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, 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. 
Patchy healthcare coverage means that patients often must dig into their own pockets for treatment. In the U.S., one in four American families turn down necessary medical care due to cost. These high costs of care are often related to the burden of excessive administration costs.
AI can significantly cut down these costs. Machine learning can find patterns in patient admission and discharges to determine which patient categories tend to overstay in hospitals -- a major expense for providers -- and reduce patients’ stays accordingly. The same algorithms can see patients at risk of readmission, so they can remain under close monitoring. AI can also enable streamlining of processes and creation of more user-friendly workflows, reducing staff time spent on tasks so the time can be used elsewhere. By powering virtual chatbots, AI eliminates unnecessary in-person doctor visits and readmissions, which can potentially save billions of dollars annually.
The use of AI to predict diseases is in its early stages, but the technology is already being used to treat patients with more precision.
As more healthcare providers invest in population health management, AI is helping drive more accurate risk stratification. By finding patterns based on large subsets of data, AI lends greater precision to segmenting patients based on risk levels and identifying a proactive course of action to treat those deemed high-risk patients.
In the case of imaging, machine learning and AI can help rule out false negatives – a valuable aid to clinicians. The UK government recently announced new medical technology centers that will use AI to aid in disease diagnosis. One such center, the London Medical Imaging and Artificial Intelligence Centre for Value-Based Healthcare, will apply AI to detect anomalies in scans, helping with earlier detection. Intelligent information will set new precedents when it comes to diagnosis accuracy. The healthcare industry has the capabilities to do this today and are creating the proof points to broaden its use.
While precision medicine is in varying stages of maturity across markets, I believe AI will serve as a springboard for its uptake. No one can deny the benefit of combining different information sets to create a single point of view for more accurate patient diagnosis. Technology will make disease assessment and interpretation even more sophisticated, so we can find the right treatment for the right person at the right time.