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Randomised controlled trials evaluating artificial intelligence in clinical practice: a scoping review

The Lancet Digital Health 2024年第5期6卷 e367-e373页

作者： Han, Ryan Acosta, Julián N Shakeri, Zahra Ioannidis, John P A Topol, Eric J Rajpurkar, Pranav Department of Biomedical Informatics Harvard Medical School BostonMA United States Department of Computer Science Stanford University StanfordCA United States University of California Los Angeles–Caltech Medical Scientist Training Program Los AngelesCA United States Department of Neurology Yale School of Medicine New HavenCT United States Rad AI San FranciscoCA United States Institute of Health Policy Management and Evaluation Dalla Lana School of Public Health University of Toronto TorontoON Canada Stanford Prevention Research Center Department of Medicine Stanford University StanfordCA United States Meta-Research Innovation Center at Stanford Stanford University StanfordCA United States Scripps Research Translational Institute Scripps Research La JollaCA United States

This scoping review of randomised controlled trials on artificial intelligence (AI) in clinical practice reveals an expanding interest in AI across clinical specialties and locations. The USA and China are leading in the number of trials, with a focus on deep learning systems for medical imaging, particularly in gastroenterology and radiology. A majority of trials (70 [81%] of 86) report positive primary endpoints, primarily related to diagnostic yield or performance;however, the predominance of single-centre trials, little demographic reporting, and varying reports of operational efficiency raise concerns about the generalisability and practicality of these results. Despite the promising outcomes, considering the likelihood of publication bias and the need for more comprehensive research including multicentre trials, diverse outcome measures, and improved reporting standards is crucial. Future AI trials should prioritise patient-relevant outcomes to fully understand AI's true effects and limitations in health care. © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license.

关键词： Diagnosis

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Transfer learning approach to modeling multichannel gate-all-around nanosheet field-effect transistors under work function fluctuations

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Engineering Applications of Artificial Intelligence 2025年 148卷

作者： Dash, Sagarika Li, Yiming Parallel and Scientific Computing Laboratory National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Communications Engineering National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Biomedical Engineering National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Artificial Intelligence Innovation National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Department of Electronics and Electrical Engineering National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Department of Microelectronics National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan

Deep learning (DL) has significantly advanced various industries, including semiconductors, by providing sophisticated methods for analyzing emerging device data. Transfer learning (TL), a prominent DL topology, leverages knowledge from a pre-trained model to improve the performance of a specific target task. This study aims to apply TL to predict the impact of work function fluctuation (WKF) on the variability of threshold voltage (σVTH) in gate-all-around silicon nanosheet field-effect transistors (GAA Si NS FETs). We used TL to transfer knowledge from models trained on 1-channel (1ch) GAA Si NS FETs to more complex 4-channel (4ch) counterparts. The prediction accuracy is evaluated by using the mean square error(MSE), root mean square error (RMSE), and the mean absolute error(MAE) metrics. Our TL approach achieved an error rate of less than 1%, demonstrating high accuracy in predicting WKF effects on σ VTH of 4ch GAA Si NS FETs using only 500 samples. This study presents a pioneering application of TL using a one-dimensional (1D) convolutional neural network (CNN) long short-term memory (LSTM) topology in semiconductor device technology. The results effectively address the challenge of predicting σ VTH variations in advanced GAA Si NS FETs because of WKF, which eliminates the need for extensive semiconductor device simulations. © 2025 Elsevier Ltd

关键词： Convolutional neural networks

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An artificial intelligence method for vessel detection in images of cardiac catheterization

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Informatics in Medicine Unlocked 2023年 39卷

作者： Chien, Ting-Ying Ting, Hsien-Wei Li, Hao-Wei Chang, Hsiao-Huang Department of Computer Science and Engineering Yuan Ze University 320 Taoyuan City Taiwan Graduate Program in Biomedical Informatics Yuan Ze University 320 Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University 320 Taoyuan City Taiwan Department of Neurosurgery Taipei Hospital Ministry of Health and Welfare 242 New Taipei City Taiwan Division of Cardiovascular Surgery Department of Surgery Taipei Veterans General Hospital Taipei 11217 Taiwan Department of Surgery School of Medicine College of Medicine Taipei Medical University Taipei 11031 Taiwan

Coronary artery disease (CAD) is one of the top ten leading causes of death in countries worldwide. There are several methods used by physicians to diagnose CAD. One common method is the use of X-ray angiogram (XRA) images. When employing a computer-aided automatic analysis method to locate blood vessels in XRA images, problems such as complex background noise, low contrast, and difficulty in separating blood vessels from the background are encountered, especially for blood vessels with a low contrast to the background noise. In this study, we used various image preprocessing methods to highlight blood vessels from complex backgrounds and different types of blood vessel, followed by the employment of four superpixel segmentation methods to partition an image into multiple segments, and finally the utilization of Weighted Voting to determine the best superpixel algorithm. A genetic algorithm was used to identify the best weights by which to determine the locations of blood vessels in images of cardiac catheterization. Our proposed method achieved an average F1-score of 73.4% in the analysis of five patients, which was better than the other superpixel algorithms tested. The results of this research will be helpful for future SYNTAX score analysis. © 2023

