Abstract
Artificial intelligence (AI) is becoming ubiquitous in health care, largely through machine learning and predictive analytics applications. Recent applications of AI to common health care scenarios, such as screening and diagnosing, have fueled optimism about the use of advanced analytics to improve care. Careful and objective considerations need to be made before implementing an advanced analytics solution. Critical evaluation before, during, and after its implementation will ensure safe care, good outcomes, and the elimination of waste. In this commentary we offer basic practical considerations for developing, implementing, and evaluating such solutions based on many years of experience.
Highlights
Artificial intelligence (AI) is becoming ubiquitous in health care, largely through machine learning and predictive analytics applications. Many of these tools have been available for decades, their recent application to common health care scenarios, such as screening and diagnosing, has fueled optimism about the use of advanced analytics to improve care
Researchers recently demonstrated that a deep convolutional neural network (CNN) may enable automated screening and diagnosis for retinopathy of prematurity with high accuracy and repeatability
Considerations before selecting an advanced analytics solution A comprehensive list of factors to consider is outside the scope of this commentary, but we offer some basic requirements based on many years of experience developing, implementing, and evaluating advanced analytics tools: 1. Define a use case: Documenting the functional requirements of the advanced analytics solution is an important and valuable requirement for developing a solution
Summary
Artificial intelligence (AI) is becoming ubiquitous in health care, largely through machine learning and predictive analytics applications Many of these tools have been available for decades, their recent application to common health care scenarios, such as screening and diagnosing, has fueled optimism about the use of advanced analytics to improve care. researchers from Johns Hopkins University developed a novel machine-learning approach that provides rapid, remote, frequent, and objective assessment of Parkinson’s disease symptom severity using smartphones [3]. These tools are important contributors to consistent decision quality in treatment planning. It is the time to assess the existing infrastructure and the capacity and capabilities of the organization
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