The future of healthcare is already here. From automated insulin pumps to diagnostic instruments that can interpret their own results, today’s medical devices are smarter and more sophisticated than ever. What’s driving these innovations? The answer is data.

Building diagnostic machines that learn

X-rays, MRIs, and ultrasounds produce diagnostic images that let doctors identify abnormalities that can’t be seen from the outside. But what if the same machines that took the images could also interpret the results? 

Data analytics can be used to help imaging devices learn to recognize abnormal scans. Smart imagers could soon be connected to vast image libraries and patient health records. Using machine learning, these devices would be able to make connections between image characteristics and ultimate health outcomes to improve diagnostic accuracy. By analyzing vast datasets, the devices would be able to find correlations that humans are likely to miss.

Medical imagers powered by data analytics could automatically alert doctors to changes from previous scans or abnormalities that require further review. Similar analytical techniques could be used to power other kinds of diagnostic devices such as blood chemistry analyzers as well. While machines will never replace skilled doctors and technicians, tapping into the power of data analytics could significantly increase diagnostic speed and accuracy and allow healthcare providers to spend more time on patient care and communication.

Closing the loop for drug delivery

Diagnostics is just one part of the equation. What if we could build medical devices that can not only interpret test results but also recommend or deliver courses of treatment?

Combining data analytics and sensor data can help us “close the loop,” creating smart devices that respond automatically to changes detected by sensors. We are already seeing some of these applications now, such as the artificial pancreas.  As the technology advances, it will open up other possibilities for automated drug delivery and other sensor and data-driven smart applications. Medical devices or mHealth apps built with data analytics could be used to automate drug delivery or simply give patients day-to-day guidance to help them better manage chronic conditions.

Collecting and analyzing data from disparate sources can help patients and providers better understand which factors are impacting health outcomes. For example, the precise amount of a drug a patient needs can vary depending on a lot of factors: patient characteristics, daily activity levels and behaviors, diet, even the weather. Doctors can’t take all of these factors into consideration each day and adjust dosages. But a smart medical device could bring together the relevant data from many different sources—body-worn sensors, weather reports, medical records, diagnostic results, diet-tracking apps and more—to make real-time recommendations.

Technology exists today that uses sophisticated analytics to power smart diagnostic and therapeutic devices. The backbone of the analytics used for medical devices is a process that relies on science. Skill sets in data sciences, statistics, biostatistics, machine learning and other quantitative research methods are combined with expertise from a vast array of industries and cutting-edge engineering. It’s one more way that Big Data is driving innovation in the medical device industry.