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Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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Molecular characterization of transesterification activity of novel lipase family I.1

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Bioscience Reports 2022年第10期42卷 BSR20220654页

作者： Haryati, Titin Widhiastuty, Made Puspasari Warganegara, Fida Madayanti Akhmaloka, Akhmaloka Doctoral Program of Chemistry Faculty of Mathematics and Natural Science Institut Teknologi Bandung Jl. Ganesha 10 Jawa Barat Bandung 40132 Indonesia National Research and Innovation Agency Gedung B.J. Habibie Jalan M.H. Thamrin Nomor 8 Jakarta Pusat 10340 Indonesia Biochemistry Research Group Faculty of Mathematics and Natural Science Institut Teknologi Bandung Jl. Ganesha 10 Jawa Barat Bandung 40132 Indonesia Department of Chemistry Faculty of Science and Computer Universitas Pertamina Jl. Teuku Nyak Arief Jakarta Jakarta Selatan 12220 Indonesia

Lipase's thermostability and organic solvent tolerance are two crucial properties that enable it to function as a biocatalyst. The present study examined the characteristics of two recombinant thermostable lipases (Lk2, Lk3) based on transesterification activity. Conversion of C12-C18 methyl ester with paranitrophenol was investigated in various organic solvent. Both lipases exhibited activity on difference carbon chain length (C12 - C18, C18:1, C18:2) of substrates. The activity of Lk2 was higher in each of substrate compared with that of Lk3. Experimental findings showed that the best substrates for Lk2 and Lk3 are C18:1 and C18:2 respectively, in agreement with the computational analysis. The activity of both enzymes prefers on nonpolar solvent. On nonpolar solvent the enzymes are able to keep its native folding shown by the value of radius gyration, solvent-enzyme interaction and orientation of triad catalytic residues. Lk3 appeared to be more thermostable, with maximum activity at 55°C. The presence of Fe3+ increased the activity of Lk2 and Lk3. However, the activity of both enzymes were dramatically decreased by the present of Ca2+ despite of the enzymes belong to family I.1 lipase known as calcium dependent enzyme. Molecular analysis on His loop of Lk2 and Lk3 on the present of Ca2+ showed that there were shifting on the orientation of catalytic triad residues. All the data suggest that Lk2 and Lk3 are novel lipase on the family I.1 and both lipase available as a biocatalyst candidate. © 2022 The Author(s).

关键词： Lipase recombinant molecular docking molecular interactions Solvent tolerance Thermostability Transesterification

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Large-Scale Distributed Multidisciplinary Design Optimization of the NASA Lift-Plus-Cruise Air Taxi Concept

Large-Scale Distributed Multidisciplinary Design Optimizatio...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： van Schie, Sebastiaan P. C. Ruh, Marius L. Fletcher, Andrew Warner, Michael Sperry, Mark Scotzniovsky, Luca Orndorff, Nicholas C. Xiang, Ru Yan, Jiayao Zhao, Han Krokowski, Joshua J. Chen, J.S. Sarojini, Darshan Gill, Hyunjune Lee, Seongkyu Tagg, Andrew Anderson, Ryan Green, Eric Joseph, Cibin Ning, Andrew Cheng, Zeyu Cao, Zhi Mi, Chunting Guibert, Alexandre T. R. Cronk, Ashley Kim, H. Alicia Meng, Y. Shirley Silva, Christopher Hwang, John T. University of California San Diego 9500 Gilman Dr La Jolla CA92093 United States Department of Mechanical and Aerospace Engineering University of California San Diego 9500 Gilman Dr La Jolla CA92093 United States Department of Structural Engineering University of California San Diego 9500 Gilman Dr La Jolla CA92093 United States Aerospace and Ocean Engineering Department Virginia Tech 1600 Innovation Dr BlacksburgVA24060 United States Department of Mechanical and Aerospace Engineering University of California Davis 1 Shields Avenue DavisCA95616 United States Mechanical Engineering Brigham Young University ProvoUT84602 United States Department of Electrical & Computer Engineering San Diego State University 5500 Campanile Dr San Diego92182 United States Structural Engineering Department University of California San Diego 9500 Gilman Dr La Jolla CA92093 United States Materials Science & Engineering Program University of California San Diego 9500 Gilman Dr La Jolla CA92093 United States Pritzker School of Molecular Engineering The University of Chicago ChicagoIL60607 United States NASA Ames Research Center Moffett FieldCA94035 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Large-scale gradient-based Multidisciplinary Design Optimization (MDO) can aid in the exploration of high-dimensional design spaces for novel air vehicle concepts, thereby leading to more efficient and economic designs. This paper builds on past works where we demonstrated large-scale physics-based MDO capabilities and applied these to NASA’s lift-plus-cruise electric air taxi concept. We extend this comprehensive mid-fidelity system-level optimization problem with high(er)-fidelity subsystem-level optimizations of various aircraft systems and mission phases, with the aim of further enhancing the accuracy and scope of the aforementioned system-level optimization problem: 1) Power minimization during the transition mission phase;2) Electrical powertrain topology optimization;3) Cell chemistry modeling and thermomechanical battery pack topology optimization;4) Shell-based coupled aero-elastic wing structure optimization. An Analytical Target Cascading-like distributed MDO architecture allows us to couple the system- and subsystem-level optimizations in order to arrive at a consistent and feasible design. We find that the system-level design is most heavily impacted by the reduced battery pack-level energy densities that stem from power peaks in the mission profile. These power peaks seem to result from the inefficient lift rotor blade designs that are needed to satisfy noise constraints. We draw conclusions based on trends in our results and give recommendations for future MDO studies of electrical air taxi vehicle concepts. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Shape optimization

