The world recently witnessed strong growth in artificial intelligence (AI) use across various sectors, driven by the digital revolution that began in 2016. Despite this progress, significant concerns persist regarding the black-box nature of AI. Intelligent systems provide decisions without explanations, which has raised pressing issues, particularly in critical domains such as medicine. In medicine, errors can lead to disastrous consequences, putting lives at risk. The "unexplainable" nature of AI is a heavily debated topic in biomedical informatics and computing. Many "black-box" algorithms and systems obscure the logic behind their decisions, leaving users and even developers in the dark about how results are derived. Researchers have developed the field of explainable artificial intelligence (XAI), which holds significant promise for fostering confidence and openness between AI systems and their users. Unlike traditional AI methods, such as deep learning (DL), XAI provides mechanisms for decision-making while offering explanations that are understandable to humans. Medical imaging (MI) plays a crucial role in diagnosing and monitoring a broad range of diseases, and advancements in computer vision, image processing, and the availability of medical image datasets have revolutionized automated MI analysis. However, trust in these systems remains a challenge. To gain the trust of clinicians, authorities, and patients, diagnostic methodologies must be transparent, interpretable, and explainable, clearly conveying the rationale behind specific decisions. This paper reviews the current landscape of XAI methods for medical imaging, including methodologies, techniques, and applications. It covers various types of explanations, such as visual explanations, textual justifications, and example-based reasoning, emphasizing their significance in medical imaging contexts. Furthermore, the paper presents a comparative analysis of XAI methods, evaluating their effectiveness, interpretability, and alignment with medical standards. By identifying research gaps and exploring potential advancements, this review aims to contribute to the development of robust, interpretable, and reliable XAI systems for critical applications like medical imaging, ensuring accountability and fostering trust in AI-powered healthcare systems.

This paper provides a thorough review of recommendation methods from academic literature, offering a taxonomy that classifies recommender systems (RSs) into categories like collaborative filtering, content-based systems, and hybrid systems. It examines the effectiveness and challenges of these systems, such as filter bubbles, the "cold start" issue, and the reliance on collaborative filtering and content-based approaches. We trace the development of RSs, emphasizing the role of machine learning and deep learning models in overcoming these challenges and delivering more accurate, personalized, and context-aware recommendations. We also highlight the increasing significance of ethical considerations, including fairness, transparency, and trust, in the design of RSs. The paper presents a structured literature review, discussing various aspects of RSs, such as collaborative filtering, personalized recommender systems, and strategies to improve system robustness. It also points out the limitations of the existing approaches and suggests promising research directions for the future. In summary, this paper offers a comprehensive analysis of RSs, focusing on their evolution, challenges, and potential future improvements, particularly in enhancing accuracy, diversity, and ethical practices in recommendations.

Medical image analysis using multiple modalities refers to the process of analyzing and extracting information from more than one type of image in order to gain a comprehensive understanding of a given subject. To maximize the potential of multimodal data in improving and enhancing our understanding of the disease, sophisticated classification techniques must be developed as part of the integration process to classify meaningful information from different types of data. A pre-trained model, such as those trained on large datasets such as ImageNet, has learned rich representations that can be used for various downstream tasks. Fine-tuning a pre-trained model refers to the process of further developing the model using the knowledge and representations gained from a pre-existing dataset. In comparison to training a model from scratch, fine-tuning allows knowledge to be transferred from the pre-trained model to the target task, thus improving performance and efficiency. In evolutionary search, the genetic algorithm (GA) is an algorithm that emulates the process of natural selection and genetics. In this context, a population of candidate solutions is generated, fitness is evaluated and new candidate solutions are generated by applying genetic operations such as mutation and crossover. Considering the above characteristics, the present study presents an efficient architecture called Selective-COVIDNet for analyzing COVID-19 cases using a novel selective layer-pruning algorithm. To detect COVID-19 from multimodal data, the current study will use a genetic algorithm to fine-tune the performance of pre-trained models by adjusting specific layers selectively. Furthermore, the proposed approach provides flexibility in the depth of two deep learning architectures, VGG-16 and MobileNet-V2. The impact of freezing specific layers on fine-tuning performance was assessed using five different strategies, namely Random, Odd, Even, Half, and Full Freezing. Therefore, existing pre-trained models can be enhanced for Covid-19 tasks while minimizing their computational burden. For evaluating the effectiveness of the proposed framework, two multi-modal standard datasets are used, including CT-scan images and electrocardiogram (ECG) recordings of individuals with COVID-19. From the conducted experiments, it is found that the proposed framework can detect Covid-19 effectively with accuracy of 98.48% for MobileNet-V2 and 99.65% for VGG-16.