This review provides a comprehensive analysis of the transformative role of artificial intelligence (AI) in predicting and preventing sports injuries across various disciplines. By exploring the application of machine learning (ML) and deep learning (DL) techniques, such as random forests (RFs), convolutional neural networks (CNNs), and artificial neural networks (ANNs), this review highlights AI’s ability to analyze complex datasets, detect patterns, and generate predictive insights that enhance injury prevention strategies. AI models improve the accuracy and reliability of injury risk assessments by tailoring prevention strategies to individual athlete profiles and processing real-time data. A literature review was conducted through searches in PubMed, Google Scholar, Science Direct, and Web of Science, focusing on studies from 2014 to 2024 and using keywords such as ‘artificial intelligence’, ‘machine learning’, ‘sports injury’, and ‘risk prediction’. While AI’s predictive power supports both team and individual sports, its effectiveness varies based on the unique data requirements and injury risks of each, with team sports presenting additional complexity in data integration and injury tracking across multiple players. This review also addresses critical issues such as data quality, ethical concerns, privacy, and the need for transparency in AI applications. By shifting the focus from reactive to proactive injury management, AI technologies contribute to enhanced athlete safety, optimized performance, and reduced human error in medical decisions. As AI continues to evolve, its potential to revolutionize sports injury prediction and prevention promises further advancements in athlete health and performance while addressing current challenges.

Maintaining effluent quality in wastewater treatment plants (WWTPs) comes with significant challenges under variable weather conditions, where sudden changes in flow rate and increased pollutant loads can affect treatment performance. Traditional physical sensors became both expensive and susceptible to failure under extreme conditions. In this study, we evaluate the performance of soft sensors based on artificial intelligence (AI) to predict the components underlying the calculation of the effluent quality index (EQI). We thus focus our study on three ML models: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Transformer. Using the Benchmark Simulation Model no. 2 (BSM2) as the WWTP, we were able to obtain datasets for training the ML models and to evaluate their performance in dry weather scenarios, rainy episodes, and storm events. To improve the classification of networks according to the type of weather, we developed a Random Forest (RF)-based meta-classifier. The results indicate that for dry weather conditions the Transformer network achieved the best performance, while for rain episodes and storm scenarios the GRU was able to capture sudden variations with the highest accuracy. LSTM performed normally in stable conditions but struggled with rapid fluctuations. These results support the decision to integrate AI-based predictive models in WWTPs, highlighting the top performances of both a recurrent network (GRU) and a feed-forward network (Transformer) in obtaining effluent quality predictions under different weather conditions.

The increasing prevalence of cardiovascular complications in cancer patients due to cardiotoxic treatments has necessitated advanced monitoring and predictive solutions. Cardio-oncology is an evolving interdisciplinary field that addresses these challenges by integrating artificial intelligence (AI) and smart cardiac devices. This comprehensive review explores the integration of artificial intelligence (AI) and smart cardiac devices in cardio-oncology, highlighting their role in improving cardiovascular risk assessment and the early detection and real-time monitoring of cardiotoxicity. AI-driven techniques, including machine learning (ML) and deep learning (DL), enhance risk stratification, optimize treatment decisions, and support personalized care for oncology patients at cardiovascular risk. Wearable ECG patches, biosensors, and AI-integrated implantable devices enable continuous cardiac surveillance and predictive analytics. While these advancements offer significant potential, challenges such as data standardization, regulatory approvals, and equitable access must be addressed. Further research, clinical validation, and multidisciplinary collaboration are essential to fully integrate AI-driven solutions into cardio-oncology practices and improve patient outcomes.

The application of artificial intelligence (AI) in electrocardiography is revolutionizing cardiology and providing essential insights into the consequences of the COVID-19 pandemic. This comprehensive review explores AI-enhanced ECG (AI-ECG) applications in risk prediction and diagnosis of heart diseases, with a dedicated chapter on COVID-19-related complications. Introductory concepts on AI and machine learning (ML) are explained to provide a foundational understanding for those seeking knowledge, supported by examples from the literature and current practices. We analyze AI and ML methods for arrhythmias, heart failure, pulmonary hypertension, mortality prediction, cardiomyopathy, mitral regurgitation, hypertension, pulmonary embolism, and myocardial infarction, comparing their effectiveness from both medical and AI perspectives. Special emphasis is placed on AI applications in COVID-19 and cardiology, including detailed comparisons of different methods, identifying the most suitable AI approaches for specific medical applications and analyzing their strengths, weaknesses, accuracy, clinical relevance, and key findings. Additionally, we explore AI’s role in the emerging field of cardio-oncology, particularly in managing chemotherapy-induced cardiotoxicity and detecting cardiac masses. This comprehensive review serves as both an insightful guide and a call to action for further research and collaboration in the integration of AI in cardiology, aiming to enhance precision medicine and optimize clinical decision-making.

Background/Objectives: This study aimed to evaluate an artificial intelligence (AI)-enhanced electrocardiogram (ECG) screening protocol for improved accuracy, efficiency, and risk stratification across six sports: handball, football, athletics, weightlifting, judo, and karate. Methods: For each of the six sports, resting 12-lead ECGs from healthy children and junior athletes were analyzed using AI algorithms trained on annotated datasets. Parameters included the QTc intervals, PR intervals, and QRS duration. Statistical methods were used to examine each sport’s specific cardiovascular adaptations and classify cardiovascular risk predictions as low, moderate, or high risk. Results: The accuracy, sensitivity, specificity, and precision of the AI system were 97.87%, 75%, 98.3%, and 98%, respectively. Among the athletes, 94.54% were classified as low risk and 5.46% as moderate risk with AI because of borderline abnormalities like QTc prolongation or mild T-wave inversions. Sport-specific trends included increased QRS duration in weightlifters and low QTc intervals in endurance athletes. Conclusions: The statistical analyses and the AI-ECG screening protocol showed high precision and scalability for the proposed athlete cardiovascular health risk status stratification. Additional early detection research should be conducted further for diverse cohorts of individuals engaged in sports and explore other diagnostic methods that can help increase the effectiveness of screening.

Aeration systems are among the most energy-intensive components of wastewater treatment plants (WWTPs), consuming up to 75% of total electricity while being prone to performance degradation caused by diffuser fouling and pressure losses. Traditional maintenance strategies are largely reactive or preventive, leading to inefficient interventions, higher operational costs, and limited fault anticipation. This study addresses the need for an advanced predictive maintenance framework capable of early detection and differentiation of multiple aeration system faults. Using the Benchmark Simulation Model No. 2 (BSM2), two representative degradation scenarios—acute airflow pressure loss and chronic diffuser fouling—were simulated to generate a labeled dataset. A hybrid machine learning approach was developed, combining Random Forest-based feature selection with Long Short-Term Memory (LSTM) neural networks for temporal, multi-label fault classification. To enhance interpretability and operator trust, SHapley Additive exPlanations (SHAP) were applied to quantify feature contributions and provide transparent model predictions. The results show that the proposed framework achieves over 94% detection accuracy and provides early warnings compared to static threshold-based methods. The integration of explainable AI ensures actionable insights for maintenance planning. This approach supports more energy-efficient, reliable, and sustainable operation of WWTP aeration systems and offers a benchmark methodology for future predictive maintenance research.