Revisited: FDA's AI Medical Device Approvals

About a year ago, I dug into FDA’s newly-released list of its AI medical device approvals. In that post, I noted some surprising findings: AI approvals were (1) becoming more concentrated, with a small group of companies winning a large percentage of the approvals, and (2) most approvals were going to established medical device titans, like Siemens and GE, rather than to startups. Both findings showing that AI in medicine was providing sustaining innovation for established companies more than disrupting the established ecosystem.

A few weeks ago, FDA provided new data:

October 19, 2023 update: 171 Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices were added to the list below. Of those newly added to the list, 155 are devices with final decision dates between August 1, 2022, and July 30, 2023, and 16 are devices from prior periods identified through a refinement of methods used to generate this list. 

With this release, I wanted to see if my earlier findings still held, how fast innovation was accelerating, how quickly were more companies getting involved — and what other surprises lay within the data.

Anti-exponential: medical AI innovation is not accelerating

Scoring points: approvals by year

Probably most interest of all findings is that AI innovation in medicine — at least by the measure of AI device approvals — isn’t accelerating at all! The graph below shows good, steady growth in approvals per year (an average 26% increase per year over the last 4 complete years) but not acceleration:

Of course the number of approvals depends on at least two variables: how many applications are submitted, and how many are approved. It’s possible that the rate of invention (i.e. the creation of new algorithms and tools) is accelerating but that the FDA approval process is the rate limiter.

Joining the team: new companies per year

In terms of broadening the group of innovators, the list this year includes 19 entities that previously had not appeared, including stalwarts of diagnostics like Beckman Coulter and Biomerieux:

• AgaMatrix Inc.

• AlgoMedica

• Appian Medical Inc.

• Beckman Coulter, Inc.

• Biomerieux, Inc.

• Bruker Daltonics, Inc.

• ClearView Diagnostics Inc.

• Cydar Ltd.

• Gauss Surgical Inc.

• IRIS Intelligent Retinal Imaging Systems, LLC

• LabStyle Innovations Ltd.

• Matakina Technology Ltd.

• Monarch Medical Technologies

• Pathwork Diagnostics Inc.

• QView Medical, Inc.

• Renalytix AI, Inc.

• Stratoscientific, Inc.

• Tyto Care Ltd.

• Zepmed, LLC

Liked approvals per year, this is an increase but not an acceleration. As shown in this graph:

So neither the number of approvals nor the number of companies winning those approvals is accelerating.

Less lonely at the top

Last year I noticed that approvals were becoming more concentrated — with only 18% of companies on the list gaining more than half of total approvals (this became 20% with revision of the list by the FDA; see notes at bottom). So far in 2023, you’ve got to include 30% of total companies to get to 50% of total approvals — so approvals are going to a broader group. Hard to say from the data whether this is a trend or just variation year to year, and I’ll plan to revisit when next year’s data comes out.

Your grandfather’s medical AI?

Age of the players

I was surprised last year to find that the average age of the top 6 companies in the approvals list (including all years) was 89 years!

The most surprising point hidden in the dataset was that a lot of the action in medical AI isn’t coming from Big Tech companies known for AI like Apple or Amazon or Google/Verily but from much much older Big Med Tech industrial companies, with GE (founded 1892) and Siemens (founded 1847) taking the number 1 and 2 spots, respectively.

That has changed a bit. The top 12 companies for 2022-23 have an average age of 58 years - so we can count that as a major shift towards new players like Annalise-AI (4 years old), Viz.ai (7 years old), Hyperfine (9 years old), and Aidoc (7 years old).

This is also reflected in the continued drop in the median age of companies receiving at least one approval, as shown in the graph below.

The bump in average age for 2023? That’s in part from the addition to the list of Cedars-Sinai Medical Center (founded 1902) and MD Anderson Cancer Center (founded 1941). Will such venerable institutions continue to move onto the list? Given the accumulated medical knowledge there, let’s hope so.

