Title : Leveraging big data and artificial intelligence for precision diagnostics and cardiovascular disease prevention

Background: The rapid expansion of digital health data and advances in artificial intelligence (AI) and machine learning (ML) have created new opportunities for laboratory medicine. Cardiovascular diseases (CVDs), a leading cause of global mortality, particularly in populations with high burdens of diabetes, hypertension, and adverse lifestyle factors, require improved approaches for early risk assessment and disease stratification. Association rule mining (ARM), a data-mining technique capable of identifying clinically meaningful relationships within large datasets, has emerged as a promising tool to support precision diagnostics and personalized medicine.

Methods: This review talk will evaluate the role of AI, ML, and ARM in laboratory medicine and clinical diagnosis. The available literature and research studies were utilised to provide the evidence from using electronic health records, biochemical markers, and data-mining approaches to identify disease patterns, predict outcomes, and improve risk stratification, with a particular focus on CVD and diabetes-related conditions.

Results: AI-driven analytical models can enhance interpretation of laboratory and clinical data, facilitate early disease detection, and support decision-making in resource-constrained healthcare systems. ARM enables the discovery of hidden associations among clinical variables, improving identification of cardiovascular risk factors and patient outcome prediction. Integration of ML with high-quality electronic health records and imaging data further strengthens predictive performance. However, implementation remains limited by concerns regarding algorithmic bias, privacy, transparency, legal accountability, and ethical governance.

Conclusion: AI and ML have substantial potential to transform laboratory medicine and improve cardiovascular risk assessment through advanced pattern recognition and data integration. Successful clinical adoption will require robust ethical frameworks, protection of patient privacy, algorithm validation, interdisciplinary collaboration, and targeted education of healthcare professionals to ensure responsible and effective implementation.

Prof. (Dr) Pradeep Kumar Dabla is an experienced Chairperson with a demonstrated history of more than 25yrs of Leadership & Administration while working in the hospital & health care industry. He is a laboratory physician skilled in Medical Devices, Molecular Biology, Biotechnology, Laboratory Medicine, Clinical Research& Artificial Intelligence. A strong research professional who also support the NABL, as Lead & Technical Assessor ISO 15189:2022& ISO 17043:2023 focused in Medical Laboratories, NABL, Quality Council, Govt. of India. Due to keen interest in research and laboratory medicine, he has been awarded Thrice for his research work in “Postmenopausal Women CAD Risk & Gene Polymorphism” and “Diabetic CAD Risk” and received another Five International Awards& Travel Grants. He has been invited several times as speaker, chair at National & International congresses.He received Eminent Teacher Award from Delhi Medical Association, Fellowship ACBI(Association of Clinical Biochemists of India), Fellowship IMSA (International Medical Sciences Academy), and Member- National Academy of Medical Sciences (MAMS),Honorary Fellowship GAPIO 2024 (Honorary GAPIO Global Association of Physicians of India Origin), Awarded GAPIO Excellence in Diagnostics Award 2024. He has 105 research papers to his credit in reputed National & International journals. In addition he also published 5 book chapters, 2 books, 2 Patents (in Artificial Intelligence) and more than 60 abstracts as a part of invited talks and papers presented.