Abstract
For the past decade, artificial intelligence (AI) and its related technologies have made remarkable advances in marketing and business solutions based on AI-driven big data analysis of customer queries, and it, when coupled with bioinformatics, seemingly holds out great promise for use in healthcare. In reality, however, AI is still largely a buzzword when it comes to disease diagnosis and treatment. This review addresses the uncertainty of AI applications to disease diagnosis and treatment, not only pinpointing AI’s inherent algorithmic problems in dealing with non-patternable stochastic healthcare data, but also revealing the innate fallacy of identifying genetic mutations as a tool for genome-based personalized medicine. Finally, this review concludes by presenting some insights into future AI application in healthcare. Key words: Artificial intelligence, machine learning, deep learning, bioinformatics, healthcare, genomic medicine, personalized medicine, reference genome, genetic variation.
Highlights
Artificial intelligence (AI) has been around for decades since its inception at the 1956 workshop in Dartmouth College
Its technology has been recently hyped with the arrival of machine learning (ML) and deep learning (DL) algorithms whose evolution centered around the artificial neural network (ANN) model to handle complex multi-layered nonlinear data (Schmidhuber, 2014; Bini, 2018)
Even coupled with mountains of healthcare big data, AI‟s critical decision-making around disease diagnosis and treatment is extraordinarily challenging, requiring a new AI algorithm to smartly deal with a multitude of non-patternable stochastic variables or factors associated with each individual disease, epidemic or rare
Summary
The perils of artificial intelligence in healthcare: Disease diagnosis and treatment. BioMolecular Engineering Program and Department of Physics and Chemistry, Milwaukee School of Engineering, Milwaukee, Wisconsin, USA. Artificial intelligence (AI) and its related technologies have made remarkable advances in marketing and business solutions based on AI-driven big data analysis of customer queries, and it, when coupled with bioinformatics, seemingly holds out great promise for use in healthcare. This review addresses the uncertainty of AI applications to disease diagnosis and treatment, pinpointing AI’s inherent algorithmic problems in dealing with non-patternable stochastic healthcare data, and revealing the innate fallacy of identifying genetic mutations as a tool for genome-based personalized medicine. This review concludes by presenting some insights into future AI application in healthcare
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