Artificial Intelligence is the great healthcare equalizer

I believe that Philips and other health tech companies hold a key to this future as we develop ways we can use artificial intelligence and machine learning to advance precision diagnosis. With this technology, we can help eliminate operator error and inconsistency and provide physicians with tools to help find disease as early as possible. 

The market for AI in healthcare is expected to surpass $36 billion by 2025[1]. Hospitals and providers are seeing a vast range of potential applications, from improving workflow and connectivity to enhancing image analysis and patient segmentation.

The Three As of AI in healthcare

Accessibility 

Today, more than 150 countries have some form of healthcare system that provides coverage to at least 90 percent of their citizens. [2] Yet, variations in access to basic and specialist care are vast. As the industry shifts to value-based care, we need to bring healthcare closer to patients and make sure those who have imaging performed in rural areas can benefit from expert, “virtual” analysis independent of where the radiologist is located geographically.

Receiving high-quality diagnostic images for review anywhere in the world, for example, is possible due to virtual radiology as AI algorithms can be supported in the cloud, speeding workflows and rapidly consolidating data. Virtual radiology is a game-changer in equalizing access to care, and AI is the technology powering it – ensuring connectivity, reducing human error and enhancing quality. 

AI is also a precursor to adaptive intelligence – where the system gets smarter and learns by use and codifying existing knowledge and past diagnoses to better understand patients and diseases. This allows real-time guidance to be given to patients or clinicians so they can catch signs of medical emergency. Last year, in a major breakthrough, AI showed how it can help identify patients at highest risk for developing sepsis[3], which often requires intensive analysis of highly complex data sets.

In North America and Europe, a ‘hub and spoke’ model for healthcare systems is emerging, through which care is delivered primarily through ambulatory channels and outpatient clinics with a focus on better prevention and out-of-hospital care, reserving hospital visits for complex care cases. AI can collate data from the clinics to a centralized war room, connect remote doctors with expertise from hospitals and determine the right location of clinics and service mix based on community needs.

AI is already making headway in enabling access. U.S. start-up InfiniteMD provides second-opinion video consultations to patients around the world, who may not otherwise have access due to financial or geographical constraints. Building on that, they are developing an algorithm for cancer patients that would aid in treatment decision-making and connect them with global treatment options or clinical trials. [4]