The combination of medical imaging and deep learning has significantly improved diagnostic and prognostic capabilities in the healthcare domain. Nevertheless, the inherent complexity of deep learning models poses challenges in understanding their decision-making processes. Interpretability and visualization techniques have emerged as crucial tools to unravel the black-box nature of these models, providing insights into their inner workings and enhancing trust in their predictions. This survey paper comprehensively examines various interpretation and visualization techniques applied to deep learning models in medical imaging. The paper reviews methodologies, discusses their applications, and evaluates their effectiveness in enhancing the interpretability, reliability, and clinical relevance of deep learning models in medical image analysis.

ChatGPT, developed by OpenAI, is a large language model (LLM) that leverages artificial intelligence (AI) and deep learning (DL) to generate human-like responses. This paper provides a broad, systematic review of ChatGPT’s applications in healthcare, particularly in enhancing patient engagement through medical history collection, symptom assessment, and decision support for improved diagnostic accuracy. It assesses ChatGPT’s potential across multiple organ systems and specialties, highlighting its value in clinical, educational, and administrative contexts. This analysis reveals both the benefits and limitations of ChatGPT, including health literacy promotion and support for clinical decision-making, alongside challenges such as the risk of inaccuracies, ethical considerations around informed consent, and regulatory hurdles. A quantified summary of key findings shows ChatGPT’s promise in various applications while underscoring the risks associated with its integration in medical practice. Through this comprehensive approach, this review aims to provide healthcare professionals, researchers, and policymakers with a balanced view of ChatGPT’s potential and limitations, emphasizing the need for ongoing updates to keep pace with evolving medical knowledge.

Human Activity Recognition (HAR) using embedded sensors in smartphones and smartwatch has gained popularity in extensive applications in health care monitoring of elderly people, security purpose, robotics, monitoring employees in the industry, and others. However, human behavior analysis using the accelerometer and gyroscope data are typically grounded on supervised classification techniques, where models are showing sub-optimal performance for qualitative and quantitative features. Considering this factor, this paper proposes an efficient and reduce dimension feature extraction model for human activity recognition. In this feature extraction technique, the Enveloped Power Spectrum (EPS) is used for extracting impulse components of the signal using frequency domain analysis which is more robust and noise insensitive. The Linear Discriminant Analysis (LDA) is used as dimensionality reduction procedure to extract the minimum number of discriminant features from envelop spectrum for human activity recognition (HAR). The extracted features are used for human activity recognition using Multi-class Support Vector Machine (MCSVM). The proposed model was evaluated by using two benchmark datasets, i.e., the UCI-HAR and DU-MD datasets. This model is compared with other state-of-the-art methods and the model is outperformed.

This article concerns smoke detection in the early stages of a fire. Using the computer-aided system, the efficient and early detection of smoke may stop a massive fire incident. Without considering the multiple moving objects on background and smoke particles analysis (i.e., pattern recognition), smoke detection models show suboptimal performance. To address this, this paper proposes a hybrid smoke segmentation and an efficient symmetrical simulation model of dynamic smoke to extract a smoke growth feature based on temporal frames from a video. In this model, smoke is segmented from the multi-moving object on the complex background using the Gaussian’s Mixture Model (GMM) and HSV (hue-saturation-value) color segmentation to encounter the candidate smoke and non-smoke regions in the preprocessing stage. The preprocessed temporal frames with moving smoke are analyzed by the dynamic smoke growth analysis and spatial-temporal frame energy feature extraction model. In dynamic smoke growth analysis, the temporal frames are segmented in blocks and the smoke growth representations are formulated from corresponding blocks. Finally, the classifier was trained using the extracted features to classify and detect smoke using a Radial Basis Function (RBF) non-linear Gaussian kernel-based binary Support Vector Machine (SVM). For validating the proposed smoke detection model, multi-conditional video clips are used. The experimental results suggest that the proposed model outperforms state-of-the-art algorithms.

XAI (eXplainable AI) has become a pivotal area of research with the advancement of deep learning (DL) technologies and applications. Post-hoc explanation methods interpret deep learning predictions by uncovering the significance of input features, while visualization tools can contribute to a deep understanding of AI model reasoning based on these methods. In this paper, we survey a broad spectrum of post-hoc explanation methods and the visual analytics work based on them. First, we categorize the computational methods into four main types: perturbation-based, gradient-based, decomposition-based, and concept-based. While the first three focus on attributing the model’s output to specific regions of the input image, concept-based methods provide global explanations by mapping human-understandable concepts to high-level features. Then, we examine the methodologies, features, strengths, and limitations of each approach. Moreover, we review existing visualization-focused work based on these computational methods. Finally, we discuss further research challenges and opportunities for XAI visualization with post-hoc explanation.