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Ensuring unbiasedness: foundational insights into integrating GSTARIMA and DNN models for rainfall prediction

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Connection science 2025年第1期37卷

作者： Devi Munandar Budi Nurani Ruchjana Atje Setiawan Abdullah Hilman Ferdinandus Pardede a Doctoral Program of Mathematics Faculty of Mathematics and Natural Sciences Universitas Padjadjaran Sumedang Indonesia b Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Padjadjaran Sumedang Indonesia c Department of Computer Science Faculty of Mathematics and Natural Sciences Universitas Padjadjaran Sumedang Indonesia d Research Center for Artificial Intelligence and Cybersecurity National Research and Innovation Agency (BRIN) Jakarta Pusat Indonesia

The GSTARIMA (Generalied Space–Time Autoregressive Integrated Moving Average) model is commonly used to analyse time series and spatial data with temporal and spatial dependencies. This paper focuses on estimating the autoregressive and moving average parameters of the GSTARIMA model using Maximum Likelihood Estimation (MLE). We theoretically demonstrate the unbiasedness of these estimates, proving that the expected values of the estimates match the true parameters. Empirical experiments further verify this property, both before and after applying Deep Neural Network (DNN) interventions to correct model errors. The results show that the parameter estimates remain unbiased, and error properties (zero mean and constant variance) are preserved even after DNN processing. This study highlights the robustness of MLE in providing unbiased estimates within the GSTARIMA framework, even when integrated with machine learning techniques.

关键词： GSTARIMA maximum likelihood estimation deep neural network parameter estimation ensuring unbiasedness

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Effect of Environment Temperature and Relative Humidity on Thermal Emissivity: Study Case of Mango Fruit

Effect of Environment Temperature and Relative Humidity on T...

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International Symposium on Intelligent Systems and Informatics (SISY)

作者： Panmanas Sirisomboon Jiraporn Sripinyowanich Jongyingcharoen Apiwat Junto Thitima Phanomsophon Cheewanun Dachoupakan Sirisomboon Panan Rerngsamran Dharma Raj Pokhrel Sneha Sharma Jetsada Posom Pimpen Pornchaloempong Anupun Terdwongwarakul Prabhas Chongstitvatana Department of Agricultural Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Agricultural Engineering School of Engineering and Innovation Rajamangala University of Technology Tawan-Ok Chon Buri Thailand Office of Administrative Interdisciplinary Program on Agricultural Technology School of Agricultural Technology King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Microbiology Faculty of Science Chulalongkorn University Bangkok Thailand Department of Infrastructure Engineering University of Melbourne Victoria Australia Department of Agricultural Engineering Faculty of Engineering Khon Kaen University Khon Kaen Thailand Department of Food Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Agricultural Engineering Faculty of Engineering at Kanpangsan Kasetsart University Thailand Department of Computer Engineering Chulalongkorn University Bangkok Thailand

ISBN: (数字)9798350385601

ISBN: (纸本)9798350385618

This research was to study the effect of the environment condition during image captured including temperature and relative humidity in the packaging house of the mango exporting factory and in the orchard on the emissivity of mango fruit. The result showed that in the controlled environment of the packaging house in factory, the emissivity was increased (the mango emitted more energy) when the surface temperature was lower and the emissivity of mango is 0.71-0.84 and in the uncontrolled environment of open-air packaging yard in the mango orchard, the different transpiration rates of mango effected mainly by ambient temperature and relative humidity make the morning condition emissivity of 0.44-0.66 and the afternoon condition of 0.72-0.98. There was the effect of the different measured positions on the fruits where the physiology was different and the fluctuated environment in the latter condition made the wide range of emissivity of mangoes. This indicated the shortcoming of thermal imaging of horticultural product if the correct emissivity varied and difficult to set in the thermal camera setting, hence the inaccurate thermogram to be obtained.

