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The FDA Designates an Eye-Diagnostic System as a “Breakthrough Device”

by Srihari Yamanoor

 

Last year, in my talk at the San Jose BIOMEDevice Conference, I had postulated that one of the key factors in the rate and level of adoption of AI based devices and systems would depend on how the FDA would act on such submissions. It is good to see that one company seems have to have proceeded with taking the FDA to task. The result is positive, and good news for companies pushing AI based systems, but also carries a tale of caution, for the road ahead for everyone involved. I will lay out the case briefly in this blog, but this is not the last time you will hear me talk about the effect of regulatory agency response to such device submissions and their long-term implications.

I just reviewed news that IDx, a company that designed what is being described as “an AI based autonomous diabetic retinopathy” detection system has been given “breakthrough” status by the FDA for their system. This provides the system with an expedited review, and potentially quick approval. This is very encouraging, not only for the organization with the retinopathy system, but also for all companies vending AI systems.

Here are a couple of key thoughts I have on this:

  1. A very promising outlook: I am not aware of how much Scott Gottlieb had, in terms of a personal hand in this decision, but I am sure his leadership had a measurable impact on the decision to expedite review. He has taken a stance of modernizing some of the approval processes, especially in the area of Digital Health. The FDA’s backing for AI based systems, is a very positive thing for companies with an AI focus on healthcare.
  2. Good for competition in the healthcare industry: Once the FDA sets AI based apps and systems on the path to commercialization, there will be no excuse for organizations that do not adopt Machine Learning and Neural Networks. Of course watch for a good spike in M&A activity in this regard.
  3. The Hype Cycle is around the corner: While companies with real ML/DL products and services will be out there, helping patients, there will also be the fakes and the wannabes that we will have to contend with. And it is quite possible that the rotten apples will ruin it for everyone. This is something to watch for.
  4. Unrealistic expectations and unintended consequences: I know this is closely related to the point about the hype cycle, but I want to make a refined point here. Even the companies with good AI based tools might push the envelope on hyping up the utility and efficiency of their products. Over-promise and under-delivery lead to angry and disappointed customers and eventually will create problems for everyone in the industry.
  5. How will CMS and private insurance companies handle reimbursement? This is a big question and can be the topic (and probably will be) of several blog posts. However, I just wanted to highlight that while AI might improve efficiency and automate diagnoses and treatments, companies have to resolve how and where the reimbursement will come from.
  6. A word of caution on regulation: While it is commendable that the FDA is moving forward with initiatives on things such as AI. However, I do not always agree they are doing a great job on things. I was irked by the craze with which the orphan applications were being reviewed by the FDA. The FDA, in the past, has gotten too cozy with the industry and Congress, with terrible consequences. Those who remember the Menaflex incident (reference below) remember how quickly the public’s trust in the FDA eroded, and how the organization took years to recover from that. Therefore, there is cause for concern here, and one hopes there will be a balancing act in making sure that the approval processes remain rigorous enough.

Conclusion: The current “breakthrough” designation and fast track review show great promise, but one hopes the FDA will balance it out with an equal dose of caution.

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References:

  1. The Press Release on IDx: https://www.eyediagnosis.net/single-post/2018/02/05/FDA-to-expedite-review-of-IDx-DR-a-breakthrough-AI-diagnostic-system?lipi=urn%3Ali%3Apage%3Ad_flagship3_feed%3B6Bf9xSfCRQSJXa4nvXjScA%3D%3D
  2. On what happened with Menaflex: https://www.fda.gov/downloads/NewsEvents/PublicHealthFocus/UCM183642.pdf
  3. Image, Courtesy, Pexels: https://www.pexels.com/photo/people-face-child-eye-32267/

Interesting AI+Health Imaging Investment and a few thoughts on where the field might be headed

by Srihari Yamanoor

 

This morning, a LinkedIn contact of mine shared an interesting MassDevice article about “Bay Labs”, a company focused on bringing Deep Learning to Cardiovascular Health Imaging (though they define themselves in broader terms as an algorithm company). It appears that Khosla Ventures (after the same much-hated Khosla from California), an existing investor (which must mean seed funding) has backed the organization with $6mn in series A funding.

