared to 63.0% and 60.3%. The results show that the conjunction of deep learning methods with humanly-interpretable rule-based algorithm is better suited for detection of hard hat non-wearers. Code is available at barwojcik/hard-hats. © 2021, CC BY.

关键词： Object detection

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Social practices of nurse care coordination using sensor technologies e Challenges with an alert system adoption in assisted living communities for older adults

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International Journal of Nursing sciences 2021年第3期8卷 289-297,I0004页

作者： Isa Jahnke Nathan Riedel Mihail Popescu Marjorie Skubic Marilyn Rantz School of Information Science and Learning Technologies University of MissouriColumbiaMOUnited States Department of Health Management and Informatics University of MissouriColumbiaMOUnited States Department of Electrical Engineering and Computer Science University of MissouriColumbiaMOUnited States Sinclair School of Nursing University of MissouriColumbiaMOUnited States

Objectives:From the view of everyday practices and the socio-technical coordination lens,this study aimed to analyz the gap between creators’intention and the users’implementation(mainly nursing staff and social workers)of an alert system in assisted living ***:Qualitative methods were employed by way of five user interviews and focus groups with six system *** instruments were applied for data collection to analyze the different clinical workflows versus the expectations of the system development ***:Results indicate that the clinical workflow changed over time,which led to a mismatch of nurse care coordination,social practices,and technology *** results show different mental models of the socio-technical *** the coordination theory,the following recommendations could be developed to overcome the ***,it is recommended that nursing staff set goals ***,a communication rhythmwith the nursing staff and developer teams should be established,with guided questions to facilitate the conversation,to shed light on the different workflows and the difference in social practices when using sensor technologies or alert ***,a checklist for new employees should be created so they know how and on which devices to use the alert ***,the user experience with the alert system should be improved(e.g.,an improved user interface).Conclusions:This work indicates recommendations to close the mental model gap to overcome the mismatch between optimal use of the alert system and how the nursing staff is actually using it.

关键词： Community health services Health services for the aged Medical informatics applications Software

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Enabling cross-sectorial, circular economy transition in SME via digital platform integrated operational services 10th

Enabling cross-sectorial, circular economy transition in SME...

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10th CIRP Sponsored Conference on Digital Enterprise Technologies - Digital Technologies as Enablers of Industrial Competitiveness and Sustainability, DET 2020

作者： Pedone, Gianfranco Beregi, Richárd Kis, Krisztián Balázs Colledani, Marcello Centre of Excellence in Production Informatics and Control Institute for Computer Science and Control Kende u. 13-17 Budapest1111 Hungary Politecnico di Milano Department of Mechanical Engineering Via la Masa 1 Milan20156 Italy

Circular Economy (CE) is an emerging paradigm aiming at establishing a new sustainable development path by decoupling economic growth and resource consumption. The main limitation in current production models is that manufacturing and de-and remanufacturing operations are carried out independently and without sharing information and economic benefits. This can be overcome by rethinking the current European industrial system into new collaborative and sustainable value networks. New business cases can be generated also for individual Small and Medium Enterprises (SME) by taking advantage of the connection with the Industry 4.0 opportunities and by building on cross-sectorial markets and the potential of digital solutions. The operational services (DOS) presented in this paper are the key part of a novel digital platform called DigiPrime, whose aim is to provide SME with a federated service infrastructure that help overcome the current information asymmetry and explore the possibility to access and transit towards a CE cross-sectorial value-chain. The manuscript presents a selection of DOS considered fundamental for an SME in such a process, through a relevant exploitation use-case scenario. The combination of such DOS has been validated within the project for the case of an electric car repair-shop network and the preliminary outcome of their integration process is also outlined. © 2020 Elsevier B.V.. All rights reserved.

关键词： Industry 4.0

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3D mapping of fire hotspot in East Rinjani forest area using GIS and remote sensing

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AIP Conference Proceedings 2024年第1期3026卷

作者： Ari Hernawan Wahyu Rahmaniar Jia Ching Wang Wirarama Wedashwara Andy Hidayat Jatmika Muhammad Ari Rifqi Feisal Dirgantara Department of Informatics Engineering Faculty of Engineering University of Mataram Mataram Indonesia Institute of Innovative Research Tokyo Institute of Technology Kanagawa 226-8503 Japan Departement of Computer Science and Information Engineering National Central University Taoyuan Taiwan Institiute of Earth Science Academica Sinica Taipei Taiwan Research Center for Geological Resources National Research and Innovation Agency (BRIN) Bogor Indonesia

Wildfires in Indonesia reached their peak during the drought of 2015, with estimated burnt forest and land of up to 2.6 million hectares. During the dry season of 2020, high temperatures and high winds were responsible for losing 659.02 hectares from forest fires in Lombok Island. The fires on the Island caused economic hardship, disrupted commerce, and led to short and long-term health problems for most of the population. This study aimed to use Geographic Information Systems (GIS) and Remote Sensing (RS) to map forest fire hotspots, establish fire brigades, and suppress forest recovery programs. Wildfire annual data were acquired from Satu Data NTB website, DEM from ASTER Global Elevation Model, and NDVI from Sentinel2B. DEM and NDVI were calculated and projected into 3D objects. The Wildfire geolocations were then added to 3D objects and the scale according to wildfire level. The performance of forest recovery is measured using the development of NDVI (Normalized Difference Vegetation Index) in affected fire areas. Results show the overall vegetation index increases in the area where fire forest has occurred. Combined RS and GIS efforts show the prospective application to evaluate forest fire control and recovery programs’ performance.

