The Emerging Role of Artificial Intelligence in Dermatology: A Systematic Review of Its Clinical Applications by Ernesto Martínez-Vargas, Jeaustin Mora-Jiménez, Sebastian Arguedas-Chacón, Josephine Hernández-López, Esteban Zavaleta-Monestel

“…The risk of bias was assessed qualitatively, using a tailored framework based on study design, dataset transparency, and clinical applicability. …”
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Comparison of Machine Learning and Deep Learning Models Performance in predicting wind energy by Saswati Rakshit, Anal Ranjan Sengupta

“…Therefore, this present study offers a robust framework for researchers and practitioners aiming to leverage machine learning and time series forecasting in the realm of renewable energy prediction. …”
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RL-Cervix.Net: A Hybrid Lightweight Model Integrating Reinforcement Learning for Cervical Cell Classification by Shakhnoza Muksimova, Sabina Umirzakova, Jushkin Baltayev, Young-Im Cho

“…<b>Results:</b> The innovative integration of RL into the CNN framework allowed RL-Cervix.Net to achieve an unprecedented classification accuracy of 99.98% in identifying atypical cells indicative of cervical lesions. …”
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PerfCam: Digital Twinning for Production Lines Using 3D Gaussian Splatting and Vision Models by Michel Gokan Khan, Renan Guarese, Fabian Johnson, Xi Vincent Wang, Anders Bergman, Benjamin Edvinsson, Mario Romero, Jeremy Vachier, Jan Kronqvist

“…We introduce PerfCam, an open source Proof-of-Concept (PoC) digital twinning framework that combines camera and sensory data with 3D Gaussian Splatting and computer vision models for digital twinning, object tracking, and Key Performance Indicators (KPIs) extraction in industrial production lines. …”
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A Model for Diagnosing Mild Nutrient Stress in Facility-Grown Tomatoes Throughout the Entire Growth Cycle by Yunpeng Yuan, Guoxiang Sun, Guangyu Chen, Qihua Zhang, Lingwei Liang

“…This study proposes a deep learning framework based on CNN + LSTM, using canopy near-infrared spectroscopy from different growth stages of tomatoes as input, to diagnose mild stress of nitrogen (N), potassium (K), and calcium (Ca) throughout the entire growth cycle of facility-grown tomatoes. …”
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EKNet: Graph Structure Feature Extraction and Registration for Collaborative 3D Reconstruction in Architectural Scenes by Changyu Qian, Hanqiang Deng, Xiangrong Ni, Dong Wang, Bangqi Wei, Hao Chen, Jian Huang

“…To address these challenges, this paper proposes an efficient deep graph matching registration framework that effectively integrates interpretable feature extraction with network training. …”
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Inductive and Transfer Learning‐Based Hybrid Model Techniques for Accurate and Automated Diagnosis of Neurological Diseases by Saroj Kumar Pandey, Yogesh Kumar Rathore, Sunakshi Mehra, Anurag Sinha, Tarun Raj Kumar, Ankit Kumar, Rekh Ram Janghel, Ayodele Lasisi, Quadri Noorulhasan Naveed, Md. Sazid Reza

“…ABSTRACT Purpose This study presents NeuroDL, a novel deep learning‐based diagnostic framework designed for the automated detection of brain tumors and Alzheimer's disease (AD) using magnetic resonance imaging (MRI). …”
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Binding Affinity Prediction for Pancreatic Ductal Adenocarcinoma Using Drug-Target Descriptors and Artificial Intelligence by Pragya, A. Amalin Prince, Jac Fredo Agastinose Ronickom

“…Our study demonstrates the potential of an AI-driven framework as an effective and scalable solution for disease-specific drug-target interaction prediction, with promising implications for drug repurposing in PDAC.…”
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Improving Road Semantic Segmentation Using Generative Adversarial Network by Arnick Abdollahi, Biswajeet Pradhan, Gaurav Sharma, Khairul Nizam Abdul Maulud, Abdullah Alamri

“…Comparisons demonstrate that the proposed GAN framework outperforms prior CNN-based approaches and is particularly effective in preserving edge information.…”
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TMS: Ensemble Deep Learning Model for Accurate Classification of Monkeypox Lesions Based on Transformer Models with SVM by Elsaid Md. Abdelrahim, Hasan Hashim, El-Sayed Atlam, Radwa Ahmed Osman, Ibrahim Gad

