Conventional treatment methods make even the most basic healthcare issues more complicated, which in turn increases the number of parties involved. Classical computing lacks the speed and accuracy needed for effective stakeholder collaboration in COVID-19 healthcare solutions, such as patients, insurance agents, healthcare practitioners, pharmaceutical suppliers, etc. The research uses organizational information processing theory (OIPT) to examine how quantum computing which is applications of artificial intelligence (AI) could transform the healthcare business, creating a more sustainable and less burdened system. The study of quantum computing (QC) has the potential to bring about “quantum leaps,” which might have unforeseen consequences for healthcare. The discovery of new medications, the personalization of medicinal treatments, and the acceleration of DNA sequencing are just a few of the many possible applications of this method. The potential of QC to transform compute-intensive healthcare tasks like drug-discovery, personalized-medicine, DNA-sequencing, medical-imaging, and operational-optimization is the primary focus of this survey paper, which offers the first comprehensive analysis of QCs diverse capabilities in improving healthcare systems. After a thorough literature study, we created taxonomies on the healthcare QC paradigm’s history and supporting technologies, applications, needs, architectures, security, outstanding questions, and future research prospects. We hope that by conducting this survey, researchers with varying levels of experience in quantum computing and healthcare will better understand the state of the art, assess opportunities and threats, and make informed decisions as they develop novel architectures and applications for this emerging field.

Large Language Models (LLMs) like ChatGPT, GPT-4, and Claude have revolutionized natural language processing, but they are increasingly vulnerable to adversarial "jailbreaking" attacks that bypass safety protocols. This paper proposes a novel multi-layered security framework to defend LLMs against jailbreaking techniques using a combination of adversarial training, behavior watermarking, and real-time anomaly detection. We present an innovative system called SAFE-LLM (Security-Aware Fine-tuned & Encrypted Language Model) to achieve robust AI safety.

The rapid growth of cloud computing services has led to a significant increase in the energy consumption of data Centres, posing both economic and environmental challenges. To address this issue, there is a growing need for intelligent and adaptive resource management strategies that can optimize energy usage without compromising performance. This research proposes an AI-based dynamic resource management framework for energy-efficient cloud data Centres. The framework leverages machine learning algorithms to predict workload patterns, optimize virtual machine (VM) placements, and dynamically adjust resource allocation in real-time. By integrating predictive analytics with intelligent scheduling techniques, the proposed system effectively minimizes idle server usage, reduces power consumption, and improves overall operational efficiency. Experimental results using simulated cloud environments demonstrate significant energy savings while maintaining desired Quality of Service (QoS) levels. This study highlights the potential of AI-driven solutions in enhancing the sustainability and cost-effectiveness of modern cloud infrastructures.