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Threshold-based Network Structural Dynamics? 2021

arXiv

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arXiv 2021年

作者： Spirakis, Paul G. Kipouridis, Evangelos Tsichlas, Kostas University of Copenhagen Universitetsparken 1 Copenhagen2100 Denmark Department of Computer Science University of Liverpool Ashton Str. LiverpoolL69 3BX United Kingdom Computer Engineering and Informatics Department University of Patras Patras Greece

The interest in dynamic processes on networks is steadily rising in recent years. In this paper, we consider the (α, β)-Thresholded Network Dynamics ((α, β)-Dynamics), where α ≤ β, in which only structural dynamics (dynamics of the network) are allowed, guided by local thresholding rules executed by each node. In particular, in each discrete round t, each pair of nodes u and v that are allowed to communicate by the scheduler, computes a value E(u, v) (the potential of the pair) as a function of the local structure of the network at round t around the two nodes. If E(u, v) Copyright © 2021, The Authors. All rights reserved.

关键词： Dynamics

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Sparse-softmax: A Simpler and Faster Alternative Softmax Transformation

arXiv

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arXiv 2021年

作者： Sun, Shaoshi Zhang, Zhenyuan Huang, BoCheng Lei, Pengbin Su, Jianlin Pan, Shengfeng Cao, Jiarun School of Computer Science and Informatics Cardiff University United Kingdom Department of Economics Osaka City University Japan School of Software Engineering Beijing Jiaotong University China College of Electronics and Information Engineering Shenzhen University China Shenzhen Zhuiyi Technology Co. Ltd. China Chinese Academy of Science China

The softmax function is widely used in artificial neural networks for the multiclass classification problems, where the softmax transformation enforces the output to be positive and sum to one, and the corresponding loss function allows to use maximum likelihood principle to optimize the model. However, softmax leaves a large margin for loss function to conduct optimizing operation when it comes to high-dimensional classification, which results in low-performance to some extent. In this paper, we provide an empirical study on a simple and concise softmax variant, namely sparse-softmax, to alleviate the problem that occurred in traditional softmax in terms of high-dimensional classification problems. We evaluate our approach in several interdisciplinary tasks, the experimental results show that sparse-softmax is simpler, faster, and produces better results than the baseline models. © 2021, CC BY-NC-ND.

关键词： Neural networks

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A Brief Review of Explainable Artificial Intelligence in Healthcare

SSRN

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SSRN 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalo, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Swinburne University of Technology HawthornVIC3122 Australia Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, The Authors. All rights reserved.

关键词： Decision making

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A comprehensive approach utilizing quantum machine learning in the study of corrosion inhibition on quinoxaline compounds

Artificial Intelligence Chemistry

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Artificial Intelligence Chemistry 2024年第2期2卷

作者： Muhamad Akrom Supriadi Rustad Hermawan Kresno Dipojono Ryo Maezono Study Program in Informatics Engineering Faculty of Computer Science Universitas Dian Nuswantoro Semarang 50131 Indonesia Research Center for Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang 50131 Indonesia Quantum and Nano Technologies Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung 40132 Indonesia School of Information Science Japan Advanced Institute of Science and Technology Ishikawa 923-1292 Japan

In this investigation, a quantitative structure-property relationship (QSPR) model coupled with a quantum neural network (QNN) was used to explore the corrosion inhibition efficiency (CIE) of quinoxaline compounds. Integrating quantum chemical properties (QCP) features reduced computational burden by strategically reducing the features from 11 to 4 while maintaining prediction accuracy. QNN models outperform traditional methods like artificial neural networks (ANN) and multilayer perceptron neural networks (MLPNN), with a coefficient of determination (R 2 ) value of 0.987, coupled with diminished root mean square error (RMSE), mean absolute error (MAE), and mean absolute deviation (MAD) values of 0.97, 0.92, and 1.10, respectively. Predictions for six newly synthesized quinoxaline derivatives: quinoxaline-6-carboxylic acid (Q1) , methyl quinoxaline-6-carboxylate (Q2) , (2 E ,3 E )-2,3-dihydrazono-1,2,3,4-tetrahydroquinoxaline (Q3) , (2 E ,3 E ) 2,3-dihydrazono-6-methyl-1,2,3,4-tetrahydroquinoxaline (Q4) , ( E )-3-(4-methoxyethyl)-7-methylquinoxalin-2(1 H)-one (Q5) , and 2-(4-methoxyphenyl)-7-methylthieno[3,2- b ] quinoxaline (Q6) , show remarkable CIE values of 95.12, 96.72, 91.02, 92.43, 89.58, and 93.63 %, respectively. This breakthrough technique simplifies testing and production procedures for new anti-corrosion materials.

关键词： Quantum neural network QSPR Corrosion inhibition Quinoxaline

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