“…Conclusions: The results of the study show that the proposed hybrid framework achieves robust diagnostic performance in monkeypox detection, offering potential utility for enhanced disease monitoring and outbreak management. …”
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MDFT-GAN: A Multi-Domain Feature Transformer GAN for Bearing Fault Diagnosis Under Limited and Imbalanced Data Conditions by Chenxi Guo, Vyacheslav V. Potekhin, Peng Li, Elena A. Kovalchuk, Jing Lian

“…To address these challenges, this paper proposes a novel fault diagnosis framework based on a Multi-Domain Feature Transformer GAN (MDFT-GAN). …”
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A Deep Learning-Based Echo Extrapolation Method by Fusing Radar Mosaic and RMAPS-NOW Data by Shanhao Wang, Zhiqun Hu, Fuzeng Wang, Ruiting Liu, Lirong Wang, Jiexin Chen

“…However, most of these extrapolation network architectures are built upon convolutional neural networks, using radar echo images as input. …”
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An Unsupervised Learning Method for Radio Interferometry Deconvolution by Lei Yu, Bin Liu, Cheng-Jin Jin, Ru-Rong Chen, Hong-Wei Xi, Bo Peng

“…Building on this insight, we develop a deep dictionary (realized through a convolutional neural network), which is designed to be multiresolution and overcomplete, to achieve sparse representation and integrate it within the CS framework. …”
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Quality-Aware PPG-Based Blood Pressure Classification for Energy-Efficient Trustworthy BP Monitoring Devices With Reduced False Alarms by Yalagala Sivanjaneyulu, M. Sabarimalai Manikandan, Srinivas Boppu, Linga Reddy Cenkeramaddi

“…The proposed framework includes a high-pass filter (HPF), PPG signal quality assessment (PPG-SQA), PPG waveform feature extraction (FE), and BP classification (hypertension and non-hypertension (NHT)). …”
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Synthesizing field plot and airborne remote sensing data to enhance national forest inventory mapping in the boreal forest of Interior Alaska by Pratima Khatri-Chhetri, Hans-Erik Andersen, Bruce Cook, Sean M. Hendryx, Liz van Wagtendonk, Van R. Kane

“…In this study, we present a framework for forest type classification combining field plots and high-resolution remote sensing data using machine learning models in the boreal forest of Interior Alaska. …”
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Application of deep learning for diagnosis of shoulder diseases in older adults: a narrative review by Sung Min Rhee

“…Recent research highlights the effectiveness of DL-based convolutional neural networks and machine learning frameworks in diagnosing various shoulder pathologies. …”
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Use of Artificial Intelligence in Imaging Dementia by Manal Aljuhani, Azhaar Ashraf, Paul Edison

“…Artificial intelligence algorithms (machine learning and deep learning) enable automation of neuroimaging interpretation and may reduce potential bias and ameliorate clinical decision-making. Graph convolutional network-based frameworks implicitly provide multimodal sparse interpretability to support the detection of Alzheimer’s disease and its prodromal stage, mild cognitive impairment. …”
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Hybrid Reinforcement Learning-Based Collision Avoidance Algorithm for Autonomous Vehicle Clusters by Chubing Guo, Jianshe Wu, Panzheng Luo, Zhigang Wang, Kai Zhang, Ziyi Yang, Zengfa Dou, Kan Song

“…A hybrid reinforcement learning framework is designed, which consists of a deep neural network structure and a reinforcement learning structure. …”
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Validation of Replicable Pipeline 3D Surface Reconstruction for Patient-Specific Abdominal Aortic Lumen Diagnostics by Edoardo Ugolini, Giorgio La Civita, Moad Al Aidroos, Samuele Salti, Giuseppe Lisanti, Emanuele Ghedini, Gianluca Faggioli, Mauro Gargiulo, Giovanni Rossi

“…The goal is to provide a solid tool for geometric reconstruction to a more complex enhanced diagnostic framework. <b>Methods:</b> A U-Net convolutional neural network is trained using preoperative CTA scans, with 101 for model training and 14 for model testing, covering a wide anatomical and aortoiliac pathology spectrum. …”
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Explainable Siamese Neural Networks for Detection of High Fall Risk Older Adults in the Community Based on Gait Analysis by Christos Kokkotis, Kyriakos Apostolidis, Dimitrios Menychtas, Ioannis Kansizoglou, Evangeli Karampina, Maria Karageorgopoulou, Athanasios Gkrekidis, Serafeim Moustakidis, Evangelos Karakasis, Erasmia Giannakou, Maria Michalopoulou, Georgios Ch Sirakoulis, Nikolaos Aggelousis

“…Methods: By leveraging convolutional neural networks (CNNs) and Siamese neural networks (SNNs), the proposed framework effectively addresses the challenges of limited datasets and delivers robust predictive capabilities. …”
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