The term “big data analytics (BDA)” defines the computational techniques to study complex datasets that are too large for common data processing software, encompassing techniques such as data mining (DM), machine learning (ML), and predictive analytics (PA) to find patterns, correlations, and insights in massive datasets. Cardiovascular diseases (CVDs) are attributed to a combination of various risk factors, including sedentary lifestyle, obesity, diabetes, dyslipidaemia, and hypertension. We searched PubMed and published research using the Google and Cochrane search engines to evaluate existing models of BDA that have been used for CVD prediction models. We critically analyse the pitfalls and advantages of various BDA models using artificial intelligence (AI), machine learning (ML), and artificial neural networks (ANN). BDA with the integration of wide-ranging data sources, such as genomic, proteomic, and lifestyle data, could help understand the complex biological mechanisms behind CVD, including risk stratification in risk-exposed individuals. Predictive modelling is proposed to help in the development of personalized medicines, particularly in pharmacogenomics; understanding genetic variation might help to guide drug selection and dosing, with the consequent improvement in patient outcomes. To summarize, incorporating BDA into cardiovascular research and treatment represents a paradigm shift in our approach to CVD prevention, diagnosis, and management. By leveraging the power of big data, researchers and clinicians can gain deeper insights into disease mechanisms, improve patient care, and ultimately reduce the burden of cardiovascular disease on individuals and healthcare systems.

Artificial intelligence (AI) is transforming cardiology across ECG interpretation, imaging, risk prediction, remote monitoring, and workflow automation. Cardiologists need governance models that preserve clinical judgment, reduce harm, and reflect Indian realities. This narrative reviews policy documents (2018-February 2026) spanning the EU AI Act, WHO, OECD, ICMR, FDA GMLP, FUTURE-AI, CHAI, Joint Commission, and MLOps frameworks, alongside medico-legal, automation-bias, fairness, and cardiology-AI implementation studies. We compared these instruments in human-in-the-loop (HITL) oversight and identify gaps in high-risk domains such as echocardiography AI, CT-FFR, cath-lab decision support, and wearable-based rhythm monitoring in India. We propose practical HITL governance priorities for cardiology: local validation, calibrated alerting, explicit override pathways, bias surveillance, medico-legal accountability, and governance structures embedded within everyday cardiac workflows. Realizing meaningful human-in-the-loop oversight requires investment in governance infrastructure, workforce development, transparent performance metrics, and learning systems treating AI-related incidents as opportunities for continuous improvement.

The field of arrhythmia detection and cardiovascular health monitoring is rapidly changing due to wearable technology. With a focus on developments in flexible materials, sensor integration, and electronic design for ongoing arrhythmia monitoring, this review offers a thorough examination of both established and new wearable sensor technologies. The article describes both commercial and experimental devices, such as textile-based, patch, and wrist-worn platforms, emphasizing their performance in clinical and real-world settings as well as their sensing modalities, including bioelectrical, optoelectrical, and mechanoelectrical techniques. The integration of machine learning (ML) and artificial intelligence (AI) algorithms is given special attention because it greatly improves wearable monitors' clinical utility, predictive power, and diagnostic accuracy. There is discussion of case studies, such as the use of deep learning to analyze photoplethysmography (PPG) and electrocardiogram (ECG) data, and their effects on earlier detection and improved management of atrial fibrillation and other arrhythmias. Critically analyzed are issues with data security, regulatory approval, signal fidelity, user adherence, and sensor ergonomics. In order to enhance long-term wearability and user comfort, the review also looks at the market environment, legal frameworks, and advancements in material science, including textile-integrated graphene electrodes and epidermal electronics. The importance of interoperable device architectures, strong privacy protection that complies with international standards, and the ongoing development of AI-driven analytics for real-time decision support in healthcare are highlighted as future research directions. The purpose of the synthesis is to direct researchers, engineers, and clinicians toward the upcoming generation of patient-centered, intelligent wearable technologies for arrhythmia detection.

Background: Artificial intelligence (AI) has moved from research labs into the everyday work of clinicians and software engineers. In healthcare, AI systems now shape triage, imaging interpretation, risk prediction and documentation workflows, while in computer science and AI development, models are increasingly used to generate code, tests and even other AI systems [1–4,7,10,11,76–82]. Early governance frameworks framed “human oversight” as a high-level ethical injunction but provided limited operational guidance on how humans should interact with data-intensive, adaptive AI systems in these settings [5,36–38,76–78]. Objective: To examine how human oversight and human-in-the-loop (HITL) paradigms have developed specifically in (i) healthcare and (ii) computer science/AI development, and to identify converging design, organizational and governance patterns that support meaningful human control in big-data AI environments. Methods: Narrative synthesis of international and national governance instruments (EU AI Act, WHO, OECD, ICMR, FDA/GMLP), sector-specific guidance for healthcare AI (FUTURE-AI, CHAI, Joint Commission) and emerging frameworks for AI lifecycle governance and MLOps in software engineering [7,10,11,14,15,18,19,79–84,86–89]. We extracted definitions and framings of human oversight, technical and organizational requirements for HITL interaction, and implementation challenges related to cognitive load, big-data pipelines and adaptive models in healthcare and AI development. Results: Across healthcare and AI development, contemporary frameworks converge on multi-layered human-in-the-loop governance embedded throughout design, deployment, monitoring and decommissioning. Mandatory and consensus instruments emphasize override capability, transparency, user training, escalation pathways and post-deployment monitoring [7,10,11,14,15,18,19,79,80,86–89]. In both domains, oversight is shifting from ad hoc individual review to structured arrangements involving multidisciplinary committees, model-governance boards, MLOps processes and incident-learning systems. Persistent gaps include limited formal treatment of cognitive load and alert fatigue, difficulties overseeing continuously learning and foundation-model-based systems, and immature metrics for the effectiveness of human oversight itself [61–63,88,114,123,133–137]. Conclusions: Healthcare and computer science/AI development are emerging as mutually informative testbeds for human-in-the-loop AI governance in big-data settings. Meaningful oversight requires more than nominal human review: it depends on human-centered interface design, realistic workload management, lifecycle-oriented technical controls and organizational cultures that make it safe to question AI outputs. Lessons from clinical safety science and MLOps can be combined to architect human–AI interaction that amplifies, rather than erodes, professional judgment in both domains.