Deep learning analysis for rheumatologic imaging: current trends, future directions, and the role of human by Jucheol Moon, Pratik Jadhav, Sangtae Choi

“…Traditional imaging techniques, including plain radiography, ultrasounds, computed tomography, and magnetic resonance imaging (MRI), play a critical role in diagnosing and monitoring these conditions, but face limitations like inter-observer variability and time-consuming assessments. Recently, deep learning (DL), a subset of artificial intelligence, has emerged as a promising tool for enhancing medical imaging analysis. …”
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Bridging technology and ecology: enhancing applicability of deep learning and UAV-based flower recognition by Marie Schnalke, Jonas Funk, Andreas Wagner, Andreas Wagner

“…Challenges remain, such as detecting flowers in dense vegetation and accounting for environmental variability.…”
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TCN-MAML: A TCN-Based Model with Model-Agnostic Meta-Learning for Cross-Subject Human Activity Recognition by Chih-Yang Lin, Chia-Yu Lin, Yu-Tso Liu, Yi-Wei Chen, Hui-Fuang Ng, Timothy K. Shih

“…However, real-world deployment faces two major challenges: (1) significant cross-subject signal variability due to physical and behavioral differences among individuals, and (2) limited labeled data, which restricts model generalization. …”
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Adaptive Outdoor Cleaning Robot with Real-Time Terrain Perception and Fuzzy Control by Raul Fernando Garcia Azcarate, Akhil Jayadeep, Aung Kyaw Zin, James Wei Shung Lee, M. A. Viraj J. Muthugala, Mohan Rajesh Elara

“…Outdoor cleaning robots must operate reliably across diverse and unstructured surfaces, yet many existing systems lack the adaptability to handle terrain variability. This paper proposes a terrain-aware cleaning framework that dynamically adjusts robot behavior based on real-time surface classification and slope estimation. …”
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Deep learning (DL)‐based advancements in prostate cancer imaging: Artificial intelligence (AI)‐based segmentation of 68Ga‐PSMSA PET for tumor volume assessment by Sharjeel Usmani, Khulood Al Riyami, Subash Kheruka, Shah P Numani, Rashid al Sukaiti, Maria Ahmed, Nadeem Pervez

“…Traditional manual segmentation methods are time‐consuming processes that are further challenged by inter‐observer variability. Artificial intelligence (AI)‐based segmentation techniques offer a promising solution to these challenges. …”
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Computer-Aided Diagnosis Techniques for Brain Tumor Segmentation and Classification Using MRI by Gadicha A. B., Kale Prachi V., Dalvi G. D., Mohod M. M., Pakhale S. C., Khan S. M.

“…Additionally, the paper highlights the challenges associated with model generalization, dataset limitations, preprocessing variability, and computational resource constraints. …”
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Advanced Diagnosis of Cardiac and Respiratory Diseases from Chest X-Ray Imagery Using Deep Learning Ensembles by Hemal Nakrani, Essa Q. Shahra, Shadi Basurra, Rasheed Mohammad, Edlira Vakaj, Waheb A. Jabbar

“…This study introduces a deep learning ensemble approach that integrates Convolutional Neural Networks (CNNs), including ResNet-152, VGG19, EfficientNet, and a Vision Transformer (ViT), to enhance diagnostic accuracy. …”
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Artificial Intelligence-Based Methodologies for Early Diagnostic Precision and Personalized Therapeutic Strategies in Neuro-Ophthalmic and Neurodegenerative Pathologies by Rahul Kumar, Ethan Waisberg, Joshua Ong, Phani Paladugu, Dylan Amiri, Jeremy Saintyl, Jahnavi Yelamanchi, Robert Nahouraii, Ram Jagadeesan, Alireza Tavakkoli

“…Despite challenges such as technical variability, data privacy concerns, and regulatory barriers, the potential of AI-enhanced neuroimaging to revolutionize early diagnosis and personalized treatment in neurodegenerative and neuro-ophthalmic disorders is immense. …”
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Influence of cognitive networks and task performance on fMRI-based state classification using DNN models by Murat Kucukosmanoglu, Javier O. Garcia, Justin Brooks, Kanika Bansal

