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.
At long last, we seem to be on the threshold of departing the earliest phases of AI, defined by the always tedious “will AI replace doctors/drug developers/occupation X?” discussion, and are poised to enter the more considered conversation of “Where will AI be useful?” and “What are the key barriers to implementation?”
As I’ve watched this evolution in both drug discovery and medicine, I’ve come to appreciate that in addition to the many technical barriers often considered, there’s a critical conceptual barrier as well – the threat some AI-based approaches can pose to our “explanatory models” (a construct developed by physician-anthropologist Arthur Kleinman, and nicely explained by Dr. Namratha Kandulahere): our need to ground so much of our thinking in models that mechanistically connect tangible observation and outcome. In contrast, AI relates often imperceptible observations to outcome in a fashion that’s unapologetically oblivious to mechanism, which challenges physicians and drug developers by explicitly severing utility from foundational scientific understanding.
Catalyst @ Health 2.0 is proud to have worked with the Robert Wood Johnson Foundation to address issues in substance misuse and artificial intelligence through two exciting innovation challenges. Following the finalists’ live pitches at the Health 2.0 Annual Conference, Matthew Holt and Indu Subaiya had the pleasure to interview leaders from the six companies that placed in the top spots across both competitions.
First Place Winners
RWJF Opioid Challenge: the Grand Prize award went to Sober Grid, a social network designed to support, assist, and educate those suffering from addiction and substance misuse. The Sober Grid platform incorporates a suite of geolocated support, a “burning desire” distress beacon, and coaching tools. For those looking to get help and support, the Sober Grid platform is a fantastic free utility.
RWJF AI Challenge: the Grand Prize award went toBuoy, a virtual triage chatbot designed to work on any browser. All too often we rely on quick online searches for health information and sometimes receive inaccurate or unreliable results. The Buoy system takes a more conversational approach and emulates similar techniques a doctor would use when diagnosing symptoms and speaking with a patient.
Second and Third place prizes were also awarded to the following organizations:
If your heart throbs with desire for the new Apple Watch, the Series 4 itself can track that pitter-pat through its much-publicized ability to provide continuous heart rate readings.
On the other hand, if you’re depressed that you didn’t buy Apple stock years ago, your iPhone’s Face ID might be able to discover your dismay and connect you to a therapist.
In its recent rollout of the Apple Watch, company chief operating officer Jeff Williams enthused that the device could become “an intelligent guardian for your health.” Apple watching over your health, however, might involve much more than a watch.
There is hope, hype and hysteria about artificial intelligence (AI). How will AI change how radiology is practiced? I discuss this with Stephen Borstelmann, a radiologist in Florida and a scholar in machine learning.
Listen to our discussion on the Radiology Firing Line Series, hosted by the Journal of the American College of Radiology and sponsored by Healthcare Administrative Partners.
About the author:
Saurabh Jha is a radiologist and contributing editor to THCB. He hosts the Radiology Firing Line Podcasts
Decision making is a daunting task. Combined with navigating health insurance jargon, scattered health information, and feeling crummy as you rush to find care during the onset of a cold, making decisions can be an absolute nightmare. However, artificial intelligence (AI) enabled tools have the potential to change the way we interact with and consume healthcare for the better. AI’s ability to comprehend, learn, optimize and act are keys to organizing the varying nuisances of the healthcare experience.
In a 2018 survey by Accenture, healthcare consumers indicated they would likely use AI for after hours care, support in navigating healthcare services, lifestyle advice, post-diagnosis management, etc. While AI in health is not limited to these functions, the report highlights consumers’ trouble in making informed healthcare decisions, hence this may be an area where AI can truly help.
Another day, another $30m round in health tech. On Monday Qventus raised that from Bessemer Partners, with Mayfield, Norwest and NY Presbyterian kicking in too. That brings their total to $43m in so far–not bad for a 75 person company that is in the somewhat obscure space of using AI to improve hospital operations. Qventus sucks in data and delivers operational suggestions to front line managers. Of course given that somewhere between $1-1.5 trillion goes through America’s hospitals each year, there’s huge potential for saving money. And given that most hospitals are being paid fixed cost per case, anything that can be done to improve throughput and increase productivity drops to the bottom line and is thus likely to meet interested buyers. I talked to CEO Mudit Garg about the problem, his company’s solution and what they were going to do next.