The graph above just looks at the age of companies “in the game,” with at least one approval. But that means that for 2022, for example, it counts GE, with 17 approvals, the same as United Imaging — with just one.

The graph below gives us more insight into that game, showing what percentage of players are “older” (>= 20 years) versus younger.

Points scored by young vs old

It’s also important to know whether older vs younger players are racking up points (i.e. approvals). And here we see that despite the influx of younger companies the older companies are more than holding their own. In fact, as noted last year, the percentage of approvals going to older companies is steady or possibly increasing.

Big tech MIA

Note that although older companies are holding their own, those companies mostly aren’t Big Tech. Since 2017, Apple’s only gotten 2 approvals, Google’s Verily 2, Microsoft 1, and Amazon 0. That’s a total of 5 out of 531 approvals: less than 1%.

What’s it all mean

At least on the medical side, as measured by FDA AI device approvals, AI innovation is chugging along — but not accelerating. This comes as a bit of a useful corrective to the relentless AI hype. It also raises the question as to whether the rate-limiting factor here is the FDA approval process itself (not hard to believe considering the slow pace of drug approvals), or something intrinsic to the market.

More companies are entering the space and getting approvals. Most of these are younger, but the entrance of MD Anderson and Cedars-Sinai stands as an example for the well-established hospital systems; an example which I hope will spur more participation from the healthcare establishment not just in adopting AI innovation but in helping to create it.

The percentage of approvals going to the older companies is, if anything, increasing. This is classic sustaining innovation in the medical device space, as companies like GE add AI components to improve their existing product line. It remains to be seen who in the space will start to significantly outpace the innovation at those older companies and seriously disrupt the space.

Finally, despite great stated and demonstrated interest in health, Big Tech is still not a major presence on FDA’s list. Given the health moves made by Apple, Google, Amazon, Microsoft and others, this is a little surprising — especially on the Apple side, given their focus on device innovation. Will this change, whether through in-house innovation or acquisition? Time will tell.

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Notes

1. I’m definitely not an FDA expert, just a doctor and tech guy trying to figure out what’s going on in the world. If you’ve got special insight into any of this, or corrections to the above, please let me know.

2. The FDA’s AI list appears to be incomplete, leaving out at least 4 de novo approvals utilizing AI that I was able to find by cross-checking other FDA references: one from 23andMe, one from Apple, one from Renalytix AI, and one from Viz.ai. Those 4 are in FDA’s searchable approvals database, but not in their AI list. I’ve included those four in the analyses above.

3. The FDA’S list includes this note: “Of those newly added to the list, 155 are devices with final decision dates between August 1, 2022, and July 30, 2023, and 16 are devices from prior periods identified through a refinement of methods used to generate this list.”

4. I was unable to find out when these two companies were founded, and so they’ve been left out of age calculations. If you know, please share!

   • Yukun (Beijing) Technology Co., Ltd, filer of item K213986 for CerebralGo Plus

   • AssembleCircle Corp., filer of item K220903 for WebCeph

A Closer Look at FDA's AI Medical Device Approvals (2022)

On October 5th, 2022, the FDA added 178 new devices to its list of (approved) “Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices.” The full list is conveniently available at that page for download as an Excel file, and I decided to take advantage of this to dig a little deeper into the data. Mostly I was interested in the pace of FDA approvals — which of course reflects both the number of applications and the speed of the FDA process. What I found was interesting, and not exactly what I expected.

Picking up the pace of approvals

Not too surprisingly considering the hype around AI — and its potential —  we can see that the pace of FDA approvals has picked up substantially in the last 10 years. While 2022 is not yet complete as of this writing, if it continues at the current pace it should just exceed 2021:

FDA AI-Powered Medical Device Approvals by Year

Lonely at the top

With a bit more digging, we can also see that the number of companies receiving at least one approval is on the upswing, as well:

Number of companies receiving at least one FDA approval by year

Despite the increase in the number of companies playing in the space, success in FDA medical AI approvals also seems to be becoming more concentrated.

