Faced with the modern-day challenges of healthcare it is becoming increasingly more difficult to practice value-based medicine in today’s climate of volume-based medicine. Over the past decade, we’ve seen health systems around the world experience significant strain due to increasing population numbers and age. There is a clear need to maximize efficiency, lighten the burden of stretched human resources and harness the medical sector’s mountains of data.

Since it is unlikely, in the near future, that administrators will tell radiologists to forget about metrics and relative value units (RVUs), and instead focus on providing a value-based interpretation for the patient, new solutions are needed to deal with the increasing workload challenges while continuing to provide value for the patient and referring clinician.

Fortunately, we now live in an exciting time of deep learning, and solutions are on the horizon. Artificial Intelligence has in fact been around since the 1950’s. Slowly but surely, artificial intelligence is infiltrating almost every aspect of our lives. It is already busy in the background of many routine tasks, powering virtual assistants like Siri and Alexa, recommendations from Amazon and Netflix, and underpinning billions of Google searches each day. But as the technology matures, AI’s impact will become more profound, and nowhere is that more apparent than in healthcare.

It is not an exaggeration to say that the potential for this technology to enhance the way we manage and treat illness is almost unimaginable today. Today’s smart-tech monitors everything from blood sugar to heart pressure. It is not unreasonable to think that by 2050, the laboratory of the future will not be in a pharmaceutical lab but a patient’s own body, where AI highlights health risks, enabling humans to make better care decisions. Tech start-ups all over are trying to address social needs and disabilities from a technological standpoint. Blindness and deafness are two such ailments, and AI startup Halfcode seeks to launch a product that will aid those visually and/or hearing impaired.

Although it’s clear to see the future revolutionary potential, AI in healthcare is still in its infancy. Still, AI research is moving quickly on a multiplicative logarithmic trajectory. Applications that didn’t exist two years ago are now available. Prior challenges are becoming strengths, which are leading to advances in radiology and medicine.

Healthcare’s data-heavy nature makes it an ideal candidate for the application of AI across multiple disciplines, from diagnosis and pathology to drug discovery and epidemiology. At the same time, the sensitivity of medical data raises fundamental questions around privacy and security. This juxtaposition makes healthcare one of AI’s most exciting frontiers.

One of AI’s biggest opportunities in medicine is ‘predictive care guidance’ which allows both patient and healthcare providers to leverage technology and make better decisions when diagnosing patients.

For all this noise, the most promising aspect of AI in healthcare is how it empowers the human element and rekindles the crucial relationship between caregivers, healthcare professionals, payers, and patients. At a time when resources are stretched, these relationships are difficult to maintain, this is where AI jumps in saving time for caregivers and also saving the relation.

The future is bright, and if we can continue to develop creative solutions to improve the care of patients, in collaboration with deep learning algorithms, then we’ll again have succeeded in advancing medicine into a new  healthcare revolution, more evolved and sophisticated, that will continue to benefit both clinicians and patients.