Advances in artificial neural networks as a disease prediction tool

Abstract

Throughout the last decade, utilization of machine learning has seen a sharp rise in fields such as computing, transportation, engineering, and medicine. Artificial neural networks (ANNs) have demonstrated increased application due to their versatility and ability to learn from large datasets. The emergence of electronic health records has propelled healthcare into an era of personalized medicine largely aided by computers. This review summarizes the current state of ANNs as a predictive tool in medicine and the downfalls of reliance on a self-adjusting computer network to make healthcare decisions. Medical ANN studies can be grouped into three categories - diagnosis, classification, and prediction, with diagnostic studies currently dominating the field. However, recent trends show prediction studies may soon outnumber the remaining categories. ANN prediction studies dominate in fields such as cardiovascular disease, neurologic disease, and osteoporosis. Neural networks consistently show higher predictive accuracy than industry standards. But several pitfalls are preventing mainstream adoption. Clinicians often rely on situational pearls to make complex healthcare decisions, ANNs often do not account for intuitive variables during their analysis. Instead, ANNs rely on incomplete patient data and ‘black box’ computing to make decisions that are not completely transparent to the end-user. This has led to ‘runaway’ networks that may ultimately make inaccurate and harmful decisions. This review emphasizes the extensive potential of machine learning in medicine and the obstacles that must be overcome to utilize its full potential.