Integrating Artificial Intelligence, Machine Learning, and Molecular Sciences in Biomedical Research: Applications in Laboratory Medicine, Cancer Biology, and Public Health

Abstract

Artificial intelligence (AI) and machine learning (ML) are rapidly transforming biomedical research, offering novel solutions to longstanding challenges in laboratory medicine, cancer biology, molecular sciences, drug discovery, and public health. These technologies enable the analysis of vast, complex datasets, facilitating pattern recognition, predictive modeling, and data integration at scales beyond human capability. In laboratory medicine, AI and ML enhance diagnostic accuracy, automate repetitive processes, reduce errors, and shorten turnaround times, while supporting decision-making through intelligent data interpretation. In cancer biology, machine learning models integrate genomic, proteomic, imaging, and clinical data to predict disease progression, therapeutic response, and patient outcomes, advancing precision oncology. Similarly, AI applications in molecular sciences, including molecular pathology and allergen classification, improve disease categorization and personalized treatment strategies. In drug discovery, AI accelerates compound screening, predicts drug-target interactions, and supports rational design of therapeutics, exemplified by technologies such as AlphaFold, which accurately models protein structures. Public health also benefits from AI-driven predictive modeling for outbreak detection, risk stratification, and resource optimization. Despite these advancements, challenges remain, including the need for high-quality, representative datasets, algorithm validation, ethical considerations, data privacy, and workforce training. Interdisciplinary collaboration among clinicians, researchers, data scientists, and policymakers is essential to ensure responsible and effective implementation. Looking forward, AI and ML are poised to redefine biomedical research, enabling data-driven insights, personalized medicine, and improved healthcare outcomes, provided that technical, ethical, and regulatory challenges are addressed.