The integration of artificial intelligence into personalized medicine

The integration of artificial intelligence into personalized medicine Priya Hays, CEO and Science Writer at Hays Documentation Specialists, LLC, discusses the integration of Artificial Intelligence (AI) into personalized medicine (PM), highlighting its potential to enhance healthcare, particularly in genomic medicine and precision oncology. When personalized medicine (PM) emerged as a field with the growth and implementation of omics, such as genomics and proteomics, stakeholders were already becoming aware of the potential of Big Data and Artificial Intelligence to further enhance PM innovation and technologies. Genomic medicine grew concomitantly with the development of next-generation sequencing and vast streams of data that were required for storage and portability. Artificial Intelligence, of which Big Data is a foundation, involves the analysis of huge reservoirs of data to reveal information about patterns in the data, and is increasingly being used in healthcare settings. In precision oncology, AI methods have found fruitful applications in imaging and pathology, improving accuracy, sensitivity, and specificity in the analysis of radiographic images and histologic slides. In pathology, through algorithmic and statistical modeling, patterns in the specimens can be identified and aid in clinical decision making through image analysis, whereby abnormal features can be detected, leading to more efficient decision making and accurate and personalized diagnosis. The relationships between Machine Learning (ML) and Deep Learning (DL) methods can be summarized as follows: ML is a subset of AI, and DL, a subset of ML, utilizes artificial networks, such as convolutional neural networks and artificial neural networks, to enable more complex learning processes, particularly in cancer diagnosis. (1)