Trust in Artificial Intelligence–Based Clinical Decision Support Systems Among Health Care Workers: Systematic Review by Hein Minn Tun, Hanif Abdul Rahman, Lin Naing, Owais Ahmed Malik

“… Abstract BackgroundArtificial intelligence–based clinical decision support systems (AI-CDSSs) have enhanced personalized medicine and improved the efficiency of health care workers. Despite these opportunities, trust in these tools remains a critical factor for their successful integration into practice. …”
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Machine learning applications in placenta accreta spectrum disorders by Mahsa Danaei, Maryam Yeganegi, Sepideh Azizi, Fatemeh Jayervand, Seyedeh Elham Shams, Mohammad Hossein Sharifi, Reza Bahrami, Ali Masoudi, Amirhossein Shahbazi, Amirmasoud Shiri, Heewa Rashnavadi, Kazem Aghili, Hossein Neamatzadeh

“…It highlights recent advancements in ML algorithms and radiomic techniques that utilize medical imaging modalities like magnetic resonance imaging (MRI) and ultrasound for effective classification and risk stratification of PAS. …”
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Power grid fault handling plan matching method based on a hybrid neural network by Dajun Xiao, Xialing Xu, Bo Zhang, Yue Zhang, Lianfei Shan, Tao Liu, Xin Li, Yongtian Qiao, Tao Jiang, Yu Wang

“…A matching method based on a hybrid neural network is proposed to improve the accuracy of online matching for a power grid fault handling plan. …”
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Using machine learning to develop a stacking ensemble learning model for the CT radiomics classification of brain metastases by Huai-wen Zhang, Yi-ren Wang, Bo Hu, Bo Song, Zhong-jian Wen, Lei Su, Xiao-man Chen, Xi Wang, Ping Zhou, Xiao-ming Zhong, Hao-wen Pang, You-hua Wang

“…Abstract The objective of this study was to explore the potential of machine-learning techniques in the automatic identification and classification of brain metastases from a radiomic perspective, aiming to improve the accuracy of tumor volume assessment for radiotherapy. …”
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Assessing reading fluency in elementary grades: A machine learning approach by Gabriel Candido da Silva, Rodrigo Lins Rodrigues, Américo N. Amorim, Lieny Jeon, Emilia X.S. Albuquerque, Vanessa C. Silva, Vinícius F. da Silva, André L.A. Pinheiro, João P.J.R. Nunes, Suzana X.M.G. de Souza, Maxsuel S. Silva, Igor Mauro, Alexandre Magno Andrade Maciel

“…This study compares eleven widely used machine learning algorithms to identify the most accurate and comprehensive method for assessing children's reading fluency. …”
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Human multiethnic radiogenomics reveals low-abundancy microRNA signature in plasma-derived extracellular vesicles for early diagnosis and molecular subtyping of pancreatic cancer by Jianying Xu, Wenjie Shi, Yi Zhu, Chao Zhang, Julia Nagelschmitz, Maximilian Doelling, Sara Al-Madhi, Ujjwal Mukund Mahajan, Maciej Pech, Georg Rose, Roland Siegfried Croner, Guoliang Zheng, Christoph Kahlert, Ulf Dietrich Kahlert

“…Pancreatic cancer (PC) is a highly aggressive malignancy in humans, where early diagnosis significantly improves patient outcomes. However, effective methods for accurate and early detection remain limited. …”
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Genome-wide identification and expression profile analysis of the NAC transcription factor family during abiotic and biotic stress in woodland strawberry. by He Zhang, Hao Kang, Chulian Su, Yanxiang Qi, Xiaomei Liu, Jinji Pu

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HETDEX-LOFAR Spectroscopic Redshift Catalog by Maya H. Debski, Gregory R. Zeimann, Gary J. Hill, Donald P. Schneider, Leah Morabito, Gavin Dalton, Matt J. Jarvis, Erin Mentuch Cooper, Robin Ciardullo, Eric Gawiser, Nika Jurlin

“…This new catalog contains the highest substantial fraction of LOFAR galaxies with spectroscopic redshift information; it improves archival spectroscopic redshifts and facilitates research to determine the [O ii ] emission properties of radio galaxies from 0.0 < z < 0.5, and the Ly α emission characteristics of both radio galaxies and quasars from 1.9 < z < 3.5. …”
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The role of endothelial cell-related gene COL1A1 in prostate cancer diagnosis and immunotherapy: insights from machine learning and single-cell analysis by Gujun Cong, Jingjing Shao, Feng Xiao, Haixia Zhu, Peipei Kang

“…Targeting endothelial cells and related signaling pathways can improve the effectiveness of immunotherapy by normalizing tumor blood vessels and promoting immune cell infiltration. …”
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Dispatch Decisions and Emergency Medical Services Response in the Prehospital Care of Status Epilepticus by Robert P. McInnis, Andrew J. Wood, Courtney L. Shay, Anna A. Haggart, Remle P. Crowe, Elan L. Guterman

