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.

Highlights

  • Artificial intelligence (AI) is the capability of computers to learn from their environment and adapt over time to achieve a goal without human input

  • This review focuses on the recent advances of Artificial neural networks (ANNs) in disease prediction, its pitfalls, and future pathways towards advancement

  • Another study used MRI imaging data to compare various convolutional neural network configurations ability to predict the outcomes of 222 ischemic stroke patients who were treated with tissue plasminogen activator versus those who were not treated with tPA

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Summary

– Introduction

Artificial intelligence (AI) is the capability of computers to learn from their environment and adapt over time to achieve a goal without human input. Each node within a hidden layer assigns a bias based on how powerful the neural network determines its data is at determining an output. A study aiming to predict a first cardiovascular event over 10 years in non-diseased individuals analyzed 30 risk factors with four different machine learning algorithms and compared them to the American Heart Association (AHA) prediction model. They found that amongst all models, neural networks successfully predicted 4,998/7,404 cardiovascular events (sensitivity 67.5%) and 53,458/75,585 of non-cases. A later study recognized that deep neural networks had little advantage over logistic regression models when predicting heart failure readmission and were susceptible to overfitting.

10 Year ASCVD risk
– Discussion
Findings
– Data availability
– Conclusion
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