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^.
What are the challenges of bringing advanced imaging services to India? What motivates an entrepreneur to start build an MRI service? How does the entrepreneur go about building the service? In this episode, I discuss radiology in India with Dr. Harsh Mahajan, Dr. Vidur Mahajan and Dr. Vasantha Venugopal. Dr. Harsh Mahajan is the founder of Mahajan Imaging, a leading radiology practice in New Delhi, and now a pioneer in radiology research in India.
Listen to our conversation on Radiology Firing Line Podcast here.
Saurabh Jha is an associate editor of THCB and host of Radiology Firing Line Podcast of the Journal of American College of Radiology, sponsored by Healthcare Administrative Partner.
In this episode of Radiology Firing Line Podcast, Danny Huges and I discuss a JAMA paper: A comparison of diagnostic imaging ordering patterns between advanced practice clinicians and primary care physicians following office-based evaluation and management visits.
Listen to our conversation on Radiology Firing Line here.
Saurabh Jha is a contributing editor to THCB and host of Radiology Firing Line Podcast of the Journal of American College of Radiology, sponsored by Healthcare Administrative Partner
What are the challenges of getting imaging to Africa? In this episode of Radiology Firing Line, I convene a panel of experts in Africa. We discuss the challenges of bringing new technology to Africa, the new need for imaging driven by public health gains and increased longevity of Africans, the insalubrious practice of “equipment dumping”, amongst others.
Kassa Darge, MD PhD, is Professor of Radiology and Radiologist-in-Chief at Children’s Hospital of Philadelphia. He is also Honorary Professor of Radiology in the Department of Radiology at Addis Ababa University in Ethiopia.
Omolola Mojisola (Monica) Atalabi MBBS MBA, is Professor of Radiology and Chief of Pediatric Radiology at University College Hospital, Ibadan, Nigeria. She is President of both the Association of Radiologist in Nigeria and the World Federation of Pediatric Imaging.
William Sykes is the CEO of Tecmed Arica – a medical equipment, device, service and training provider in the Southern African region.
Artificial intelligence requires data. Ideally that data should be clean, trustworthy and above all, accurate. Unfortunately, medical data is far from it. In fact medical data is sometimes so far removed from being clean, it’s positively dirty.
Consider the simple chest X-ray, the good old-fashioned posterior-anterior radiograph of the thorax. One of the longest standing radiological techniques in the medical diagnostic armoury, performed across the world by the billions. So many in fact, that radiologists struggle to keep up with the sheer volume, and sometimes forget to read the odd 23,000 of them. Oops.
Surely, such a popular, tried and tested medical test should provide great data for training AI? There’s clearly more than enough data to have a decent attempt, and the technique is so well standardised and robust that surely it’s just crying out for automation?Continue reading…
I’ve previously written comprehensively on where to invest in Radiology AI, and how to beat the hype curve precipice the field is entering. For those that haven’t read my previous blog, my one line summary is essentially this:
“Choose companies with a narrow focus on clinically valid use cases with large data sets, who are engaged with regulations and haven’t over-hyped themselves …”
The problem is… hardly any investment opportunities in Radiology AI like this actually exist, especially in the UK. I thought it’s about time I wrote down my ideas for what I’d actually build (if I had the funding), or what companies I would advise VC’s to invest in (if they existed).
Surprisingly, none of the companies actually interpret medical images – I’ll explain why at the end!
We’ve all heard the big philosophical arguments and debate between rockstar entrepreneurs and genius academics – but have we stopped to think exactly how the AI revolution will play out on our own turf?
At RSNA this year I posed the same question to everyone I spoke to: What if radiology AI gets into the wrong hands? Judging by the way the crowds voted with their feet by packing out every lecture on AI, radiologists would certainly seem to be very aware of the looming seismic shift in the profession – but I wanted to know if anyone was considering the potential side effects, the unintended consequences of unleashing such a disruptive technology into the clinical realm?
While I’m very excited about the prospect and potential of algorithmic augmentation in radiological practice, I’m also a little nervous about more malevolent parties using it for predatory financial gains.
Dean Jameson, Trustees, Faculty, Family and Friends, and most of all, Graduates of the Class of 2017:
Standing before you on this wonderful day, seeing all the proud parents and significant others, I can’t help but think about my father. My dad didn’t go to college; he joined the Air Force right after high school, then entered the family business, which manufactured women’s clothing. He did reasonably well, and my folks ended up moving to a New York City suburb, where I grew up.
There were a lot of professionals in the neighborhood, but my dad admired the doctors the most. He was even a little envious of them. This became obvious on weekend evenings when he’d get dressed to go out to a neighborhood party. He’d look perfectly fine – slacks, collared shirt, maybe a sweater. But there was one thing out of place: he’d be wearing our garage door opener on his belt. “Dad, what exactly are you doing?” I would ask, somewhat mortified.
“There’ll be lots of doctors at the party tonight,” he’d reply. “They all have beepers, I have nothing.” The strangest part was when the party was next door, the garage door would sometimes go up and down, as dad showed off his “beeper.”