Tag: RSNA 2024

AI As Malpractice Safety Net

Posted on by Brian Casey

One of the emerging use cases for AI in radiology is as a safety net that could help hospitals avoid malpractice cases by catching errors made by radiologists before they can cause patient harm. The topic was reviewed in a Sunday presentation at RSNA 2024. 

Clinical AI adoption has been held back by economic factors such as limited reimbursement and the lack of strong return on investment. 

  • Healthcare providers want to know that their AI investments will pay off, either through direct reimbursement from payors or improved operational efficiency.

At the same time, providers face rising malpractice risk, with a number of recent high-profile legal cases.

  • For example, a New York hospital was hit with a $120M verdict after a resident physician working the night shift missed a pulmonary embolism. 

Could AI limit risk by acting as a backstop to radiologists? 

  • At RSNA 2024, Benjamin Strong, MD, chief medical officer at vRad, described how they have deployed AI as a QA safety net. 

vRad mostly develops its own AI algorithms, with the first algorithm deployed in 2015. 

  • vRad is running AI algorithms as a backstop for 13 critical pathologies, from aortic dissection to superior mesenteric artery occlusion.

vRad’s QA workflow begins after the radiologist issues a final report (without using AI), and an algorithm then reviews the report automatically. 

  • If discrepancies are found the report is sent to a second radiologist, who can kick the study back to the original radiologist if they believe an error has occurred. The entire process takes 20 minutes. 

In a review of the program over one year, vRad found …

  • Corrections were made for about 1.5k diagnoses out of 6.7M exams.
  • The top five AI models accounted for over $8M in medical malpractice savings. 
  • Three pathologies – spinal epidural abscess, aortic dissection, and ischemic bowel due to SMA occlusion – would have amounted to $18M in payouts over four years.
  • Adding intracranial hemorrhage and pulmonary embolism creates what Strong called the “Big Five” of pathologies that are either the most frequently missed or the most expensive when missed.

The Takeaway

The findings offer an intriguing new use case for AI adoption. Avoiding just one malpractice verdict or settlement would more than pay for the cost of AI installation, in most cases many times over. How’s that for return on investment?

RSNA Goes All-In on AI

Posted on by Brian Casey

CHICAGO – It’s been AI all the time this week at RSNA 2024. From clinical sessions packed with the latest findings on AI’s utility to technical exhibits crowded with AI vendors, artificial intelligence and its impact on radiology was easily the hottest trend at McCormick Place.

Radiology greeted AI with initial skepticism when the first applications like IBM Watson were introduced at RSNA around a decade ago.

  • But the field’s attitude has been evolving to the point where AI is now being viewed as perhaps the only technology that can save the discipline from the vicious cycle of rising exam volume, falling reimbursement, and pervasive levels of burnout.

RSNA telegraphed the shift last year by announcing that Stanford University’s Curtis Langlotz, MD, PhD, would be RSNA 2024 president. 

  • Langlotz is one of the most respected AI researchers and educators in radiology, and even coined the phrase that while AI would not replace radiologists, radiologists with AI would replace those without it. 

In his president’s address, Langlotz echoed this theme, painting a picture of a future radiology in which humans and machines collaborate to deliver better patient care than either could alone.

  • Langlotz’s talk was followed by a presentation by another prominent AI luminary – Nina Kottler, MD, of Radiology Partners.

Kottler took on the concerns that many in radiology (and in the world at large) have about AI as a disruptive force in a field that cherishes its traditions.

  • She advised radiology to take a leading role in AI adoption, repeating a famous quote that the best way to predict the future is to create it yourself. 

What were the other trends besides AI at RSNA 2024? They included…

  • Photon-counting CT, which is likely to see new market entrants in 2025.
  • Total-body PET, with PET scanners that have extra-long detector arrays.
  • Theranostics, a discipline that integrates diagnosis and therapy and promises to breathe new life into SPECT.
  • CT colonography and CCTA, which will see positive reimbursement changes in 2025.
  • Continued growth of CT lung screening, especially as a tool for opportunistic screening of other conditions.
  • Continued expansion of AI for breast screening.

The Takeaway

The RSNA meeting has been called radiology’s Super Bowl and World Cup all rolled into one, and this year didn’t disappoint. RSNA 2024 showed that radiology is prepared to fully embrace AI – and a future in which humans and machines collaborate to deliver better patient care.

Mammo AI Kicks Off RSNA 2024

Posted on by Brian Casey

Welcome to RSNA 2024! This year’s meeting is starting with a bang, with two important sessions highlighting the key role AI can play in breast screening. 

Sunday’s presentations cap a year that’s seen the publication of several large studies demonstrating that AI can improve breast cancer screening while potentially reducing radiologist workload. 

  • That momentum is continuing at RSNA 2024, with morning and afternoon sessions on Sunday dedicated to mammography AI. 

Some findings from yesterday’s morning session include … 

  • Two AI algorithms were better than one when supporting radiologists in breast screening, with cancer detection ratios relative to historic performance rising from 0.97 to 1.08 with one AI to 1.09 to 1.14 with two algorithms.
  • ScreenPoint Medical’s Transpara algorithm was able to prioritize the worklist for 57% of breast screening exams by assigning risk scores to mammograms, helping reduce report turnaround times. 
  • iCAD’s ProFound AI software helped radiologists detect 7.8% more breast cancers on DBT exams, and cancers were detected at an earlier stage. 
  • Applying AI for breast screening to a racially diverse population yielded evenly distributed performance improvements.

Meanwhile, the Sunday afternoon session also included significant mammography AI presentations, such as …

  • A hybrid screening strategy – with suspicious breast cancer cases only recalled if the AI exhibits high certainty – reduced workload 50%. 
  • Lunit’s Insight DBT AI showed potential to reduce interval cancer rates in DBT screening by identifying 27% of false-negative and 36% of interval cancers.
  • In the ScreenTrustCAD trial in Sweden, using Lunit’s Insight MMG algorithm to replace a double-reading radiologist reduced workload 50% with comparable cancer detection rates.
  • A German screening program found that ScreenPoint Medical’s Transpara AI boosted the cancer detection rate by 8.7% (from 0.68% to 0.74%), with 8.8% of cancers solely detected by AI.
  • Researchers took a look back at abnormality scores from three commercially available AI algorithms after cancer diagnosis, finding evidence that cancers could be detected earlier. 

The Takeaway

Breast screening seems to be the clinical use case where radiologists need the most help, and Sunday’s sessions show the progress AI is making toward achieving that reality. 

Be sure to check back on our X, LinkedIn, and YouTube pages for more coverage of this week’s events in Chicago. And if you see us on the floor of McCormick Place, stop and say hello!

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