New scientific methods, such as genome sequencing for the next decade, produce enormous amounts of biological data that leads us to scientific breakthroughs through careful study and interpretation, However, scholars fail to keep up with all the enormous data blocks. As the generation of salient Information is rising with each day, we can use it in a productive way. The medical data can be useful in fields of personalized medicine whose demand is growing exponentially. The term emerged in 2012, along with the “Industry 4.0” concept, Famous, and refers to big data, in the marketing of IT, in which industrial equipment-generated data could hold more Potential benefit for sector. Large Data Industrials refers to many Diverse produced time series by industrial equipment at a high speed. Preventive medicine for individual patients with chronic conditions, Big Data and healthcare are critical to address the risk of hospitalization. The current review discusses about application of big data in gene sequencing, healthcare, electronic health reports (EHRs), medical scans, genomic sequencing, reports of payers, pharmacy studies, wearables, and medical devices, gathered ample health data, to name a few. This paper mainly talks about the ways in which big data can be used and the tools for analyzing big data. Big data analytics aims to provide innovations that optimize patient care and generate value for healthcare institutions with improvements in medicine, infrastructure and funding.

Priyanka Narad, Himanshu, Hina Bansal. J. Microbiol. Biotechnol. -0001;0:. https://doi.org/10.4014/jmb.2007.07006

This chapter investigates the integration of artificial intelligence, nutrigenomics, and the human polybiome to enhance precision healthcare. It outlines how deep learning and multiomics approaches decode host–microbe interactions influencing nutrition, gene expression, and disease risk. This chapter introduces advanced analytical frameworks linking microbial metabolites, epigenetic regulation, and metabolic balance. By merging computational biology with clinical insight, AI-driven strategies for predictive nutrition, individualized therapy, and early disease intervention can be developed. Furthermore, this chapter discusses the ethical and interpretability dimensions of AI in biomedical data, emphasizing transparent algorithms and privacy-preserving models that ensure reliability, reproducibility, and responsible innovation in personalized healthcare systems.