Artificial Intelligence (AI) is rapidly transforming modern healthcare, biomedical research, and pharmaceutical development by enabling the analysis of large and complex datasets. AI technologies, including machine learning (ML), natural language processing...
The proliferation of artificial intelligence in healthcare has introduced transformative capabilities alongside complex ethical challenges requiring rigorous scholarly examination. This article provides a comprehensive ethical analysis of three...
At the present time, AI is poised to revolutionise nearly every sector of the healthcare industry. Research into this technology is necessary so that its various medicinal uses may be identified. Analysed "Artificial intelligence" and "Artificial...
The integration of artificial intelligence into healthcare systems presents multifaceted technical and pedagogical challenges that require comprehensive multidisciplinary approaches. This article examines the practical obstacles encountered in AI...
The concept of artificial intelligence (AI) has a lengthy history. It has become evident that achieving human-level intelligence is more complex than initially expected. Presently, there is a resurgence of interest in AI, driven by a substantial...
Artificial intelligence is the capability of computer systems to emulate and replicate human intelligence. Artificial intelligence has transformed the accuracy and efficacy of diagnostics and therapeutics across multiple medical disciplines. Artificial...
A great deal of terminology pertaining to AI, ML, and deep learning is utilised extensively in today's corporate, healthcare, industrial, and military lexicons. No matter the size of the data, precise prediction and analysis are of the utmost importance in...
Although resilient performance (RP) in health services can be both enhanced and hindered by the use of digital technologies (DTs), little is known on how this influence occurs through design. This study introduces a framework for designing resilient health...
Computerization in medical services is rapidly transforming the landscape of modern healthcare, particularly within critical environments such as the Operating Room (OR) and Intensive Care Unit (ICU). The integration of advanced digital technologies has led...
Multimorbidity denotes the simultaneous presence of two or more chronic illnesses inside an individual. Consequently, individuals with multimorbidity possess diverse and specific treatment requirements. In reality, fulfilling these objectives is challenging...
The use of quantum computing (QC) and machine learning (ML) is on the rise in medical decision-making. These technologies can analyse large datasets, enhance diagnoses, and make personalised therapies possible. In many real-world applications, QC is still...
Artificial Intelligence (AI) has emerged as a transformative force across scientific domains, with particularly profound implications in the field of medicine. Originally conceptualized in the mid-20th century as a means to replicate human intelligence, AI...
The integration of artificial intelligence into healthcare systems presents multifaceted technical and pedagogical challenges that require comprehensive multidisciplinary approaches. This article examines the practical obstacles encountered in AI...
Estimates put the amount of new healthcare data at 2,314,000 gigabytes in 2022, according to the global trend towards digitising health care systems. Better reasoning and more effective utilisation of obtained data are continuous goals of intelligent system...
Artificial intelligence (AI) refers to the utilization of computers and technology to emulate intelligent behavior and critical thinking akin to that of a person. In 1956, John McCarthy initially defined the term artificial intelligence as the science and...
The rapid advancement of large language models (LLMs) such as GPT and BERT has revolutionized multiple industries, including healthcare, finance, supply chain management, and cybersecurity. While these models demonstrate remarkable capabilities in natural...
Data-driven research, together with its implications in machine learning and the broader domain of artificial intelligence, have the capacity to instigate significant transformations in medicine. Nevertheless, medicine is not a science akin to others. It is...
Data quality has emerged as a fundamental pillar of scientific integrity, organizational performance, and evidence-based decision-making in the contemporary digital landscape. As artificial intelligence systems become increasingly integrated into...
The rapid advancement of Large Language Models (LLMs) has significantly transformed industries such as healthcare, finance, e-commerce, travel, and cybersecurity by enabling intelligent automation, advanced analytics, and human-like communication. However,...
Artificial Intelligence (AI) data governance has become a critical component in ensuring the responsible, secure, and transparent deployment of AI systems across multiple domains. This article explores the implementation of AI data governance in supply...
Distributed computing particularly, fault tolerant systems has indispensable functionality in maintaining the dependability and availability of the actual time applications across various sectors including but not limited to healthcare, aerospace,...
Visual Data Mining is an interdisciplinary approach that integrates data mining algorithms with advanced visualization techniques to support effective knowledge discovery from large and complex datasets. As the volume, variety, and velocity of data continue...
The goal of this literature review is to summarise the most important findings from recent studies on AI in healthcare settings. When it comes to solving difficult medical challenges, artificial intelligence has been a game-changer. Artificial...
The growing complexity of global regulations in sectors such as e-commerce and healthcare has made traditional manual compliance systems inefficient and difficult to scale. This paper proposes an agentic AI–driven regulatory intelligence framework that...
Ensemble machine-learning models have achieved striking published results for financial transaction fraud detection, with recent peer-reviewed studies reporting F1-scores as high as 0.99 and accuracy exceeding 99.9 percent on benchmark credit-card fraud...
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