H1 Radiology Recap

That’s a wrap for the first half of 2023. Below are the top stories in radiology for the past 6 months, as well as some tips on what to look for in the second half of the year.

  • Radiology Bounces Back – After several crushing years in the wake of the COVID-19 pandemic, the first half brought welcome news to radiology on several fronts. The 2023 Match wrapped up with diagnostic radiology on top as the most popular medical specialty for medical students over the past 3 years. Radiology was one of the highest-compensated specialties in surveys from Medscape and Doximity, and even vendors got into the act, reporting higher revenue and earnings as supply chain delays cleared up. Will the momentum continue in the second half? 
  • Burnout Looms Large – Even as salaries grow, healthcare is grappling with increased physician burnout. Realization is growing that burnout is a systemic problem – tied to rising healthcare volumes – that defies self-care solutions. Congressional legislation would boost residency slots 5% a year for 7 years, but is even this enough? Alternatively, could IT tools like AI help offload medicine’s more mundane tasks and alleviate workloads? Both questions will be debated in the back half of 2023. 
  • In-Person Shows Are Back – The pandemic took a wrecking ball to the trade show calendar, but things began to return to normal in the first half of 2023. Both ECR and HIMSS held meetings that saw respectable attendance, following up on a successful RSNA 2022. By the time SIIM 2023 rolled around in early June, the pandemic was a distant memory as radiology focused on the value of being together

The Takeaway

As the second half of 2023 begins, all eyes will be on ChatGPT and whether a technology that’s mostly a curious novelty now can evolve into a useful clinical tool in the future. 

Radiology Puts ChatGPT to Work

ChatGPT has taken the world by storm since the AI technology was first introduced in November 2022. In medicine, radiology is taking the lead in putting ChatGPT to work to address the specialty’s many efficiency and workflow challenges. 

Both ChatGPT and its newest iteration, GPT-4, are forms of AI known as large language models – essentially neural networks that are trained on massive volumes of unlabeled text and are able to learn on their own how to predict the structure and syntax of human language. 

A flood of papers have appeared in just the last week or so investigating ChatGPT’s potential:

  • ChatGPT could be used to improve patient engagement with radiology providers, such as by creating layperson reports that are more understandable, or by answering patient questions in a chatbot function, says an American Journal of Roentgenology article.
  • ChatGPT offered up accurate information about breast cancer prevention and screening to patients in a study in Radiology. But ChatGPT also gave some inappropriate and inconsistent recommendations – perhaps no surprise given that many experts themselves often disagree on breast screening guidelines.
  • ChatGPT was able to produce a report on a PET/CT scan of a patient – including technical terms like SUVmax and TNM stage – without special training, found researchers writing in Journal of Nuclear Medicine.
  • GPT-4 translated free-text radiology reports into structured reports that better lend themselves to standardization and data extraction for research in another paper published in Radiology. Best of all, the service cost 10 cents a report.

Where is all this headed? A review article on AI in medicine in New England Journal of Medicine gave the opinion – often stated in radiology – that AI has the potential to take over mundane tasks and give health professionals more time for human-to-human interactions. 

They compared the arrival of ChatGPT to the onset of digital imaging in radiology in the 1990s, and offered a tantalizing future in which chatbots like ChatGPT and GPT-4 replace outdated technologies like x-ray file rooms and lost images – remember those?

The Takeaway

Radiology’s embrace of ChatGPT and GPT-4 is heartening given the specialty’s initial skeptical response to AI in years past. As the most technologically advanced medical specialty, it’s only fitting that radiology takes the lead in putting this transformative technology to work – as it did with digital imaging.

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