Brain stroke, or a cerebrovascular accident, is a devastating medical condition that disrupts the blood supply to the brain, depriving it of oxygen and nutrients. Each year, according to the World Health Organization, 15 million people worldwide experience a stroke. This results in approximately 5 million deaths and another 5 million individuals suffering permanent disabilities. The complex interplay of various risk factors highlights the urgent need for sophisticated analytical methods to more accurately predict stroke risks and manage their outcomes. Machine learning and deep learning technologies offer promising solutions by analyzing extensive datasets including patient demographics, health records, and lifestyle choices to uncover patterns and predictors not easily discernible by humans. These technologies enable advanced data processing, analysis, and fusion techniques for a comprehensive health assessment. We conducted a comprehensive review of 25 review papers published between 2020 and 2024 on machine learning and deep learning applications in brain stroke diagnosis, focusing on classification, segmentation, and object detection. Furthermore, all these reviews explore the performance evaluation and validation of advanced sensor systems in these areas, enhancing predictive health monitoring and personalized care recommendations. Moreover, we also provide a collection of the most relevant datasets used in brain stroke analysis. The selection of the papers was conducted according to PRISMA guidelines. Furthermore, this review critically examines each domain, identifies current challenges, and proposes future research directions, emphasizing the potential of AI methods in transforming health monitoring and patient care.

Background: COPD is a chronic disease characterized by frequent exacerbations that require hospitalization, significantly increasing the care burden. In recent years, the use of artificial intelligence-based tools to improve the management of patients with COPD has progressed, but the prediction of readmission has been less explored. In fact, in the state of the art, no models specifically designed to make medium-term readmission predictions (2–3 months after admission) have been found. This work presents a new intelligent clinical decision support system to predict the risk of hospital readmission in 90 days in patients with COPD after an episode of acute exacerbation. Methods: The system is structured in two levels: the first one consists of three machine learning algorithms —Random Forest, Naïve Bayes, and Multilayer Perceptron—that operate concurrently to predict the risk of readmission; the second level, an expert system based on a fuzzy inference engine that combines the generated risks, determining the final prediction. The employed database includes more than five hundred patients with demographic, clinical, and social variables. Prior to building the model, the initial dataset was divided into training and test subsets. In order to reduce the high dimensionality of the problem, filter-based feature selection techniques were employed, followed by recursive feature selection supported by the use of the Random Forest algorithm, guaranteeing the usability of the system and its potential integration into the clinical environment. After training the models in the first level, the knowledge base of the expert system was determined on the training data subset using the Wang–Mendel automatic rule generation algorithm. Results: Preliminary results obtained on the test set are promising, with an AUC of approximately 0.8. At the selected cutoff point, a sensitivity of 0.67 and a specificity of 0.75 were achieved. Conclusions: This highlights the system’s future potential for the early identification of patients at risk of readmission. For future implementation in clinical practice, an extensive clinical validation process will be required, along with the expansion of the database, which will likely contribute to improving the system’s robustness and generalization capacity.

Background: Chronic Obstructive Pulmonary Disease (COPD) represents a significant global health burden, characterized by complex diagnostic and management challenges. Artificial Intelligence (AI) presents a powerful opportunity to enhance clinical decision-making and improve patient outcomes by leveraging complex health data. Objectives: This scoping review aims to systematically map the existing literature on AI applications in COPD. The primary objective is to identify, categorize, and summarize research into three key domains: (1) Diagnosis, (2) Clinical Symptoms, and (3) Clinical Outcomes. Methods: A scoping review was conducted following the Arksey and O’Malley framework. A comprehensive search of major scientific databases, including PubMed, Scopus, IEEE Xplore, and Google Scholar, was performed. The Population–Concept–Context (PCC) criteria included patients with COPD (Population), the use of AI (Concept), and applications in healthcare settings (Context). A global search strategy was employed with no geographic restrictions. Studies were included if they were original research articles published in English. The extracted data were charted and classified into the three predefined categories. Results: A total of 120 studies representing global distribution were included. Most datasets originated from Asia (predominantly China and India) and Europe (notably Spain and the UK), followed by North America (USA and Canada). There was a notable scarcity of data from South America and Africa. The findings indicate a strong trend towards the use of deep learning (DL), particularly Convolutional Neural Networks (CNNs) for medical imaging, and tree-based machine learning (ML) models like CatBoost for clinical data. The most common data types were electronic health records, chest CT scans, and audio recordings. While diagnostic applications are well-established and report high accuracy, research into symptom analysis and phenotype identification is an emerging area. Key gaps were identified in the lack of prospective validation and clinical implementation studies. Conclusions: Current evidence shows that AI offers promising applications for COPD diagnosis, outcome prediction, and symptom analysis, but most reported models remain at an early stage of maturity due to methodological limitations and limited external validation. Future research should prioritize rigorous clinical evaluation, the development of explainable and trustworthy AI systems, and the creation of standardized, multi-modal datasets to support reliable and safe translation of these technologies into routine practice.

Background: Chronic Obstructive Pulmonary Disease (COPD) often leads to acute exacerbations requiring hospitalization. While artificial intelligence (AI) has been increasingly used to improve COPD management, predicting whether the length of hospital stay (LOHS) will exceed clinically relevant thresholds remains insufficiently explored. Methods: This study presents a novel clinical decision support system to predict whether LOHS following an acute exacerbation will surpass specific cutoffs (6 or 10 days). The approach involves two stages: (1) clinical, demographic, and social variables are encoded into structured signals and transformed into spectrogram-like images via a pipeline based on sinusoidal encoding and Mel-frequency cepstral coefficients (MFCCs); and (2) these images are fed into a Convolutional Neural Network (CNN) to estimate the probability of extended hospitalization. Feature selection with XGBoost reduced dimensionality to 16 variables. The model was trained and tested on a dataset of over 500 patients. Results: On the test set, the model achieved an AUC of 0.77 for predicting stays longer than 6 days and 0.75 for stays over 10 days. Sensitivity and specificity were 0.79/0.72 and 0.74/0.80, respectively. Conclusions: This system leverages image-based data formalization to predict LOHS in COPD patients, facilitating early risk stratification and more informed clinical planning. Results are promising, but external validation with larger and more diverse datasets remains essential.