Super-resolution* promises to be one of the most impactful medical imaging AI technologies, but only if it is safe.
Last week we saw the FDA approve the first MRI super-resolution product, from the same company that received approval for a similar PET product last year. This news seems as good a reason as any to talk about the safety concerns myself and many other people have with these systems.
Disclaimer: the majority of this piece is about medical super-resolution in general, and not about the SubtleMR system itself. That specific system is addressed directly near the end.
Zoom, enhance
Super-resolution is, quite literally, the “zoom and enhance” CSI meme in the gif at the top of this piece. You give the computer a low quality image and it turns it into a high resolution one. Pretty cool stuff, especially because it actually kind of works.
In medical imaging though, it’s better than cool. You ever wonder why an MRI costs so much and can have long wait times? Well, it is because you can only do one scan every 20-30 minutes (with some scans taking an hour or more). The capital and running costs are only spread across one to two dozen patients per day.
So what if you could get an MRI of the same quality in 5 minutes? Maybe two to five times more scans (the “getting patient ready for the scan” time becomes the bottleneck), meaning less cost and more throughput.
Medical AI testing is unsafe, and that isn’t likely to change anytime soon.
No regulator is seriously considering implementing “pharmaceutical style” clinical trials for AI prior to marketing approval, and evidence strongly suggests that pre-clinical testing of medical AI systems is not enough to ensure that they are safe to use. As discussed in a previous post, factors ranging from the laboratory effect to automation bias can contribute to substantial disconnects between pre-clinical performance of AI systems and downstream medical outcomes. As a result, we urgently need mechanisms to detect and mitigate the dangers that under-tested medical AI systems may pose in the clinic.
In a recent preprint co-authored with Jared Dunnmon from Chris Ré’s group at Stanford, we offer a new explanation for the discrepancy between pre-clinical testing and downstream outcomes: hidden stratification. Before explaining what this means, we want to set the scene by saying that this effect appears to be pervasive, underappreciated, and could lead to serious patient harm even in AI systems that have been approved by regulators.
But there is an upside here as well. Looking at the failures of pre-clinical testing through the lens of hidden stratification may offer us a way to make regulation more effective, without overturning the entire system and without dramatically increasing the compliance burden on developers.
Despite an area under the ROC curve of 1, Cassandra’s
prophesies were never believed. She neither hedged nor relied on retrospective
data – her predictions, such as the Trojan war, were prospectively validated. In
medicine, a new type of Cassandra has emerged –
one who speaks in probabilistic tongue, forked unevenly between the
probability of being right and the possibility of being wrong. One who, by conceding
that she may be categorically wrong, is technically never wrong. We call these
new Minervas “predictions.” The Owl of Minerva flies above its denominator.
Deep learning (DL) promises to transform the prediction
industry from a stepping stone for academic promotion and tenure to something
vaguely useful for clinicians at the patient’s bedside. Economists studying AI believe that AI is revolutionary,
revolutionary like the steam engine and the internet, because it better predicts.
Recently published in Nature, a sophisticated DL algorithm was able to predict acute kidney injury (AKI), continuously, in hospitalized patients by extracting data from their electronic health records (EHRs). The algorithm interrogated nearly million EHRS of patients in Veteran Affairs hospitals. As intriguing as their methodology is, it’s less interesting than their results. For every correct prediction of AKI, there were two false positives. The false alarms would have made Cassandra blush, but they’re not bad for prognostic medicine. The DL- generated ROC curve stands head and shoulders above the diagonal representing randomness.
The researchers used a technique called “ablation analysis.”
I have no idea how that works but it sounds clever. Let me make a humble
prophesy of my own – if unleashed at the bedside the AKI-specific, DL-augmented
Cassandra could unleash havoc of a scale one struggles to comprehend.
Leaving aside that the accuracy of algorithms trained
retrospectively falls in the real world – as doctors know, there’s a difference
between book knowledge and practical knowledge – the major problem is the
effect availability of information has on decision making. Prediction is
fundamentally information. Information changes us.
By ROBERT C. MILLER, JR. and MARIELLE S. GROSS, MD, MBE
This piece is part of the series “The Health Data Goldilocks Dilemma: Sharing? Privacy? Both?” which explores whether it’s possible to advance interoperability while maintaining privacy. Check out other pieces in the series here.
