A State-of-the-art Review on Artificial Intelligence and Machine Learning Applications

The integration of artificial intelligence and machine learning holds great promise for enhancing healthcare institutions and providing fresh perspectives on the origins and advancement of long-term illnesses. In the healthcare sector, artificial intelligence and machine learning are used to address supply and demand concerns, genomic applications, and new advancements in drug development, cancer, and heart disease. The article explores the ways that machine learning, AI, precision medicine, and genomics are changing healthcare. The essay also discusses how AI's examination of various patient data could enhance healthcare institutions, provide fresh insights into chronic conditions, and advance precision medicine. The potential uses of machine learning for genome analysis are also examined in the paper, particularly about genetic biomarker-based disease risk and symptom prediction. The challenges posed by the phenotype-genotype relationship are examined, as well as the significance of comprehending disease pathways in order to create tailored treatments. Moreover, it offers a streamlined and modularized method that predicts how genotypes affect cell properties using machine-learning models, enabling the development of personalized drugs. The collective feedback highlights the rapid interdisciplinary growth of medical genomics following the completion of the Human Genome Project. It also emphasizes how important genomic data is for improving healthcare outcomes and facilitating personalized medicine. The study's conclusions point to a revolutionary shift in healthcare: the application of AI/ML to illness control. Even though these innovations have a lot of potential benefits, problems like algorithm interpretability and ethical issues need to be worked out before they can be successfully incorporated into routine medical practice. Using machine learning in medicine has enormous potential benefits for the biotech industry. Further research, ongoing regulatory frameworks, and collaboration between medical professionals and data analysts are necessary to fully utilize machine learning as well as artificial intelligence in disease management.