In aquatic environments, the health of fish populations is crucial for maintaining ecological balance and sustaining aquaculture industries. Timely and accurately detecting fish diseases is paramount for effective management and mitigation strategies. This paper presents a novel approach for fish disease detection leveraging transfer learning and ensemble techniques. Our method combines features extracted from three pre-trained deep learning models, namely VGG-16, MobileNet-V2, and Inception-V3, with a Support Vector Machine (SVM) for classification. Through empirical experimental evaluation on a comprehensive dataset, we demonstrate the effectiveness of our proposed model in accurately detecting various fish diseases. The results showcase significant improvements in sensitivity and specificity compared to existing approaches. Additionally, we analyze the impact of different transfer learning strategies and feature fusion techniques on the model’s overall performance. Our findings underscore the potential of transfer learning and ensemble methods in enhancing fish disease detection systems, offering promising avenues for future research in aquatic health management and aquaculture.

Epileptic seizures are sudden disturbances in the brain’s electrical activity, disrupting normal function. Early detection, accurate diagnosis, and timely treatment are essential to manage and reduce seizure frequency. This paper presents an intelligent model for epileptic seizure detection using two standard imbalanced EEG datasets. Electroencephalogram (EEG) signals serve as input for training and testing the model. The Slantlet Transform (SLT), an advanced signal processing technique, is used for effective feature extraction, offering improved localization and representation of EEG signals. These extracted features are utilized in a spiking neural network (SNN) to create a hybrid model, SLTSNN. The model is evaluated using the CHB-MIT and UCI-ESR datasets. Performance metrics such as accuracy, sensitivity, and F1-score are calculated and compared with five machine learning classifiers Logistic Regression (LRC), Support Vector Machine (SVMC), Long Short-Term Memory (LSTM), Random Forest (RFC), and Decision Tree (DTC) and one deep learning model, the Convolutional Neural Network (CNN). SLTSNN outperforms all baseline models, achieving 98% accuracy, 98% sensitivity, and 96% F1-score on the UCI-ESR dataset, and 93% across all three metrics on the CHB-MIT dataset. It also performs better than the results reported in eight existing methods.

In the healthcare and clinical domain, artificial intelligence (AI) is evolving from earlier models that primarily predicted outcomes or generated content toward agentic AI systems that demonstrate the capability to make decisions and complete tasks autonomously. Previous research on AI has contributed significantly to disease identification, deep learning applications, large language models (LLMs), and generative AI. These systems primarily function as assistive tools, as they generate text or predictions without directly interacting with clinical infrastructures. Therefore, recent research trends are increasingly oriented toward agentic AI systems that extend beyond traditional predictive and generative model performance. This manuscript provides a detailed review of the current state of agentic AI, starting with the evolution of AI and the concept of a medical agent. A medical agent refers to an intelligent AI system designed to assist in clinical or administrative tasks by analyzing data, supporting decision making, and interacting with healthcare environments. Its underlying agentic AI architecture integrates planning, memory, reasoning, and environmental interaction to enable autonomous tool use, multi-agent collaboration, and continuous perception decision action loops across diverse healthcare applications and clinical workflows. The review further examines safety mechanisms, including human-in-the-loop oversight, self-verification strategies, and regulatory alignment frameworks, which are designed to ensure reliability, accountability, compliance, and safe deployment in regulated healthcare environments. Our findings indicate that a large number of AI agents have been introduced in various manuscripts for healthcare applications; however, fully autonomous systems remain challenging to achieve, as AI still faces several limitations related to reliability, interpretability, data dependency, and integration within complex clinical workflows. In response to these challenges, this survey shifts the focus from task-specific model performance to system-level autonomy and workflow orchestration, providing a structured foundation for understanding the design, deployment, governance, and limitations of agentic AI systems in modern healthcare ecosystems.

Automated arrhythmia detection from electrocardiogram (ECG) signals is crucial and important for the early treatment of cardiac disease (CD). In this investigation, eight machine-learning models have been developed to identify improved ECG arrhythmia detection using two standard datasets (MIT-BIH Arrhythmia and the ECG 5000). In the first phase, two types of feature extraction schemes (autoencoder) and (Convolution) are used to obtain relevant features from ECG samples and subsequently, eight ML models are successfully trained and tested to find various performance matrices through simulation-based experiments. Then, the TOPSIS and mRMR ranking schemes are used to rank the ML models and identify the three best-performing models recommended for real-time arrhythmia detection. In this study, it is observed that for the same number of input features, models based on autoencoder features offer enhanced performance compared to those based on convolutional features. It is generally observed that the top identified hybrid model, Autoencoder Features with Neural Network (AEFNN) on the MIT-BIH dataset, achieves an accuracy of 97.96% and on the ECG5000 dataset, the hybrid model achieves an accuracy of 99.20%. This proposed model can be utilized for the early detection of arrhythmia, particularly in large-scale healthcare screening programs, thereby aiding in timely diagnosis and intervention. In this study, two types of features are used to model development in future work. Other relevant important features can be extracted from ECG samples, and those features can be used to develop accurate models to identify Heart disease.