Big Data Analytics (BDA) has garnered significant attention in both academia and industries, particularly in sectors such as healthcare, owing to the exponential growth of data and advancements in technology. The integration of data from diverse sources and the utilization of advanced analytical techniques has the potential to revolutionize healthcare by improving diagnostic accuracy, enabling personalized medicine, and enhancing patient outcomes. In this paper, we aim to provide a comprehensive literature review on the application of big data analytics in healthcare, focusing on its ecosystem, applications, and data sources. To achieve this, an extensive analysis of scientific studies published between 2013 and 2023 was conducted and overall 180 scientific studies were thoroughly evaluated, establishing a strong foundation for future research and identifying collaboration opportunities in the healthcare domain. The study delves into various application areas of BDA in healthcare, highlights successful implementations, and explores their potential to enhance healthcare outcomes while reducing costs. Additionally, it outlines the challenges and limitations associated with BDA in healthcare, discusses modelling tools and techniques, showcases deployed solutions, and presents the advantages of BDA through various real-world use cases. Furthermore, this study identifies and discusses key open research challenges in the field of big data analytics in healthcare, aiming to push the boundaries and contribute to enhanced healthcare outcomes and decision-making processes.

In recent years, large language models (LLMs) have been employed significantly in different domains of computing education. Nevertheless, these models have been focused on essential adherence to their integration as coding assistants in computing education. However, attention has been switched to thoroughly examining and analyzing LLM behavior, particularly in computing education for programming tasks such as code generation, code explanation, and programming error message explanation. Therefore, it becomes imperative to understand their behavior to examine potential pitfalls. This article addresses this gap systematically and details how different LLM-based coding chatbots, such as ChatGPT, Codex, Copilot, and others, react to various coding inputs within computing education. To achieve this objective, we collected and analyzed articles from 2021 to 2024, and 72 studies were thoroughly examined. These objectives include investigating the existing limitations and challenges associated with utilizing these systems for coding tasks, assessing their responses to prompts containing coding syntax, examining the impact of their output on student learning, and evaluating their performance as debugging tools. The findings of this review highlight that it is premature to incorporate these systems into computing education due to their limitations that may limit their effectiveness as comprehensive coding assistants for computer science students. These limitations include issues with handling prompts containing code snippets, potential negative impacts on student learning, limited debugging capabilities, and other ineffectiveness. The finding also reports multiple research directions that can be considered in future research related to LLMs in computing education.

Prompt Engineering has emerged as a pivotal technique in Natural Language Processing, providing a flexible approach for leveraging pre-trained language models. Particularly, a prompt is used to instruct the model to adopt the nature of given prompts, which became a well-adoptable approach in wide areas of domains. Yet, existing prompt-guided frameworks are experiencing various challenges, such as crafting prompts for specific tasks to achieve clarity and conciseness and avoid ambiguity, which requires time and computational resources. Further existing methods heavily rely on the extensive labelled datasets, yet many domain-specific challenges exist, particularly in healthcare. This study presents Prompt-Eng, a novel framework emphasizing its wide-ranging applications in healthcare, where we design precise prompts with positive and negative aspects; we hypothesize that designing prompts in pairs helps models to generalize effectively. We delve into the significance of quick design and optimization, highlighting its influence in shaping model responses. In addition, we explore the increasing demand for prompts that are aware of the context in multimodal data analysis and the incorporation of prompt engineering in new machine-learning approaches. The essence of our approach is in creating tailored prompts, which serve as instructive guidelines for the models during the prediction procedure. The proposed methodology emphasizes utilizing context-aware prompt pairs to facilitate interpreting and extracting healthcare information from a health corpus by models. The study uses the medical MIMIC-III \footnotehttps://physionet.org/content/mimiciii/1.4/ corpus to predict medicine prescriptions. The paper also explores visual and textual prompts for X-ray image analysis for pneumonia prediction on the MIMIC-CXR \footnote\urlhttps://physionet.org/content/mimic-cxr/2.0.0/ dataset. This approach stands out from existing methods by addressing challenges such as clarity, conciseness, and context awareness, thereby enabling improved interpretation and extraction of healthcare information from diverse data sources.

Skin cancer is one of the most prevalent forms of cancer worldwide, and early and accurate diagnosis critically impacts patient outcomes. Given the sensitive nature of medical data and its fragmented distribution across institutions (data silos), privacy-preserving collaborative learning is essential to enable knowledge-sharing without compromising patient confidentiality. While federated learning (FL) offers a promising solution, existing methods struggle with heterogeneous and missing modalities across institutions, which reduce the diagnostic accuracy. To address these challenges, we propose an effective and flexible Personalized Multimodal Federated Learning framework (PMM-FL), which enables efficient cross-client knowledge transfer while maintaining personalized performance under heterogeneous and incomplete modality conditions. Our study contains three key contributions: (1) A hierarchical aggregation strategy that decouples multi-module aggregation from local deployment via global modular-separated aggregation and local client fine-tuning. Unlike conventional FL (which synchronizes all parameters in each round), our method adopts a frequency-adaptive synchronization mechanism, updating parameters based on their stability and functional roles. (2) A multimodal fusion approach based on multitask learning, integrating learnable modality imputation and attention-based feature fusion to handle missing modalities. (3) A custom dataset combining multi-year International Skin Imaging Collaboration(ISIC) challenge data (2018–2024) to ensure comprehensive coverage of diverse skin cancer types. We evaluate PMM-FL through diverse experiment settings, demonstrating its effectiveness in heterogeneous and incomplete modality federated learning settings, achieving 92.32% diagnostic accuracy with only a 2% drop in accuracy under 30% modality missingness, with a 32.9% communication overhead decline compared with baseline FL methods.