Artificial Intelligence in Medical Diagnostics
Artificial intelligence has already begun to drive major innovation in medical diagnostics, and regulatory authorities are trying to catch up. Read this article to learn about exactly how AI is improving medical diagnostics and the pressure that regulators are under to preserve the pace of medical innovation.
When you think of artificial intelligence, do any of these come to mind?
Enabling Autopilot and letting your Tesla drive you through rush hour traffic on the 405
Watching IBM’s Watson defeating Ken Jennings on Jeopardy
The cell phone surveillance system that Morgan Freeman created for Batman in The Dark Knight
Regardless of what you know or think about artificial intelligence and the associated hype, rest assured that it is real and has already made fundamental changes in the world. One of the areas where AI is making a major impact is in healthcare, and specifically in medical diagnostics.
Medical diagnostics work by measuring some aspect of your physiology, comparing it to what “normal” should be, and determining whether or not your result is healthy.
In the current paradigm of product development for medical diagnostics, developers have to “lock in” key characteristics of the diagnostic test early in the development process. This includes deciding
How much material (blood, tissue, etc.) is needed to perform a test?
How accurate will the results be?
What physiological conditions or diseases can be determined based on the results?
How frequently does the diagnostic device need to be checked or calibrated?
Once these parameters are determined, the developer has to reach a few major milestones before the product can improve patient lives
Complete extensive testing to prove the device can meet the requirements
Analyze all the results, summarize the data and submit the information to the appropriate regulatory authority
Receive approval to market the diagnostic device as described in the submission
This process typically takes about two years.
As the device is used in the real world, data is generated that can pinpoint ways to improve the performance of the test. This could include how to perform the test with less tissue, how to improve the test accuracy, or expanding the types of diseases that can be measured. This data is generated by customers who are using the existing diagnostic test, internal testing performed by the developer, and sometimes through scientific or technical advancements made by others in the field. Once the developer has gathered enough information to justify an improvement, the product development process is started once more. This improvement cycle occurs once a year or so.
If you add up the time it takes to develop and launch a medical diagnostic, with the time it takes to gather enough data to start an improvement initiative, you’ll realize that if you decided to start a medical diagnostic company in September 2019, it would be late 2024 before you’d released a second version!
This time frame makes sense for “traditional diagnostics”, where the majority of the analytical work is performed by consuming some amount of human tissue, purifying it, and observing how it reacts with chemicals which were precisely dispensed within a large test system.
In this paradigm
The tissue being tested has a limited shelf life
It is consumed during the analysis and must therefore be replaced
Performing one analysis requires a non-trivial amount of consumable material such as specimen containers or test chemicals
The diagnostic value of the test is fixed and pre-determined based on the understanding of the chemical interactions at the time that the test was created
Compare this paradigm to that of next generation of diagnostic devices, where the majority of the analytical work is performed by computer code which analyzes information about the tissue and observes how it compares to a given dataset.
Once information is extracted, it can be stored forever without degradation
The information can be re-used for an infinite number of tests
Performing a test has negligible cost or required material
As new information improves any aspect of the computer code, the diagnostic value can increase
This last distinction is where artificial intelligence can drive exponential improvements. The developer of a next generation diagnostic device (or a traditional diagnostic with some next generation capabilities) can imbue the product with the ability to improve itself - independent of input from the developer. Examples of such improvements include
Improved accuracy in detection of the test
The ability to detect new disease states
The ability to analyze patients in a new demographic group
Needless to say, this self-improvement ability can drastically reduce the amount of time it takes for an improved diagnostic product to be developed. Instead of taking two years to develop a version, one year to gather data to improve the performance, then another two years to develop the improved version, artificial intelligence can allow a diagnostic test to immediately improve in real time - as soon as new information is available and validated!
However, determining performance parameters and proving the diagnostic test’s performance is only half the battle when it comes to launching a medical diagnostic. The other half is creating the regulatory submission package and achieving approval to market the device. If the regulatory steps in the product development process don’t accelerate, the overall pace of medical improvements won’t change. Fortunately, the FDA has recognized the looming imbalance between the pace of technological innovation and the pace of the regulatory process and is actively developing a new approach to support the use of artificial intelligence in medical diagnostics.
In the next article in this series, I'll explain the FDA’s proposed approach and the major roadblocks that must be addressed so the regulatory process can keep pace with the exciting potential of Artificial Intelligence in medical diagnostic innovation.
What Scott Gottlieb’s Resignation Means for Medical Device Investors
How to navigate a sudden leadership vacuum at the FDA
This Tuesday Scott Gottlieb, the US Food and Drug Administration (FDA) commissioner, announced that he is resigning at the end of April. Let’s unpack that a bit and dive specifically into what his resignation means for the medical device segment.
Gottlieb was confirmed as FDA commissioner in May 2017, immediately following a ten year stint in the private sector which included time as an investor, consultant, and board member in the biotechnology and healthcare industries. Given that background it is no surprise that Gottlieb has been viewed as generally favorable to the medical device industry, especially with regards to new product approval timelines as well as championing reform at the policy level to continue to accelerate approvals.
Industry sympathies notwithstanding, Gottlieb is most commonly associated with his pronounced anti-vaping agenda. This included restriction of flavored vaping juices as well as his recent stinging criticism of major corporations including 7-Eleven, Walgreens, and Walmart for selling vaping products to minors.
Gottlieb’s tenure as commissioner has been positive for the medical device industry, and his departure creates risk for medical device innovation in the short- and medium-term.
There are two specific areas of exposure:
Gottlieb was a proponent of “modernizing” the medical device approval pathway, specifically by revitalizing a rarely-used option known as the de novo process. This process allows manufacturers of certain high-risk devices to attain marketing approval with less documentation and clinical testing relative to the traditional Premarket Authorization process. The de novo process can therefore decrease the cost and approval time for cutting-edge devices. Although the ball is already rolling to expand the use of de novo approvals, if Gottlieb’s successor is not as supportive of the initiative it will take longer to see material upside from this policy initiative.
There has been negative public sentiment in general about medical device regulation not protecting patient safety (specifically vaginal mesh and steel orthopedic implants, as shown in popular Bleeding Edge documentary). Furthermore, During Gottlieb’s tenure the number of 510(k) clearances began to increase, reversing a slight decline from 2013–2016. These two issues may compound to create significant pressure for Gottlieb’s successor to be more conservative with regard to medical device approvals.
For investors in the medical device segment, the best way to mitigate against these potential risks is to reduce exposure to companies who rely significantly on new product approvals to drive growth and/or cash flow. This would necessarily mean allocating more capital to large, public medical device companies with stable product lines rather than early-stage companies or those driven by new product introductions.
This can be visualized in the chart above, which plots the percentage of revenue invested into R&D (as an estimate of revenue from new products versus existing ones) on the Y-axis and market cap on the X-axis. Companies in the lower right section of the scatter are large and drive revenue through existing product lines, and should therefore by minimally impacted by potential regulatory headwinds opposing new product approval.