“…Despite this, dispatch-assigned acuity did not have a strong relationship with the ambulance response or training level of the EMS responder or response time, suggesting that use of dispatch algorithms might be further optimized and highlighting a potential area to improve quality of EMS care.…”
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Promising methods of prenatal diagnostics based on passive sensors and machine learning by A. A. Ivshin, V. M. Vorobyova, N. A. Malyshev

“…All the technologies under consideration are still experimental, and further work is required to improve the algorithms and integrate various types of sensors to ensure comprehensive analysis.Conclusion. …”
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Machine learning to predict virological failure among HIV patients on antiretroviral therapy in the University of Gondar Comprehensive and Specialized Hospital, in Amhara Region, E... by Daniel Niguse Mamo, Tesfahun Melese Yilma, Makda Fekadie Tewelgne, Yakub Sebastian, Tilahun Bizuayehu, Mequannent Sharew Melaku, Agmasie Damtew Walle

“…Thus, Machine learning predictive algorithms have the potential to improve the quality of care and predict the needs of HIV patients by analyzing huge amounts of data, and enhancing prediction capabilities. …”
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Leveraging AI for early cholera detection and response: transforming public health surveillance in Nigeria by Adamu Muhammad Ibrahim, Mohamed Mustaf Ahmed, Shuaibu Saidu Musa, Usman Abubakar Haruna, Mohammed Raihanatu Hamid, Olalekan John Okesanya, Aishat Muhammad Saleh, Don Eliso Lucero-Prisno III

“…By integrating AI into Nigeria’s public health infrastructure, early detection and response can be improved, resource allocation optimized, and disease transmission minimized. …”
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A secure and efficient user selection scheme in vehicular crowdsensing by Min Zhang, Qing Ye, Zhimin Yuan, Kaihuan Deng

“…This emerging sensing paradigm is beneficial for reducing financial and time costs related to data collection and improving data quality. Inappropriate user selection may lead to redundant sensing data, consequently degrading the quality of sensing tasks. …”
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Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning by Tiantian He, Jie Geng, Chuandong Hou, Hongyi Li, Hong Zhang, Peng Zhao, Peifeng He, Xuechun Lu

“…These findings lay the groundwork for further exploration of DLBCL progression and potential prognostic improvements.…”
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A Novel SOH Estimation Method for Lithium-Ion Batteries Based on the PSO–GWO–LSSVM Prediction Model with Multi-Dimensional Health Features Extraction by Xu He, Zhengpu Wu, Jinghan Bai, Junchao Zhu, Lu Lv, Lujun Wang

“…With strong generalization and robustness, least squares support vector machine (LSSVM) is widely applied to nonlinear computations and function approximation. To improve LSSVM model accuracy and efficiency, this paper develops a novel prediction model that uses particle swarm optimization (PSO) combined with grey wolf optimization (GWO) algorithms to optimize the LSSVM model. …”
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May the Extensive Farming System of Small Ruminants Be Smart? by Rosanna Paolino, Adriana Di Trana, Adele Coppola, Emilio Sabia, Amelia Maria Riviezzi, Luca Vignozzi, Salvatore Claps, Pasquale Caparra, Corrado Pacelli, Ada Braghieri

“…Precision Livestock Farming (PLF) applies a complex of sensor technology, algorithms, and multiple tools for individual, real-time livestock monitoring. …”
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Multi-Agent Meta Reinforcement Learning for Reliable and Low-Latency Distributed Inference in Resource-Constrained UAV Swarms by Marwan Dhuheir, Aiman Erbad, Bechir Hamdaoui, Samir Brahim Belhaouari, Mohsen Guizani, Thang X. Vu

“…Our approach is tested on CNN networks and benchmarked against state-of-the-art conventional reinforcement learning algorithms. Extensive simulations show that our model outperforms competitive methods by around 29% in terms of latency and around 23% in terms of transmission power improvements while delivering results comparable to the traditional LDTP optimization solution by around 9% in terms of latency.…”
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Parametric Optimization and Assessment of Modern Heritage Shading Screen for a Mid-Rise Building in Arid Climate: Modernizing Traditional Designs by Anwar Ahmad, Lindita Bande, Waleed Ahmed, Kheira Tabet Aoul, Mukesh Jha

“…This research explores how parametric design and optimization based on genetic algorithms (GAs) can improve shading structures to reduce solar radiation and lower cooling energy consumption. …”
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A Reinforcement Learning Approach to Personalized Asthma Exacerbation Prediction Using Proximal Policy Optimization by Dahiru Adamu Aliyu, Emelia Akashah Patah Akhir, Maryam Omar Abdullah Sawad, Jameel Shehu Yalli, Yahaya Saidu

“…Asthma exacerbation prediction is critical for preventing severe respiratory complications and improving patient outcomes. Traditional predictive models rely on static machine learning approaches, which lack adaptability to evolving patient conditions and environmental changes. …”
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