By Arthur A. Burley 
The current healthcare system has many limitations. One of them is the high degree of health inequalities. There are huge differences in healthcare access and clinical performance depending on where you live and what care environment you choose. A woman screened for breast disease in rural areas has a lower chance of finding it early than a woman who goes to an urban center that does more than 300 screenings per day. Next-generation technologies like A.I., machine learning, and blockchain will make this a reality. They will allow patients and providers to access healthcare in any location.
These gaps in life expectancy and socioeconomic conditions and the large variations in standard care are not only found in emerging countries like , these gaps are just as apparent in rural America compared in a city like San Francisco or Boston. Virtual care, connected health systems, and powerful data analytics promise a universal standard for patient care as trained clinicians are becoming a declining and unsustainable resource.
Philips and other tech companies are key to this future. We can use machine learning and artificial intelligence to improve precision diagnosis. This technology can eliminate operator error and inconsistency and give physicians tools to find the disease early.
By 2025, the market for A.I. in healthcare will exceed $36 billion[1]. Providers and hospitals are discovering a wide range of possible applications for A.I. in healthcare. These include improving workflow and connectivity and enhancing image analysis and segmentation.
The Three As of A.I. and Healthcare
Accessibility
More than 150 countries today have a healthcare system that covers at least 90% of their citizens. There are many variations in the access to specialists and basic care. As the industry shifts towards value-based care, healthcare must be closer to patients. We also need to ensure that rural imaging can be performed without regard to the geographical location of the radiologists.
Virtual radiology allows for high-quality diagnostic images to be reviewed from anywhere. This is possible because A.I. algorithms can support workflows in the cloud. Virtual radiology can be a game-changer for equalizing access to healthcare. A.I. is the technology that powers it, providing connectivity, reducing human error, and improving quality.
A.I. is also an indicator of adaptive intelligence. The system learns and gets smarter by using and codifying past knowledge to understand patients better. This allows clinicians and patients to receive real-time guidance so that they can spot signs of medical emergencies. A.I. was a breakthrough in A.I.’s ability to identify patients most at risk of developing sepsis last year. This requires extensive analysis of complex data sets.
A hub and spoke healthcare delivery model is being developed in Europe and North America. This allows care to be delivered mostly through outpatient clinics and ambulatory channels. The focus is on prevention and outpatient care. Hospital visits are reserved for more complex cases. A.I. can connect remote doctors to hospitals, collect data and determine the best location for clinics and the service mix that is most appropriate for the community.
A.I. is making progress in opening up access. InfiniteMD, a U.S. startup, offers second-opinion video consultations for patients all over the globe. This is available to patients who might not otherwise be able to access them due to geographical or financial limitations. They are now developing an algorithm to aid cancer patients in their treatment decisions and connect them to global treatment options. [4]
Affordability
Patients often have to pay out of their own pockets due to the lack of healthcare coverage. One in four American families refuses to pay for medical care because it is too expensive. This is often due to high administrative costs.
These costs can be significantly reduced by A.I. Machine learning can identify patient admissions and discharges patterns and determine which patient groups tend to stay longer in hospitals. This is a significant expense for providers and could help reduce patient stays. These algorithms can also identify patients at high risk of readmission and allow them to be closely monitored. A.I. can streamline processes and create more user-friendly workflows. This can reduce staff time spent on tedious tasks, allowing them to spend their time doing other things. A.I. can be used to power virtual chatbots. This could potentially reduce the number of unnecessary readmissions and doctor visits, saving billions annually.
Accuracy
Although A.I. is still in its infancy, A.I. is being used to predict disease. However, A.I. is already being used to diagnose and treat patients more precisely.
A.I. helps improve risk stratification as more healthcare providers invest more in population health management. A.I. can identify patterns in large subsets and determine a proactive course for high-risk patients.
Machine learning and A.I. can rule out false positives in imaging. This is a valuable tool for clinicians. Recently, the U.K. government announced new medical technology centers [5] that will utilize A.I. to assist in disease diagnosis. The London Medical Imaging and Artificial Intelligence Centre of Value-Based Healthcare will use A.I. to detect anomalies in scans and help with their earlier detection. Intelligent information will create new standards in diagnosis accuracy. The Healthcare industry can achieve this today, and they are creating evidence points to expand its use.
Although precision medicine is still in various stages of maturity, A.I. will be a catalyst for its adoption. Combining multiple information sets can lead to a more accurate diagnosis. The technology will allow us to find the best treatment for each patient at the right moment.
A.I. is the future of healthcare
A.I., like any new technology, raises questions about privacy, safety, and the potential repercussions of combining machine-based care with human care. It won’t happen quickly, and there will be periods for testing to see if the technology works efficiently and safely.
However, I believe that healthcare will change dramatically if we look forward to the next five years. This will be driven by economic necessity. We will see standardized care in all settings, easier access, and virtual experts available for consultation on cases from around the globe. A.I. will be at the core of this paradigm shift.