Artificial intelligence (AI) is playing a dominant role in advancing heart failure detection and diagnosis, significantly furthering personalized healthcare. This review synthesizes AI-driven innovations by examining methodologies, applications, and outcomes. We investigate the integration of machine learning algorithms, diverse datasets including electronic health records (EHRs), medical records, imaging data, and clinical notes, deep learning models, and neural networks to enhance diagnostic accuracy. Key advancements include prediction models that leverage real-time data from wearable devices alongside state-of-the-art AI systems trained on patient data from hospitals and clinics. Notably, recent studies have reported diagnostic accuracies ranging from 86.7% to as high as 99.9%, with sensitivity and specificity values often exceeding 97%, underscoring the potential of these AI systems to improve early detection and clinical decision-making substantially. Our review further explores the impact of symmetry and asymmetry in model design, highlighting that symmetric architectures like U-Net offer computational efficiency and structured feature extraction. In contrast, asymmetric models improve the sensitivity to rare conditions and subtle clinical patterns. Incorporating these deep learning (DL) methods in anomaly detection and disease progression modeling further reinforces their positive impact on diagnostic accuracy and patient outcomes. Furthermore, this review identifies challenges in current AI applications, such as data quality, algorithmic transparency, model bias, and evaluation metrics, while outlining future research directions, including integrating generative models, hybrid architectures, and explainable AI techniques to optimize clinical practice.

Early, accurate diagnosis of Alzheimer’s Disease (AD) is vital for effective intervention. Properly classifying its progression from cognitively normal to moderate dementia is essential for tailoring treatment and management plans. The proposed research is a hybrid deep learning framework that integrates the EfficientNet-B3 and the ResNet50 with sophisticated attention units in order to classify the MRI scans of Alzheimer's disease in multi-classes. The model proposed combines both a Convolutional Block Attention Module (CBAM) based on the refinement of channels and space features and Multi-Head Self-Attention based on cross-branch feature interaction. The dual-branch architecture yields complementary features, with EfficientNet-B3 being able to pick fine-grained patterns and ResNet50 being able to pick strong hierarchical representations. The characteristics in both branches are mapped to 512 dimensions, operated by multi-head attention classification. The model and extensive preprocessing were implanted on a series of 33,984 augmented Alzheimer's MRI images in four categories (MildDemented, ModerateDemented, NonDemented and VeryMildDemented. This hybrid model had outstanding performance of 98.21% test accuracy, 98.23% precision, 98.21% recall, and 98.21% F1-score, which was far much better than the accuracies of other baseline architectures such as VGG16 (74.11%), ResNet50 (93.68%), EfficientNetB0 (63.52%), DenseNet121 (64.12%), and CustomCNN (68.87%). These findings support the usefulness of hybrid systems consisting of attention mechanisms to diagnose Alzheimer's disease automatically by using neuroimaging information.