Leave your bias aside and take a look into the healthcare future with me. No, artificial intelligence, augmented intelligence and machine learning will not replace the radiologist. It will allow clinicians to.
The year is 2035 (plus or minus 5 years), the world is waking up after a few years of economic hardship and maybe even some dreaded stagflation. This is an important accelerant to where we are going, economic hardship, because it will destroy most radiology AI startups that have thrived on quantitative easing polices and excessive liquidity of the last decade creating a bubble in this space. When the bubble pops, few small to midsize AI companies will survive but the ones who remain will consolidate and reap the rewards. This will almost certainly be big tech who can purchase assets/algorithms across a wide breadth of radiology and integrate/standardize them better than anyone. When the burst happens some of the best algorithms for pulmonary embolism, stroke, knee MRI, intracranial hemorrhage etc. etc. will become available to consolidate, on the “cheap”.
Hospitals can now purchase AI equipment that is highly effective both in cost and function, and its only getting better for them. It doesn’t make sense to do so now but soon it will. Consolidation in healthcare has led to greater purchasing power from groups and hospitals. The “roads and bridges” that would be needed to connect such systems are being built and deals will soon be struck with GE, Google, IBM etc., powerhouse hundred-billion-dollar companies, that will provide AI cloud-based services. RadPartners is already starting to provide natural language processing and imaging data to partners; that’s right, you speak into the Dictaphone and it is recorded, synced with the image you dictated, processed with everyone else to find all the commonalities in descriptors to eventually replace you. It is like the transcriptionists ghost of the past has come back to haunt us and no one cried for them. Prices will be competitive, and adoption will be fast, much faster than most believe.
Now we have some patients who arrive for imaging, as outpatients, ER visits, inpatients; it does not matter the premise is the same. Ms. Jones has chest pain, elevated d-dimer, history of Lupus anti-coagulant and left femoral DVT. Likely her chart has already been analyzed by a cloud-based AI (merlonintelligence.com/intelligent-screening/) and the probability of her having a PE is high, this is relayed to the clinician (PA, NP, MD, DO) and the study is ordered. She’s sent for a CT angiogram PE protocol imaging study. This is important to understand because there will be no role for the radiologist at this level. The recommendation for imaging will be a machine learning algorithm based off more data and papers than any one radiologist could ever read; and it will be instantaneous and fluid. Correct studies will be recommended and “incorrectly” ordered studies will need justifications without radiologist validation.
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^.
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!