What is interesting

  1. So far, what we have seen is, the really big companies, such as nVidia and G.E. working on backing up imaging with AI. They even recently announced a collaboration. This would have been a scary scenario for start-ups trying to break into the space. Even now, one should not take too much comfort. Bay Labs apparently won in an AI company contest conducted by nVidia. It clearly shows that big players are keeping an eye on the start up space, potentially looking to scoop up promising organizations.
  2. Though Bay Labs likes to talk about themselves in somewhat general terms; they state this on their website, “In order to serve the largest number of people, we aim to amplify the benefits of deep learning by providing high-performance algorithmic capabilities to assist with healthcare challenges on a global scale.”, they are at least currently focused on Deep Learning, as it relates to Imaging for Cardiovascular Health. Don’t get me wrong, I like both ends of the spectrum displayed here. They appear to have a broad vision of where they want to end up, and yet, as is important for a startup, they have a somewhat keen sense of focus.
    1. Given that, even with all the healthcare and lifestyle influences urged on by healthcare systems, governments, non-profits and others, cardiovascular diseases remain the cause behind the most deaths in the US and around the world. Thus, even within just this field, in imaging alone, the opportunities abound.
    2. Plus, cardiovascular health also happens to be one of the most expensive, and is a low hanging-fruit for startups, expecting to break into a new field and work towards financial success. The other field is probably oncology, which however is crowded with Watson and others. Still, I expect at least a handful of startups focused on various subsets of oncological diagnosis and treatment to emerge out of the woods as time progresses.
    3. It is also one of the fields where practitioners are prone to adopt cutting edge solutions.
  3. Bubbles: Bay Labs appears to be an organization founded on some sound principles. The next few should probably follow along the same path. One has to wonder though, at what point, in the not too far future  will we see organizations touting AI-this and AI-that, without any sound basis? This should be interesting to track.
  4. Algorithm Patents: The debate on whether software and algorithms can be patented, and under what conditions was never properly settled (what, ever is?). A new wave of companies touting algorithms will now come along, and generally speaking, take each other to court, giving headaches to Luddite Justices, such as in the US Supreme Court who are still hemming and hawing about allowing basic AV equipment within the courts (ironically AI has been trained to predict how individual US Supreme Court Judges would rule, with fairly good efficiency). This whole space is something to watch closely.
  5. Assets: Besides patents, there are other things to be concerned about here. Who ultimately owns the data? The results from what the Machine Learning and Deep Learning algorithms find? Are the findings marketable? If not, who pays to get this done? How do governments, healthcare systems and private organizations, all of which form the backbone of medicine share the costs and the benefits?
  6. Mergers & Acquisitions: Once the hype cycle is ridden and who owns what is also a bit more clear, comes the M&A song and dance. This also should be interesting to watch.
  7. Interoperability: Data interoperability in heatlhcare, is already a major headache. This is currently one of my core research areas. I am deeply interested in unraveling the challenges of how data should be cleaned and labeled to be used in AI applications. On the surface, it appears that all you have to do is collect the data, just transform it for various purposes – to upload to EHRs, to use in Machine Learning and so on and then get on with it. The underlying issues are much more complicated. Who collects the data? How is it blinded? Who pays for all this? How does the learning get applied? How do you glean precision medicine uses out of all this? And, many, many more…
  8. Regulatory Dilemmas: I have a series of blog posts planned on this topic, but let’s take a quick dive here. How do you get approvals for such AI systems? How do you fix problems? Should improvements, fed by data volume and algorithm changes require regulatory updates? What happens when an algorithm produces defective results? These and many others problems will need some type of resolution, and will create new problems and opportunities of their own.

Summary: In summary, I’d say, news of companies such as Bay Labs is good. However, headwinds lie ahead for them, and everyone in industry. We are looking at huge paradigm shifts and thus, not much is predictable. New issues will unfold while ones that seem dire right now, might become trivial. We are in for the ride of our lifetimes!

References:

  1. The MassDevice Article: http://www.massdevice.com/cardiovascular-ai-imaging-dev-bay-labs-raises-6m-series/?lipi=urn%3Ali%3Apage%3Ad_flagship3_feed%3BMBJbTpHMSb6f%2B2WzYDP%2BUA%3D%3D
  2. Bay Labs: https://baylabs.io/
  3. Image, Courtesy Pexels: https://www.pexels.com/photo/low-angle-view-of-spiral-staircase-against-black-background-247676/

Slides – How Artificial Intelligence is Changing Medical Devices

by Srihari Yamanoor

The main goal of this site is to air my thoughts on how new technologies and paradigms impact the practice of medicine. In relation to this, I made a presentation, just yesterday, December 6, 2017, on the topic of Artificial Intelligence, and the growing influence of this discipline on the field of medical devices.

The slides will be available to download this through the site.

I do have to caution you. I do not present a lot of text in the slides. I have written some notes on the slides, but together, they still will not be enough to understand all of what I spoke about.

However, do not worry. I will blog about each slide, concept and use-case presented on this presentation over the next few weeks and months. Subscribe for updates and stay tuned! In addition, if you feel you have a set of burning questions and need them answered as soon as possible, do feel free to contact me and I will answer them as early as possible.

The download link: https://drive.google.com/file/d/12o1DHzJ2f_jNJi8MzZFPqhOwydVoDU52/view?usp=sharing