关键词： Temperature measurement Transpiration Temperature Humidity Packaging Position measurement Cameras Production facilities Physiology Temperature control

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Industrial Internet of Things: Implementations, challenges, and potential solutions across various industries

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computers in Industry 2025年 170卷

作者： Shaila Afrin Sabiha Jannat Rafa Maliha Kabir Tasfia Farah Md. Sakib Bin Alam Aiman Lameesa Shams Forruque Ahmed Amir H. Gandomi Science and Math Program Asian University for Women Chattogram 4000 Bangladesh Cell & Developmental Biology Program Medical College of Wisconsin Milwaukee WI 53226 USA Biomedical Sciences Program University of Kansas Medical Center Kansas City KS 66103 USA Department of Information Technology University of Information Technology and Sciences Dhaka 1212 Bangladesh Department of Computer Science American International University - Bangladesh (AIUB) Dhaka 1229 Bangladesh School of Mathematical Sciences Sunway University Bandar Sunway Petaling Jaya Selangor Darul Ehsan 47500 Malaysia Department of Mathematics & Physics North South University Dhaka 1229 Bangladesh Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW 2007 Australia University Research and Innovation Center (EKIK) Óbuda University Budapest 1034 Hungary Department of Computer Science Khazar University Mahsati 41 Baku Azerbaijan

The Industrial Internet of Things (IIoT) has emerged as a potent catalyst for transformation across many industries as a part of Industry 4.0. This review thoroughly examines IIoT applications, demonstrating how it enhances operational efficiency, informed decision-making, cost optimization, innovation, and workplace safety. While prior research has often concentrated on technical dimensions such as fog and edge computing, network protocols, or big data integration, several emerging and high-impact application areas remain underexplored. This study addresses that gap by systematically reviewing IIoT implementations in critical yet often overlooked domains, including environmental monitoring, agriculture, construction, healthcare, robotics, smart grids, and predictive maintenance. It offers fresh insights into how IIoT is being adapted to meet real-world challenges in these sectors. In addition to outlining the current landscape, the review identifies core barriers such as data security, interoperability, and system scalability. It underscores the importance of cross-sector collaboration and strategic alignment to fully leverage the transformative potential of IIoT. The paper concludes by outlining key research gaps and future opportunities to guide continued innovation and scholarly investigation.

关键词： Intelligent manufacturing systems Artificial intelligence Wireless Internet of things Industrial IoT IoT Industry 4.0

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An approach for crop recommendation with uncertainty quantification based on machine learning for sustainable agricultural decision-making

Results in Engineering

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Results in Engineering 2025年 26卷

作者： Md. Sakib Bin Alam Vatcharaporn Esichaikul Aiman Lameesa Shams Forruque Ahmed Amir H. Gandomi Department of Information Technology University of Information Technology and Sciences (UITS) Dhaka 1212 Bangladesh Computer Science and Information Management Program School of Engineering and Technology Asian Institute of Technology Pathum Thani 12120 Thailand Department of Computer Science American International University-Bangladesh Dhaka 1229 Bangladesh School of Mathematical Sciences Sunway University Bandar Sunway Petaling Jaya 47500 Selangor Darul Ehsan Malaysia Department of Mathematics & Physics North South University Dhaka 1229 Bangladesh Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW 2007 Australia University Research and Innovation Center (EKIK) Óbuda University 1034 Budapest Hungary Department of Computer Science Khazar University Mahsati 41 Baku Azerbaijan

While machine learning (ML) models for crop recommendation have demonstrated high predictive accuracy, a critical gap persists in their practical reliability: the omission of uncertainty quantification. Existing studies predominantly deliver deterministic recommendations, neglecting inherent uncertainties arising from data noise. This raises concerns about the reliability of the decision support systems for crop recommendation. To address this, we propose an ensemble ML framework incorporating entropy-based uncertainty quantification. Trained on a publicly available Indian agricultural dataset with 2200 samples across seven agro-climatic features (nitrogen, phosphorus, potassium, temperature, humidity, pH, and rainfall) and 22 crop classes, the model achieves a predictive accuracy of 99.54 %. By estimating prediction confidence using entropy, the framework offers probabilistic recommendations that support environmentally informed decision-making under uncertainty. These findings suggest that integrating uncertainty measures into ML-driven crop recommendation systems can enhance reliability and promote sustainable agricultural practices.

关键词： Crop recommendation Machine learning Uncertainty Precision agriculture

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