- 29th August 2018
- Posted by: Manolis
- Category: Blockchain
Interest in artificial intelligence (AI) is exploding, with Accenture forecastingthat AI in health care will grow to $6.6 billion in a few short years, at a 40% annual compounded growth rate. Accenture also believes this technology will enable an opportunity for $150 billion in industry savings. So, is this hype justified? The short answer is yes, but it belies a much deeper question: How do we weed out the hype and determine exactly what is the most effective role for AI so that we make the rest of 2018 a year for positive change and not disruptive chaos?
AI can augment a physician’s thought process and how he or she reasons out a problem. It can uncover hidden health markers that medical professionals do not observe manually. It can look at both structured and unstructured data — the results and experience of practitioners from across the health care ecosystem — to identify trends and predict potential future health issues. But does this mean that entirely automated processes are going to replace physicians?
It is a simple fact that algorithms are replacing some clinical tasks. But clinicians still have something that a mathematical construct does not: expertise, intuition and human insight. It’s the wisdom of physicians that is irreplaceable in the continuum of care. The cycle of medical research, new discoveries on existing conditions and new lessons learned during the course of care that can be fed back into these algorithms to make them better and more accurate. With these improved algorithms handling mundane care practices with increasing efficiency, physicians are freed up to focus on more complex issues.
What’s Happening With AI Today?
Advances in clinical analytics and machine learning have the potential to drive medical discovery at a pace never seen before, but we currently lack the ability to efficiently place resulting breakthroughs in the hands of clinicians. For example, in a project we’re working on in collaboration with Partners Medical, we are building algorithms that can target patients that could benefit from a specific treatment to improve their outcomes. Through an open-source platform, this initiative should increase knowledge transfer between providers and contribute to the development of clinical, decision-support applications.
AI is also being used today at the University of Iowa Healthcare to detect diabetic retinopathy in adults diagnosed with diabetes who had not previously received a diagnosis of diabetic retinopathy. The algorithms allow health care providers who are not normally involved in eye care to test for diabetic retinopathy during routine office visits.
Enthusiasm Is Building
Reaction Data released a recent report (registration required) that polled radiology professionals to get their opinion on the level of AI adoption, hype versus reality and which applications and vendors are more popular. A majority of those they polled, including directors of radiology, imaging directors, radiology managers and even picture archiving and communication system (PACS) administrators as well as employees at imaging centers believe that AI is “important or extremely important,” while only 16% are not yet sold on the potential impact of machine learning. A majority of hospital radiology departments and imaging centers stated that they plan to implement some form of machine learning technology within the next two years. But there is an interesting caveat: Currently, it’s hospitals, not imaging centers, that are utilizing AI today. Also, when Reaction Data queried imaging professionals that are not embracing AI on the nature of their concerns, 46% were still unsure of its usefulness.
An AI Transformation Is Happening
Regardless of any current uncertainty, the potential of AI is difficult to ignore. To understand why, it’s helpful to look at the impact algorithms have had on other industries to gain a perspective. In omnichannel retail, an algorithm can enable a store associate to engage with customers right in front of them, as well as those sitting in their living rooms. Now, think of the reach and impact of a single clinician that can now touch the lives of so many more patients. You can basically think of AI’s role in clinical care at two levels: standardized deployment of clinical knowledge, and AI as an implement to redistribute work tasks across the entire health care system.
From wearable devices collecting patient health data to predictive analytics clarifying probable patient outcomes, AI is a decision engine that will exponentially increase how effective and efficient healthcare can touch the lives of more people in stronger ways. But it will never be just a mindless robot delivering rigid, programmatic care. The real potential of AI is far beyond such limited science fiction-based thinking and is already yielding significant results in 2018.