There’s been no shortage of studies demonstrating the capacity of AI to diagnose specific diseases more quickly than, and often more accurately, human beings, and as AI’s potential is increasingly recognized some stakeholders are pointing to specific diseases where AI should be applied immediately.
In a recent commentary, for example, Steven Coca, DO, associate chair of research for the department of medicine at Mount Sinai, and cofounder of RenalytixAI, a developer of AI-enabled diagnostics for kidney disease, pointed to the alarming rise of chronic kidney disease, in recent years, which is being fueled, in part, by a steady increase in diabetes cases.
“It has been reported that approximately 12 million individuals in the United States have diabetic kidney disease (DKD),” he notes, “and 38% of end-stage kidney disease in the United States is caused by diabetes; more than any other cause, including high blood pressure. Currently, 44% of new cases of CKD are caused by diabetes,6 and this number has continued to grow.”
Given these increases, he argues, “it is more important now than ever for healthcare providers to have the ability to detect kidney disease at its earliest stages. Early and accurate risk stratification of individuals likely to experience rapid kidney function decline or kidney failure is critical for improving patient outcomes, clinical resource allocation, and cost control.”
So, how can providers get their arms around this growing problem?
One key way, Coca says, is to call on new AI technology. “Using machine learning algorithms to analyze electronic health record information and proven predictive blood-based biomarkers associated with kidney disease,” he explains, “researchers have created a predictive model to help identify patients with CKD with high risk of progressing to dialysis and transplant and patients who are at a lower risk for progression. Used in conjunction with clinical evaluation to match appropriate management and treatment strategies that can delay or prevent progression to kidney failure, this application of AI for CKD holds great promise as it will enable healthcare providers to better stratify patients and determine which patients must be seen by a specialist.”
Earlier identification, in turn, will allow providers to take an array of steps to treat symptoms sooner, while patients “found to be at low risk for rapid kidney function decline will avoid potentially unnecessary specialist referral and medical interventions. More accurate decisions about referral, monitoring, and treatment will have a positive impact on long-term patient outcomes, reducing the percentage of patients with DKD who progress to ESRD, dialysis, and kidney transplant.”
In Coca’s view, the results of the first wave of AI-enabled diagnostic technologies for the nephrology and endocrinology market have been very promising, and as those results are increasingly recognized, provider interest in AI is bound to grow.