Here’s How AI Is Fulfilling Priorities Of HealthCare Industry

Here’s How AI Is Fulfilling Priorities Of HealthCare Industry

Interest in artificial intelligence (AI) is exploding, Accenture forecasted that AI in health care will grow to $6.6 billion within just a few short years, at a 40% annual compounded growth rate. Accenture also believes that this technology will enable an opportunity for $150 billion in industry savings. Thus, is this hype justified? The brief answer is yes, but it instills a deeper question: Just how can we weed out the hype and then determine precisely what would be the very best role for AI so we create the rest of 2018 a year for positive change and not tumultuous chaos?

AI can fortify a doctor's thought process and the way he or she reasons out an issue. It could find hidden health markers that medical professionals don't observe manually. It may examine both structured and unstructured information -- the results and expertise of professionals from throughout the healthcare ecosystem -- to spot trends and forecast possible future health problems. However, does this imply that completely automated procedures will replace doctors?

It's a very simple fact that calculations are replacing some medical activities. However, clinicians still have something a mathematical construct does not: expertise, intuition and individual insight. It's the wisdom of doctors that is irreplaceable at the continuum of treatment. The practice of health research, new discoveries about present conditions and fresh lessons learned through the course of care that can be fed into those algorithms to make them better and more accurate. With these advanced algorithms managing routine care practices with enhancing efficiency, doctors are freed up to concentrate on more complex problems.

Where Does AI Currently Stand?

Advances in clinical analytics and machine learning have the capability to drive medical discovery at a speed never seen before, however, we now lack the capacity to efficiently place resulting breakthroughs at the hands of clinicians. For example, in a project we're working in cooperation with Partners Medical, we're building algorithms which may target patients that could benefit from a specific treatment to enhance their outcomes. Through an open-source platform, this initiative must increase knowledge transfer between suppliers and contribute to the evolution of clinical, decision-support applications.

AI can be used today at the University of Iowa Healthcare to find diabetic retinopathy in adults diagnosed with diabetes that hadn't previously received a diagnosis of diabetic retinopathy. The algorithms allow healthcare providers who are not typically involved in eye care to test for diabetic retinopathy during regular office visits.

Interest Is Building

Reaction Data published a current report (registration required) that polled radiology professionals to get their opinion on the level of AI adoption, hype versus reality and applications and sellers are somewhat more popular. A vast majority of the they polled, including supervisors of radiology, imaging directors, radiology managers as well as image archiving and communication system (PACS) administrators as well as workers at imaging centers think that AI is"significant or extremely significant," while only 16 percent aren't yet sold on the potential impact of machine learning. A vast majority of hospital radiology departments and imaging centers said they intend to implement some form of machine learning technologies within the following two decades. But there's an interesting caveat: Currently, it is hospitals, maybe not imaging facilities, which are utilizing AI today. Additionally, when Reaction Data queried imaging professionals which aren't embracing AI about the character of their worries, 46 percent were still unsure about its usefulness.

An AI Shift Is Occurring

Regardless of any present uncertainty, the potential of AI is challenging to dismiss. To know why it is helpful to look at the influence algorithms have experienced in other industries to get a perspective. At omni-channel retail, an algorithm can allow a store link to interact with customers right in front of these, as well as those sitting in their living rooms. Now, consider the reach and effect of one clinician that can touch the lives of so many more patients. You can basically consider AI's role in clinical care at two degrees: standardized deployment of clinical knowledge, and AI as an employee to redistribute work jobs across the entire health care system.

From wearable apparatus collecting patient health information to predictive analytics clarifying likely patient results, AI is a choice engine which will exponentially increase how effective and efficient healthcare can get into the lives of people in stronger ways. However, it will never be only a dumb robot delivering stiff, programmatic care. The real possibility of AI is far beyond these limited science fiction-based thinking and is already yielding significant results in 2018.