When new technology gets implemented in healthcare or other sectors of the economy, it’s often necessary to leave the use and understanding of the new to systems to the experts, particularly those who are trained specifically to maintain the equipment and programs.

But AI in healthcare is different, many argue, primarily because of the advantages it will increasingly give doctors when it comes to patient diagnosis.

Writing recently at Forbes, for example, tech entrepreneur Shourjya Sanyal points to five reasons why physicians and caregivers should learn about emerging technology such as data science and artificial intelligence.

First, he says, it will enable them to “diagnose using large volumes of data generated from continuous monitoring.”  Specifically, he says, with the advent of wearable medical device companies . . . clinicians can now look at continuous daily biometric data collected over months.  . . . Basic descriptive statistical results like the average resting heart rate could give you a quick understanding of the overall cardiac health of the patient. More advanced indicators such as stress index or LF/HF ratio of RR distance could be used to predict chances of heart arrhythmias more accurately.”

Next, providers will be able to diagnose using “multiparameter" data.

In his view, “the most significant insight in healthcare is often obtained by combining multiple data sources. For example, combining heart rate and heart rate variability can be used to compute overall stress. Respiratory conditions such as COPD and asthma conditions could be triggered by both internal factors, as well as environmental factors such as pollution.”

Given that fact, learning data science techniques such as data fusion can help physicians understand how data is merged in these systems, and therefore diagnose patients more efficiently.

Yet another advantage Sanyal points to is the ability for doctors to use AI and data science to diagnose using data visualization.

“Radiologists analyze high dimensional medical images such as CT and MRI scans,” he notes, “to aid other specialists such as cardiologists and pulmonologists to deliver critical care. Radiologists are already using machine learning based software tools which automatically color codes the different features of an internal organ. Learning data science will help radiologists understand the strengths and limitations of these software, helping them to deliver even better diagnostic outcomes.”

Rounding out his list are two more systems oriented advantages to doctors understanding AI.

First, they’ll be better able to understand AI workflow. “With the advent of AI, physicians and other caregivers will soon come across multiple health predictors such as early warning scores that were designed using deep learning,” he says. “Understanding how these machine leaning algorithms were designed and therefore their limitations will help caregivers to rely on these early warning scores just the right amount.”

And, second, doctors will be better able to understand the statistical significance of clinical studies. 

In his view, learning data science can help clinicians evaluate the relevance of studies relevant to their expertise, thus helping them choose which ones should be incorporated into their own practice.