The problem with porridge
Today, we regularly hear stories of research teams using artificial intelligence to detect and diagnose diseases earlier with more accuracy and speed than a human would have ever dreamed of. Increasingly, we are called to contribute to these efforts by sharing our data with the teams crafting these algorithms, sometimes by healthcare organizations relying on altruistic motivations. A crop of startups have even appeared to let you monetize your data to that end. But given the sensitivity of your health data, you might be skeptical of this—doubly so when you take into account tech’s privacy track record. We have begun to recognize the flaws in our current privacy-protecting paradigm which relies on thin notions of “notice and consent” that inappropriately places the responsibility data stewardship on individuals who remain extremely limited in their ability to exercise meaningful control over their own data.
Emblematic of a broader trend, the “Health Data Goldilocks Dilemma” series calls attention to the tension and necessary tradeoffs between privacy and the goals of our modern healthcare technology systems. Not sharing our data at all would be “too cold,” but sharing freely would be “too hot.” We have been looking for policies “just right” to strike the balance between protecting individuals’ rights and interests while making it easier to learn from data to advance the rights and interests of society at large.
What if there was a way for you to allow others
to learn from your data without compromising your privacy?
To date, a major strategy for striking this balance has involved the practice of sharing and learning from deidentified data—by virtue of the belief that individuals’ only risks from sharing their data are a direct consequence of that data’s ability to identify them. However, artificial intelligence is rendering genuine deidentification obsolete, and we are increasingly recognizing a problematic lack of accountability to individuals whose deidentified data is being used for learning across various academic and commercial settings. In its present form, deidentification is little more than a sleight of hand to make us feel more comfortable about the unrestricted use of our data without truly protecting our interests. More of a wolf in sheep’s clothing, deidentification is not solving the Goldilocks dilemma.
Tech to the rescue!
Fortunately, there are a handful of exciting new technologies that may let us escape the Goldilocks Dilemma entirely by enabling us to gain the benefits of our collective data without giving up our privacy. This sounds too good to be true, so let me explain the three most revolutionary ones: zero knowledge proofs, federated learning, and blockchain technology.
On Episode 3 of HardCore Health, Jess & I start off by discussing all of the health tech companies IPOing (Livongo, Phreesia, Health Catalyst) and talk about what that means for the industry as a whole. Zoya Khan discusses the newest series on THCB called, “The Health Data Goldilocks Dilemma: Sharing? Privacy? Both?”, which follows & discuss the legislation being passed on data privacy and protection in Congress today. We also have a great interview with Paul Johnson, CEO of Lemonaid Health, an up-and-coming telehealth platform that works as a one-stop-shop for a virtual doctor’s office, a virtual pharmacy, and lab testing for patients accessing their platform. In her WTF Health segment, Jess speaks to Jen Horonjeff, Founder & CEO of Savvy Cooperative, the first patient-owned public benefit co-op that provides an online marketplace for patient insights. And last but not least, Dr. Saurabh Jha directly address AI vendors in health care, stating that their predictive tools are useless and they will not replace doctors just yet- Matthew Holt
Matthew Holt is the founder and publisher of The Health Care Blog and still writes regularly for the site.
The year is 2019 and Imaging By Machines have fulfilled their prophesy and control all Radiology Departments, making their organic predecessors obsolete.
One such lost soul tries to decide how he might reprovision the diagnostic equipment he has set up on his narrow boat on the Manchester Ship Canal, musing at the extent of the digital take over during his supper (cod of course).
What I seek to do in this short paper is not to revisit the well-trodden road of what Artificial Intelligence, deep learning, machine learning or natural language processing might be, the data-science that underpins them nor limit myself to what specific products or algorithms are currently available or pending. Instead I look to share my views on what and where in the patient journey I perceive there may be uses for “AI” in the pathway.
I’ve been talking in recent posts about how our typical methods of testing AI systems are inadequate and potentially unsafe. In particular, I’ve complainedthat all of the headline-grabbing papers so far only do controlled experiments, so we don’t how the AI systems will perform on real patients.
Today I am going to highlight a piece of work that has not received much attention, but actually went “all the way” and tested an AI system in clinical practice, assessing clinical outcomes. They did an actual clinical trial!
