By some estimates, the U.S. healthcare system now generates over one trillion gigabytes of data annually. And with this proliferation of information, inexpensive, large-scale computing power has found itself meeting this data at the edge of many healthcare enterprise networks.
This newly developed collaboration, if you will, is now creating an artificial intelligence (AI) and machine learning (ML) tidal wave in the health and healthcare industries. The plan for this collaboration is for them to uncover and deliver the insights required to accelerate the discovery of new therapeutics, while making the effectiveness and delivery of existing therapeutics even more effective.
Artificial intelligence and machine learning are now being utilized in more than therapeutic development and healthcare delivery in a variety of ways. Pharma and medical-technology manufacturers are using machine learning to drive development, guide clinical-trial design, and determine phases of life-cycle management. This is an important area in which AI and ML can make a compelling contribution, as they can aid in monitoring and compiling information that will compress the timelines in understanding variations in responses to treatment. Additional uses of machine learning include informing diagnosis, developing treatment algorithms, and employing new digitally-based therapeutics.
Health and healthcare are different from other industry sectors
Many industries have experienced almost instant success when applying machine learning. The fact is though, other industries’ success does not readily translate when attempting to scale machine learning in the healthcare vertical. The reason for this is that the understanding of complex diseases and their corresponding treatments are totally different from other static business applications in which machine learning has been used. The good news is this is where artificial intelligence can help.
All we have to do is look at the current pandemic. Despite notable gains in the understanding of COVID-19 since early January, there is much we still don’t know about the virus. Like many other diseases, the coronavirus’ conditions involve ever-evolving multifactorial processes. In addition, as with any virus, it mutates. This “moving target” makes machine learning so much more difficult since the data is dynamic in nature. Also, the drug, device, and healthcare companies have examples of interventions that have worked mechanistically, improved conditions temporarily, but ultimately failed in final evaluation. Hence, there are risks associated with relying unconditionally on machine learning alone in healthcare, absent the help of artificial intelligence.
AI and ML challenges in healthcare present opportunities to connected systems providers
While health and healthcare providers face distinct challenges with machine learning and artificial intelligence, providers of AI and ML systems for the industry should look upon these challenges as an enormous opportunity. By unlocking machine learning’s full potential with AI, providers of connected healthcare solutions that recognize and address the issues raised above can prosper greatly. Smart healthcare system providers that can demonstrate how pharmaceutical and healthcare companies can use artificial intelligence and machine learning to exploit its full promise in driving innovation to improve health will experience great success themselves.
Given a much more challenging analytical health and healthcare environment, providers of connected AI and ML systems for healthcare need to be more thoughtful about how they employ these solutions throughout the industry. Improving the health of the population requires new and modernized care models. As such, the technologies that address the success factors that drive health, enable early accurate diagnosis, and monitor response to treatment are key. Knowing the hurdles that medical researchers, practitioners, and providers face when trying to put these advanced technologies to work, is critically important for healthcare AI and ML systems builders to recognize – and think about in advance for their healthcare customers when designing and developing solutions for their use.