AI and Predictive Analytics are Transforming Healthcare – Here’s How

Smitha Radhakrishna

Artificial Intelligence has slowly yet surely pervaded into our daily lives; we delegate mundane, thoughtless tasks to automated systems, attempting to purge some of the busy work and free up physical time and mental space for the proverbial Jack to either be more productive at Work, or indulge a little more in Play. Today, we resort to “Alexa” or other smart assistants on our personal devices to help check off our shopping lists, update reminders, make home security safer, you name it. AI algorithms are getting increasingly sophisticated and more ubiquitous, to do what humans can do today – except faster, cheaper and better. From programming to cleaning floors with smart vacuums to a not-so-distant future of self-driving cars chauffeuring us to personalized co-working spaces, AI is already in most places we look and is here to stay!

Not-so-surprisingly, AI has found its way into healthcare and is making noticeable impacts in several areas.

  • When it comes to general health and wellness, AI in combination with IoT (Internet of Things) empowers us, the consumers, to take charge of our health and pay attention to our vitals, fitness and diet. Smart medical devices and/or wearables now have the ability to continuously track health conditions.

  • Google’s DeepMind Health combines machine learning systems and neuroscience, and aims to build algorithms to imitate the human brain.

  • IBM’s Watson for Health, a form of AI, can store far more health and treatment information and review it exponentially faster than any human ever can. In 2016, a partnership between Barrow Neurological Institute and IBM Watson made a groundbreaking discovery that was possible by Watson’s review of thousands of pieces of research to identify new genes linked to ALS (Amyotrophic Lateral Sclerosis).

  • The Guardian reports that in 2018, researchers from US, Germany and France trained a deep learning AI platform to identify skin cancer with greater accuracy than humans, the result was that the AI platform found 95% of the melanomas, outperforming most dermatologists at 86.6%.

The benefits of integrating AI into our healthcare ecosystem and devices are numerous, including automation of tasks, analyses of patient data and delivery of better healthcare. Analyses of massive amounts of data lead to patient insights and predictive analytics, facilitating healthcare interventions to occur at the right times and avoiding expensive medical situations for patients, payers and providers. Simply put, AI and predictive analytics create better outcomes for the healthcare community as a whole.

We would be remiss if we didn’t address some of the challenges that AI faces in terms of adoption and widespread use in healthcare – The top concern raised by far is the handling and privacy of patient data. Even though tech companies, device manufacturers and other healthcare entities are increasingly investing in state-of-the-art technology to address this concern, protecting patient data continues to remain a major concern.

Another big challenge is to form meaningful and marketable insights – While the technology exists to capture and analyze large amounts of data, the inferences themselves may not make sense without the right format, context and key metrics for the right audience to take actionable steps.

Finally, there is a notion that AI is bound to replace care providers, when in reality, AI Technology and health professionals work hand-in-hand to create the best possible patient outcomes. This in large part is due to the fact that Predictive Analytics and the use of AI in healthcare are relatively new and are yet to gain the trust of professionals in the medical community.

Despite these potential pitfalls, public and private sector investment in healthcare AI has been remarkable. According to PwC, more than a third of provider executives were investing in AI, Machine Learning and predictive analytics in 2018. Some estimates predict total healthcare AI investment will reach $6.6 billion by 2021. In a Forbes article, Accenture predicts AI can help address 20% of unmet clinical need, and top AI applications could result in annual savings of $150 billion across the healthcare industry by 2026. With that kind of potential to pare back healthcare expenditures across the board and create better outcomes for every participant in the healthcare community, the future of AI in healthcare is certainly looking bright!

  • LinkedIn Social Icon
  • Twitter

© 2020 by Milliman HealthIO 

All Rights Reserved