Big news … so why haven’t you heard about it?
The Great Wall of the West
Tragically, this paper has been mostly ignored. 89 tweets*, which when you compare it to many other papers with hundreds or thousands of tweets and news articles is pretty sad. There is an obvious reason why though; the article I will be talking about today comes from China (there are a few US co-authors too, not sure what the relative contributions were, but the study was performed in China).
China is interesting. They appear to be rapidly becoming the world leader in applied AI, including in medicine, but we rarely hear anything about what is happening there in the media. When I go to conferences and talk to people working in China, they always tell me about numerous companies applying mature AI products to patients, but in the media we mostly see headline grabbing news stories about Western research projects that are still years away from clinical practice.
This shouldn’t be unexpected. Western journalists have very little access to China**, and Chinese medical AI companies have no need to solicit Western media coverage. They already have access to a large market, expertise, data, funding, and strong support both from medical governance and from the government more broadly. They don’t need us. But for us in the West, this means that our view of medical AI is narrow, like a frog looking at the sky from the bottom of a well^.
With the application deadline for Bayer’s G4A Partnerships program coming up on Friday, I thought I’d throw out a little inspiration to would-be applicants by featuring an interview I did with one of last year’s program participants at the grand-finale Launch Event.
Not only was this a great party, but a microcosm of the G4A program experience itself: a way to meet Bayer execs en-masse, an opportunity to sell directly to key decision-makers across Bayer’s various global business units, and a chance to feed off the energy of like-minded innovators eager to see ‘big health care’ change for the better.
While the G4A program itself has changed a bit this year to be more streamlined and to allow for bespoke deal-making that may or may not involve giving up equity (my favorite new feature), startups questioning whether or not they have what it takes should take a look at some alums.
There’s a playlist with nearly two dozen interviews waiting for you here if you’re REALLY up for some procrastinating, or you can click through and just check out my chat with Joe Curcio, CEO of KinAptic. A healthtech startup taking wearables to the bleeding edge, Joe shows us a mock-up of the KinAptic ‘smart shirt’ which features their real innovation: printed ink electronics that look and feel like screenprinting ink, but work bi-directionally to both collect data from the body AND apply signals back to it. Is it AI-enabled? Did you have to ask? Listen in for a mindblowing chat about how this tech can change diagnostic analysis and treatment and completely redefine our current limitations when it comes to healthcare wearables.Once you’re inspired, don’t forget to head over to www.g4a.health and fill out your own application for this year’s partnership program.
Jessica DaMassa is the host of the WTF Health show & stars in Health in 2 Point 00 with Matthew Holt
Today, we are featuring Dr. Jesse Ehrenfeld from the American Medical Association (AMA) on THCB Spotlight. Matthew Holt interviews Dr. Ehrenfeld, Chair-elect of the AMA Board of Trustees and an anesthesiologist with the Vanderbilt University School of Medicine. The AMA has recently released their Digital Health Implementation Playbook, which is a guide to adopting digital health solutions. They also launched a new online platform called the Physician Innovation Network to help connect physicians with entrepreneurs and developers. Watch the interview to find out more about how the AMA is supporting health innovation, as well as why the AMA thinks the CVS-Aetna merger is not a good idea and how the AMA views the role of AI in the future of health care.
Zoya Khan is the Editor-in-Chief of THCB as well as an Associate at SMACK.health, a health-tech advisory services for early-stage startups.
I have seen the light. I now, finally, see a clear role for artificial intelligence in health care. And, no, I don’t want it to replace me. I want it to complement me.
I want AI to take over the mandated, mundane tasks of what I call Metamedicine, so I can concentrate on the healing.
In primary care visits in the U.S., doctors and clinics are buried in government mandates. We have to screen for depression and alcohol use, document weight counseling for every overweight patient (the vast majority of Americans), make sure we probe about gender at birth and current gender identification, offer screening and/or immunizations for a host of diseases, and on and on and on. All this in 15 minutes most of the time.
Never mind reconciling medications (or at least double checking the work of medical assistants without pharmacology training), connecting with the patient, taking a history, doing an examination, arriving at a diagnosis, and formulating and explaining a patient-focused treatment plan.
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