“…This study highlights the application of interpretable DNNs in revealing cognitive mechanisms associated with task performance and individual variability.…”
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Enhanced Heart Disease Classification Using Dual Attention Mechanisms and 3D-Echo Fusion Algorithm in Echocardiogram Videos by S Deepika, N. Jaisankar

“…Furthermore, our proposed method addresses challenges related to the variability and complexity of cardiac data, aligning with current research that advocates for automated learning models in interpreting echocardiographic imagery. …”
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Preliminary Electroencephalography-Based Assessment of Anxiety Using Machine Learning: A Pilot Study by Katarzyna Mróz, Kamil Jonak

“…However, challenges such as data variability, noise, and model interpretability remain significant. …”
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A Novel Multimodal Deep Learning Approach With Loss Function for Detection of Sleep Apnea Events by Alireza Fakhim Babaei, Jafar Tanha, Mohammad Ali Balafar, Seyedehsan Roshan

“…Detecting sleep apnea accurately and efficiently presents several challenges, including variability in physiological signals among individuals and class imbalance for apnea events. …”
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Extraction of Clinically Relevant Temporal Gait Parameters from IMU Sensors Mimicking the Use of Smartphones by Aske G. Larsen, Line Ø. Sadolin, Trine R. Thomsen, Anderson S. Oliveira

“…Stride time predictions were highly accurate (<5% error), while stance and swing times exhibited moderate variability and double support time showed the highest errors (>20%). …”
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Application of Gated Recurrent Unit in Electroencephalogram (EEG)-Based Mental State Classification by Gst. Ayu Vida Mastrika Giri, Ngurah Agus Sanjaya ER, I Ketut Gede Suhartana

“…Due to high signal variability and sensitivity to noise, correct classification is still tricky, even with advances in the analysis of EEG signals. …”
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Hand Gesture Recognition in Indian Sign Language Using Deep Learning by Harsh Kumar Vashisth, Tuhin Tarafder, Rehan Aziz, Mamta Arora, Alpana

“…Recognizing hand gestures in sign languages is a challenging task due to the high variability in hand shapes, movements, and orientations. …”
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Unsupervised Deep Clustering on Spatiotemporal Objects Extracted from 4D Point Clouds for Automatic Identification of Topographic Processes in Natural Environments by J. Wang, K. Anders

“…Subsequently, after jointly optimizing the reconstruction and clustering loss, our model generates unique clusters with high intra-cluster similarity and inter-cluster variability. We validated the proposed method on a six-month 4D dataset, acquired at Kijkduin sandy beach (The Netherlands), yielding distinctive clusters that correspond to sediment change phenomena. …”
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HEE-SegGAN: A holistically-nested edge enhanced GAN for pulmonary nodule segmentation. by Yong Wang, Seri Mastura Mustaza, Mohammad Syuhaimi Ab-Rahman, Siti Salasiah Mokri

“…However, this task remains challenging due to the complex morphological variability of pulmonary nodules in CT images and the limited availability of well-annotated datasets. …”
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Leveraging artificial intelligence for diagnosis of children autism through facial expressions by Mahmood A. Mahmood, Leila Jamel, Nazik Alturki, Medhat A. Tawfeek

“…Future research needs to include multiple data types as well as extend dataset variability while optimizing hybrid architecture systems to elevate diagnostic forecasting. …”
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State of Charge Estimation in Li-Ion Batteries Using a Parallel LSTM-Based Approach: The Impact of Modeling Based on Operating States by Osman Ozer, Hayri Arabaci

“…Nevertheless, the current-voltage behavior of Li-ion cells varies significantly under different operating conditions, such as charging, discharging, and idle states. This variability negatively impacts the performance of conventional LSTM models. …”
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Accuracy of artificial intelligence in caries detection: a systematic review and meta-analysis by Alexander Maniangat Luke, Nader Nabil Fouad Rezallah

“…Significant variability in study results highlights the need for additional research to comprehend the components affecting AI effectiveness. …”
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