Affidavit: Healthcare and the Law - The Evolving Emergence of Artificial Intelligence in Healthcare

Contributor: Neville M. Bilimoria
To learn more about Neville, click here.

 

0b49f38a-a003-47ae-9464-96c43c66a64c.jpgI am always intrigued by new ventures and technologies in healthcare.  Recently, the newest area has been medical marijuana, and it keeps health lawyers like me busy, tackling new frontiers as the healthcare industry strives to improve healthcare in all facets.  But recently a new technology is becoming more talked about, stemming from a true hot button in healthcare technology:  Big Data.  For years “Big Data” has loomed large as the basis or cornerstone for fascinating healthcare technologies in the healthcare sector.  “Big Data” is essentially the conglomeration of data points gathered to assess patients and their outcomes, resulting in an overall improvement in the quality of care – plus, at less cost.  

For years, this has been the holy grail of Big Data healthcare technology:  gather as much data as you can, more than you can ever imagine about a patient and their treatment, and you will have the ability to analyze and perform better outcomes for the patient and better healthcare overall at less cost.  But now comes the next logical evolution of Big Data in healthcare and the next frontier in healthcare:  artificial intelligence (“AI”).  And now, the healthcare sector is seeing more and more healthcare technology transactions dealing with healthcare AI.

The utilization of healthcare technology and devices to gather Big Data has been around for many years now.  As the push towards wellness becomes more evident in U.S. healthcare, the natural progression was to utilize technology and Big Data, perhaps interspersed with telemedicine, to improve it.  However, now there is more and more fervor over the inevitable next step with Big Data: AI.  

Yes, AI is no longer something you see in the movies, but a real world application, especially in the area of healthcare, that is a driving force in private equity health transactions and the proliferation of healthcare private equity dollars.  And what about those movies? Terminator, iRobot, Ex Machina and countless others depicting a futuristic society in which artificial intelligence runs awry, with computers taking over human civilization.  Far-fetched?  Stephen Hawking, Elon Musk, and Bill Gates don’t think so.  In fact, Stephen Hawking stated that “Success in creating [artificial intelligence] would be the biggest event in human history.  Unfortunately, it might also be the last, unless we learn how to avoid the risks.”  Elon Musk calls AI “our greatest existential threat,” and Bill Gates warned the present beneficial effects of AI could be superseded decades from now when it will threaten human jobs and pose dangerous threats to human civilization.

But we are a society who is caught in the now.  Healthcare is highly susceptible to technology and results, because technology leads to the ultimate goal of healthcare: better quality outcomes for patients.  Healthcare and private equity firms are always looking for more and better technology to improve overall healthcare.  And AI is leading the way into providing an avenue for breakthrough care and outcomes.  

But what do we mean when we say AI in healthcare?  I think there are a lot of misconceptions, and many folks misunderstand true AI in healthcare.  True AI is something more than a computer reading thousands of x-rays or scans and then categorizing anomalies on scans in one pile vs. another:  yes or no cancer, for example.  That computer method is more akin to “statistical categorization.”  Statistical categorization is nothing more than data in, data out.  Statistical categorization can then lead to the next iteration of Big Data, predictive analytics (taking the statistical categorization and running that information through another algorithm to predict outcomes or forecast the future).  But true AI is something more than statistical categorization or mere predictive analytics.  It is affirmative machine learning that allows the machine to develop its own algorithms based on data, improve those algorithms on its own, and deliver output in the form of real-time diagnosis and treatment.  

Take, for example, the world’s most famous AI machine or robot:  Watson, IBM’s AI machine that won the game show Jeopardy 5 years ago, beating two Jeopardy champions.  Not impressed, since Watson has the entire Library of Congress at its disposal and can consume 1 million books a second?  Well what about Watson attending medical school?

On a recent 60 Minutes segment on “Artificial Intelligence” that aired on June 25, 2017, CBS reported that Watson attended medical school at the University of North Carolina at Chapel Hill and learned to analyze scans and images of cancer patients to detect anomalies and cancer.  But Watson was also learning.  Watson participated in cancer tumor boards with physicians.  In a remarkable 30% of patients, Watson offered better diagnosis and treatment than the tumor board of physicians, mainly due to Watson’s uncanny ability to recognize additional published literature and studies in real-time that the physicians simply could not encompass in their analysis.  Watson even surprised the most skeptical physicians at the medical school.  

So how can we control AI and bring it to the marketplace in healthcare?  First, AI has to gain the support of skeptical physicians.  That skepticism will only be lifted through real world utilization and experience with AI and patients, and physician trust in the machine learning algorithms that are at the heart of AI.  Radiologists are already using AI to assist them in clinical decision-making according to a recent article in Modern Healthcare, “Artificial Intelligence Takes on Medical Imaging,” July 10, 2017, with superior results.  And hospitals are enjoying the consistency of AI technology and improved reliability in medical imaging.  

Second, the healthcare marketplace must decide how to regulate AI. For example, do AI machines or robots have to obtain a medical license in each state to “practice medicine” and treat patients?  That might be a far-fetched idea for consideration down the road, but for now the Food and Drug Administration (“FDA”) is focused on smart apps and wearables that do more than just spit out data.  The FDA is concerned about AI devices that diagnose cancer or predict heart attacks, for example.  In fact, in May 2017, it has been reported that the FDA assembled a team to oversee the current AI revolution in healthcare, headed by Associate Center Director for Digital Health, Bakul Patel.  But the FDA would be a cumbersome agency to regulate AI, as any software changes for a medical device, like AI, would have to be continuously reported and approved by the FDA.  But with AI changing its “software” data almost instantly, second by second, it is not feasible that FDA regulations will be able to keep up with AI, at least not in the current state of FDA’s rules.  

But think what you will about AI or its potential challenges and even dangers; AI is here to stay.  Healthcare will continue to embrace AI one way or another.  Why? Just look at AI’s effect on patient outcomes, outcomes that are surpassing the best teams of doctors, outcomes that can save patient lives, and, yes, deliver better healthcare than humans.  As we know in dealing with healthcare transactions, there will always be an appetite for new and better healthcare with improved outcomes, and AI is the next logical evolution.  

 

Contact Neville at:
nmbilimoria@duanemorris.com

 

Disclaimer: This article is prepared and published for informational purposes only and should not be construed as legal advice. The views expressed in this article are those of the author and do not necessarily reflect the views of the author’s law firm or its individual partners.