AI continues to evolve at a rapid pace, with new algorithms and solutions being developed all the time. What kind of long-term impact could these technologies have on patient care? What will radiology—and healthcare as a whole—look like in 2030 or 2035?
At RSNA 2019 in Chicago, we asked numerous vendors to look into their own personal crystal balls and predict how AI will change radiology in another 10 to 15 years. A selection of vendor responses can be read below:
- Imad B. Nijim, chief information officer, MEDNAX Radiology Solutions:
“AI in radiology is just as impactful and transformational to our industry as when we moved from film to digital imaging. AI will permeate clinical informatics across all healthcare disciplines, with radiology having the most potential for innovation. First, AI will empower radiologists and impact the clinical practice by improving quality, reducing administrative burden, introducing a new level of image quantification and more. Second, AI will empower the administrators through the use of workflow models to ensure billing efficacy, procedure integrity and fraud detection.”
- Karley Yoder, vice president and general manager of AI, GE Healthcare:
“AI will radically transform radiology over the next 10 to 15 years, allowing clinicians to be much more efficient, accurate and available to spend time with the care team and their patients. This transformation will come as the industry learns how to efficiently unlock data and unleash intelligence.
- Unlocking Data: For the potential of AI to be realized in healthcare, the first step has nothing to do with AI. The first step is breaking down decades of entrenched data silos that exist within a hospital setting. Platforms will be introduced over the next five years that effectively aggregate data from across the patient record—imaging, EMR, pathology, lab, patient-generated, genomics, etc.—to enable a clinician (and their AI tools) to see the entirety of the patient story and context. Right now this task is left up to a time-strapped clinician to ‘hunt and peck’ across many different systems to manually compile this view. All this work will be eliminated.
- Unleashing Intelligence: Once you have all the data in one place, the next step is to run world-leading AI across the entire longitudinal spectrum of the patient history. This ability will require sophisticated platforms to build these complex and nuanced AI solutions in a manner that is both ethical and generalizable to an entire global population. Furthermore, once the AI is developed, it will not be enough to release thousands of different AI solutions that each tell part of the story. Instead, there will be ‘AI for AI’ that sits on top of each AI puzzle piece and pulls the various insights into one complete view for the clinician to review and act upon. Finally, this completed puzzle of AI then must be delivered in an invisible manner into the clinicians' existing workflow, leveraging the tools and workflow steps they already use and trust.”
- Gene Saragnese, CEO, MaxQ AI:
“In radiology specifically, I think AI is moving us from a place where radiologists are interpreting images to one where they are diagnosing patients. That community does have a history of bringing IT solutions together to achieve a goal.”
- Woojin Kim, MD, chief medical information officer, Nuance:
“Taking advice from Amara's law that says, ‘We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run,’ I hope that we would have made significant advances in AI in healthcare in 10-15 years. I honestly do not know what the impact will be 10-15 years out, as no one predicted the impact of deep learning on radiology today 10 years ago. In healthcare IT, things move slower. There are many challenges when it comes to AI in healthcare, but what I can see for the short term is that more and more research will be done on the evaluation of commercial AI models as more sites will pilot these AI models.
More places will also implement and use AI models in the future. People will use advanced analytics to expedite the validation process and use it for ongoing surveillance as more and more realize its importance and become more familiar with topics like concept drift and data drift. The FDA will hopefully have a mechanism to clear continuously learning algorithms safely.
Over the next several years, I think you will begin to see AI models performing more complex tasks, including identifying more than one finding. As there is greater adoption, I hope these models can move to demonstrate actual patient outcome improvements—even at the personalized medicine level—but that data will take time to gather. I hope this excitement around AI can be a catalyst to update the healthcare IT infrastructure and institutions put even greater emphasis on data governance. I also hope this can lead to some real positive changes in interoperability that is so lacking today. On a more global scale, I hope this technology can be used to address physician shortage issues in underserved countries over the next decade.
We have to be careful not to overhype and overpromise; as long as we do not enter another AI winter, this progress should continue.”
- Elad Walach, co-founder and CEO, Aidoc:
“In the short term—say, the next five years—AI will be an augmenter and force multiplier, helping clinicians target their expertise via triage, acting as a ‘second reader’ or automating some routine tasks like measurement.
In the long term, I believe radiology AI will move into new, powerful areas like predictive analytics. This won't just be about making the radiologist faster and more accurate; it will uncover completely new, unknown value from medical imaging and be able to detect new signs of disease or risk factors.”
- Hyun-Jun Kim, co-founder and chief strategy officer, VUNO:
“I strongly believe that we will see AI making greater contributions to revolutionizing the entire aspects of clinical workflows, shifting its role from throughput-based applications to providing more individualized and personalized healthcare to patients. That is, AI-driven approaches will enable more rapid, consistent, robust and objective quantification of abnormalities in radiology, allowing medical professionals to diagnose diseases more efficiently and offer bespoke medical care through identifying the types and timing of medical exams a patient needs. This will ultimately lead to early detection and timely treatment of diseases reducing overall medical costs.”
- Marcel Nienhuis, vice president of marketing, VIDA:
“Initially, we were seeing a lot of AI algorithms offering detection of suspicious features and anatomical segmentation. Now we are seeing algorithms that go beyond detection to automatically measure or quantify image features. The value proposition of both detection and auto-quantification is largely built around physician efficiency. AI algorithms will continue to deliver on automating the mundane portions of radiology reads.
Soon, we see a world in which the first step of a radiologist's workflow will be to review AI findings, confirm/reject findings and then continue on to efficiently review the case. Beyond detection/quantification is decision support and predictive algorithms. We are starting to see these in the market and have several examples in our own portfolio. The value proposition of these algorithms goes beyond physician efficiency to bring improved patient outcomes, which in turn, lowers the cost of care and improves quality of life.”