Artificial intelligence in vaccine research and development: an umbrella review by Rabie Adel El Arab, May Alkhunaizi, May Alkhunaizi, Yousef N. Alhashem, Alissar Al Khatib, Munirah Bubsheet, Salwa Hassanein, Salwa Hassanein

“…BackgroundThe rapid development of COVID-19 vaccines highlighted the transformative potential of artificial intelligence (AI) in modern vaccinology, accelerating timelines from years to months. …”
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Improving the accuracy and interpretability of neural networks for wind power forecasting by Wenlong Liao, Birgitte Bak-Jensen, Zhe Yang, Gonghao Zhang, Jiannong Fang, Fernando Porté-Agel

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Forecasting of the Geomagnetic Activity for the Next 3 Days Utilizing Neural Networks Based on Parameters Related to Large‐Scale Structures of the Solar Corona by Tingyu Wang, Bingxian Luo, Jingjing Wang, Xianzhi Ao, Liqin Shi, Qiuzhen Zhong, Siqing Liu

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Explainable Machine Learning for Radio Environment Mapping: An Intelligent System for Electric Field Strength Monitoring by Yiannis Kiouvrekis, Theodor Panagiotakopoulos, Efthymia Nousi, Ioannis Filippopoulos, Agapi Ploussi, Ellas Spyratou, Efstathios P. Efstathopoulos

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Wireless Channel Prediction Using Artificial Intelligence With Imperfect Datasets by Gowhar Javanmardi, Ramiro Samano Robles

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A Hybrid Transfer Learning Framework for Brain Tumor Diagnosis by Sadia Islam Tonni, Md. Alif Sheakh, Mst. Sazia Tahosin, Md. Zahid Hasan, Taslima Ferdaus Shuva, Touhid Bhuiyan, Muhammad Ali Abdullah Almoyad, Nabil Anan Orka, Md. Tanvir Rahman, Risala Tasin Khan, M. Shamim Kaiser, Mohammad Ali Moni

“…The proposed system integrates advanced preprocessing techniques, an ensemble of pretrained deep learning models, and explainable artificial intelligence (XAI) methods to achieve high accuracy and reliability. …”
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An explainable federated blockchain framework with privacy-preserving AI optimization for securing healthcare data by Tanisha Bhardwaj, K. Sumangali

“…To address these limitations, this paper proposes the Privacy Preserving Federated Blockchain Explainable Artificial Intelligence Optimization (PPFBXAIO) framework, which integrates blockchain technology, Explainable AI (XAI), and optimization techniques to ensure privacy, traceability, and robustness in FL-based systems. …”
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Machine Learning-Based Interpretable Screening for Osteoporosis in Tuberculosis Spondylitis Patients Using Blood Test Data: Development and External Validation of a Novel Web-Based... by Yasin P, Ding L, Mamat M, Guo W, Song X

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GeneXAI: Influential gene identification for breast cancer stages using XAI-based multi-modal framework by Sweta Manna, Sujoy Mistry, Debashis De

“…The study introduces a GeneXAI multi-modal approach, which classifies the cancer stages and identifies the influential genes by the explainable artificial intelligence models. In the first phase of the GeneXAI, a hybrid optimal feature selection method is applied to extract the imperative features using an early fusion technique. …”
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Multi-task reinforcement learning and explainable AI-Driven platform for personalized planning and clinical decision support in orthodontic-orthognathic treatment by Zhiyuan Li, Liwei Wang

“…Abstract This study presents a novel clinical decision support platform for orthodontic-orthognathic treatment that integrates multi-task reinforcement learning with explainable artificial intelligence. The platform addresses the challenges of personalized treatment planning in complex dentofacial deformities by formulating treatment as a sequential decision-making process optimizing multiple clinical objectives simultaneously. …”
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Interpretable artificial intelligence for modulated metasurface antenna design using SHAP and MLP by Amrollah Amini, Ali Moshiri, Mohammad Amin Chaychi Zadeh, Vahid Nayyeri

“…In this work, we propose an interpretable artificial intelligence framework that integrates SHapley Additive exPlanations (SHAP) with a multi-layer perceptron (MLP) to predict two key radiation metrics: sidelobe level (SLL) and half-power beamwidth (HPBW). …”
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Thresholds of Body Composition Changes Associated with Survival During Androgen Deprivation Therapy in Prostate Cancer by Pin-Chi Chen, Pai-Kai Chiang, Jhen-Bin Lin, Wei-Kung Tsai, Wan-Chun Lin, Ya-Ting Jan, Kun-Pin Wu, Jie Lee

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Artificial intelligence in neurodegenerative diseases research: a bibliometric analysis since 2000 by Yabin Zhang, Lei Yu, Yuting Lv, Tiantian Yang, Qi Guo

“…This bibliometric review examines the evolving landscape of artificial intelligence (AI) in neurodegenerative diseases research from 2000 to March 16, 2025, utilizing data from 1,402 publications (1,159 articles, 243 reviews) indexed in the Web of Science Core Collection. …”
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Artificial Intelligence in Primary Malignant Bone Tumor Imaging: A Narrative Review by Platon S. Papageorgiou, Rafail Christodoulou, Panagiotis Korfiatis, Dimitra P. Papagelopoulos, Olympia Papakonstantinou, Nancy Pham, Amanda Woodward, Panayiotis J. Papagelopoulos

“…Artificial Intelligence (AI) has emerged as a transformative force in orthopedic oncology, offering significant advances in the diagnosis, classification, and prediction of treatment response for primary malignant bone tumors (PBT). …”
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Artificial Intelligence-Based Prediction of Bloodstream Infections Using Standard Hematological and Biochemical Markers by Ferhat DEMİRCİ, Murat AKŞİT, Aylin DEMİRCİ

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Interpretable Machine Learning for Legume Yield Prediction Using Satellite Remote Sensing Data by Theodoros Petropoulos, Lefteris Benos, Remigio Berruto, Gabriele Miserendino, Vasso Marinoudi, Patrizia Busato, Chrysostomos Zisis, Dionysis Bochtis

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An improved XAI-based DenseNet model for breast cancer detection using reconstruction and fine-tuning by Md. Alamin Talukder

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Predictive modeling and interpretative analysis of risks of instability in patients with Myasthenia Gravis requiring intensive care unit admission by Chao-Yang Kuo, Emily Chia-Yu Su, Hsu-Ling Yeh, Jiann-Horng Yeh, Hou-Chang Chiu, Chen-Chih Chung

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An EfficientNet integrated ResNet deep network and explainable AI for breast lesion classification from ultrasound images by Kiran Jabeen, Muhammad Attique Khan, Ameer Hamza, Hussain Mobarak Albarakati, Shrooq Alsenan, Usman Tariq, Isaac Ofori

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Application of a methodological framework for the development and multicenter validation of reliable artificial intelligence in embryo evaluation by D. Gilboa, Akhil Garg, M. Shapiro, M. Meseguer, Y. Amar, N. Lustgarten, N. Desai, T. Shavit, V. Silva, A. Papatheodorou, A. Chatziparasidou, S. Angras, J. H. Lee, L. Thiel, C. L. Curchoe, Y. Tauber, D. S. Seidman

“…The four-step methodology for developing and evaluating the AI model include: (I) curating annotated datasets that represent the intended clinical use case; (II) developing and optimizing the AI model; (III) evaluating the AI’s performance by assessing its discriminative power and associations with pregnancy probability across variable data; and (IV) ensuring interpretability and explainability by correlating AI scores with relevant morphologic features of embryo quality. …”
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