As the demand for Healthcare rises we know there is an urgent need for greater efficiency with accuracy to deliver a better quality of care, see my previous post on the topic. This is certainly true in countries with a large demographic that require specific problems to be solved, like lung cancer cases in China where the recent economic growth has made X-rays or CT exams more accessible. To expedite the diagnosis many local solutions are coming up in collaboration with global technology companies that can leverage AI.
Despite the buzz in startup circles about AI in Healthcare there are many challenges ahead including regulatory mechanisms which are difficult to keep pace with innovation. Yet funding is rising to “more than a billion dollars in 2017 per IDC” and driving significant results in healthcare. Clinicians see improving results from AI across the diagnosis and treatment spectrum from detecting or analyzing diseases like cancer with improved results to predicting readmission rates or prioritizing patients.
These successes are in turn driving technology which could accelerate at a faster pace. As a result we see collaboration and development of AI focused chips that improve image processing, “In fact, AI-enabled medical imaging and diagnosis is projected to generate more than 40% growth with expectations to surpass $2.5 billion by 2024”. The momentum is here to stay with real world applications coming from big names ranging from medical device manufacturers to general technology creators working with industry associations like, “The American College of Radiology (ACR) recently launched a new Data Science Institute aimed at leveraging AI-based technologies in radiology”. To me this means AI has reached an inflection point now which has helped move AI from hype to reality and will be an interesting space to watch how it disrupts Healthcare products and services in days to come.
Rad@Sami 