For example, in 2022 so far, just 10 of the 55 companies winning at least one approval (18%) were responsible for more than half of total approvals. Five years ago, that percentage was 55%:

Percent of Companies Winning Approval Each Year Getting 50% of Approvals

Your grandfather’s medical AI?

The most surprising point hidden in the dataset was that a lot of the action in medical AI isn’t coming from Big Tech companies known for AI like Apple or Amazon or Google/Verily but from much much older Big Med Tech industrial companies, with GE (founded 1892) and Siemens (founded 1847) taking the number 1 and 2 spots, respectively.

[Note that I’ve counted related company divisions as a single unit. For example: GE Medical Systems, GE Healthcare Japan, and GE Healthcare are just counted as “GE”.]

Here are the companies taking the top 5 spots for “number of approvals in 2022 so far,” with number of approvals in parentheses (and note that Philips and Canon tied for 4th place with 4 approvals):

Top 6 Companies for FDA Al Approvals in 2022 (so far)

The average age of these top six companies is 89 years old! Leaving out Hyperfine and Aidoc, that figure rises to 130. Not exactly startups.

Nonetheless, the oldsters are being crowded out gradually as the median age of companies getting approvals goes down:

Mean and mean age of companies receiving FDA approval

Sustaining innovation rules medical AI

So we’ve established that some pretty ancient companies are among the dominant players in medical AI. But is the tide shifting towards the startups (i.e. towards disruption)?

I’ll define an “older” company as >= 20 years old.

Certainly we can see that the percentage of companies winning at least one approval each year that are older is decreasing:

Percentage of companies winning at least one approval that are >=20 years old vs younger

This may reflect the increasing simplicity and economy of AI tools available, and the increasing number of coders who understand how to use them: the cost in time and money of playing in the AI space is going down, and that means younger and smaller companies can join the game.

At the same time, older companies gain the benefit of those decreasing costs, too, and they have more resources with which to deploy them. Here’s a chart showing the percentage of approvals going to older vs younger companies:

Percentage of FDA approvals going to companies >= 20 years old vs younger

What the last two graphs shows us is that while younger companies are increasingly in the game, the older companies (with presumably greater resources) are more than holding their own in terms of productivity. They make up a smaller percentage of companies winning at least one approval each year, but are taking an increasing share of those approvals.

So using FDA approvals as the metric, it doesn’t look like there’s much disruptive innovation going on in AI-powered med tech: we don’t see established medical device makers like GE being pushed aside (at least not yet). Instead we see:

1. increasing numbers of new companies entering the space and winning FDA approvals

2. continued dominance by established med tech players as they add AI on to their existing products to improve performance and appeal to their best existing customers.

In other words, classic sustaining innovation.

Does this mean that AI isn’t causing disruption related to healthcare? No, not at all. But, as I’ve explained in previous articles on disruption within healthcare and disruption to healthcare, it shows that the same technology (AI) can be used to

1. sustain current healthcare practices (e.g. GE and Siemens and Canon, along with some youngsters, making hospital-based imaging cheaper, more useful, and more accurate)

2. enable players outside traditional healthcare to shift low-hanging fruit to another part of the healthcare system (e.g. Walmart Health)

3. allow companies completely outside healthcare to shift other low-hanging fruit completely outside healthcare (e.g. ResApp)

Thoughts about the above? Inside knowledge you’d like to share? Feel free to contact me privately.

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Further Reading

• If you’re interested in learning more about the FDA approval process for AI medical devices, I strongly recommend “How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals” by Eric Wu et al in Nature Medicine (April 2021).

• A good, but now somewhat out-of-date discussion of the types of AI medical devices, can be found in “The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database” by Stan Benjamens et al in NPJ Digital Medicine (September 2020).

• Nice discussion of current FDA rules for software-as-a-medical-device (SaaMD) at Emerge (June 2022).