Precision medicine is a method of identifying the phenotypes of patients who have less frequent responses to treatment or unique healthcare needs. Artificial intelligence (AI) is a powerful tool that can be used to generate information, reason, learn, and augment doctors' decision-making. Multidimensional biological data and AI approaches are necessary to decipher pathogenicity. This article will explore the ways in which AI can be used in modern medicine and provide a vision of the future. AI has the potential to automate some of the tasks of the diagnostic pathway, which could have far-reaching implications for clinical care and the health economy.
Investing in machine learning now will bring long-term benefits in terms of financial savings from precision medicine and improved population health. Eleven teams have reported promising results from applying AI to precision medicine. AI and machine learning can be used in medical chemistry and the validation of emerging drug targets. AI algorithms can also be used to address complex unsolved problems in neurodevelopmental disorders. The k-nearest neighbor (KNN) algorithm has been used in several scientific fields, including healthcare.
AI can make workflows more efficient and reliable, which is an important goal for personalized medicine. Using epilepsy as an example, this article will discuss how AI can be used to address complexity in NDDs. The genomic profile can add the sensitivity required by the AI algorithm to design a drug at the individual or subpopulation level. Frontline healthcare workers will need training on the strengths and limitations of AI, as well as how to interpret and translate knowledge derived from AI into information that is clinically meaningful for patients. AI algorithms use learning strategies based on classification or pattern recognition to input data in order to predict from future data sets. The application of AI in medicine is a rapidly growing area with great potential for improving healthcare services.
