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.
As I walk into the building, the sheer grandiosity of the room is one to withhold — it’s as if I’m walking into Grand Central station. There’s a small army of people, all busy at their desks, working to carry out the next wave of innovations helping more than a million lives within the Greater Philadelphia region. However, I’m not here to catch a train or enjoy the sights. I’m at the office of the President and CEO of Thomas Jefferson University, Dr. Stephen Klasko, currently at the helm of one of the largest healthcare systems in the U.S.
Let me backup a little.
The theme of nearly every conversation about the future of technology now revolves around Artificial Intelligence (AI). Much weight is placed on the potential capacity of AI to disrupt industries and change them to the very core. This pressure has been felt to a large extent within nearly every aspect of healthcare where AI has been projected to improve patient care delivery while saving billions of dollars.
Unfortunately, most discussions exploring the implications of AI only superficially look at either the product or the algorithm that powers these products. The short-sightedness of this approach is not an easy one to fix. Yes, clinical studies validating AI backed products are vital but AI cannot be viewed just like any other drug or a medical device. There’s much more to be considered when we examine the broader role of this technology, because this technology can shape the entire healthcare system. To place the impact of a far reaching technology, you need an even longer sighted vision. It’s a rare breed of people that have experienced the tumultuous history of change within medicine but can still call upon the lessons learned to execute innovations and bring meaningful results.
Currently, three South Korean medical institutions – Gachon University Gil Medical Center, Pusan National University Hospital and Konyang University Hospital – have implemented IBM’s Watson for Oncology artificial intelligence (AI) system. As IBM touts the Watson for Oncology AI’s to “[i]dentify, evaluate and compare treatment options” by understanding the longitudinal medical record and applying its training to each unique patient, questions regarding the status and liability of these AI machines have arisen.
Given its ability to interpret data and present treatment options (along with relevant justifications), AI represents an interim step between a diagnostic tool and colleague in medical settings. Using philosophical and legal concepts, this article explores whether AI’s ability to adapt and learn means that it has the capacity to reason and whether this means that AI should be considered a legal person.
Through this exploration, the authors conclude that medical AI such as Watson for Oncology should be given a unique legal status akin to personhood to reflect its current and potential role in the medical decision-making process. They analogize the role of IBM’s AI to those of medical residents and argue that liability for wrongful diagnoses should be generally based on a medical malpractice basis rather than through products liability or vicarious liability. Finally, they differentiate medical AI from AI used in other products, such as self-driving cars.
“We built it and we just let it run. We’re a few dudes in an office and our goal is to keep it running. It does everything we could do, except it’s significantly more powerful and it has completely automated how our work is being done,” casually said the hedge fund manager as he described the process by which nearly $1billion was being managed within his fund.
The ‘it’ is an artificial intelligence (AI) based algorithm that uses complex statistics to analyze variables that went into successful decisions and uses advanced computer programs to keep replicating those decisions. All this, while it continuously learns from – and improves upon – its mistakes as it encounters new variables.
These machine intelligent systems are applying the many different forms of AI and fundamentally changing the financial industry. From applying Natural Language Processing in detecting Anti-Money Laundering and fraudulent financial activity to applying Cognitive Computing to analyze wide varieties of variables in building better trading algorithms and to leveraging Deep Learning to looking at consumer decision patterns and providing personalized ‘chatbots,’ AI is transforming the financial sector.
One of the most noticeable areas where this disruption is taking place is within hedge funds: hedge funds that are transitioning their trading desks to AI backed systems, are already beginning to outperform hedge-funds backed by humans alone. What’s really quite astonishing though is how, in the short span of a few years, how far reaching the results have been.
Hearing about hedgies working with AI researchers to make even more money doesn’t inspire the rest of us to greatness. However, it may be valuable to look a brief historical overview of how the financial industry reached this juncture.
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.