From the October 2022 issue of HealthCare Business News magazine
By Peter Shen
Only a few years ago, radiology’s artificial intelligence solutions were touted to the rafters at medical imaging conferences. In 2022, the hype dust has settled. Certainly, the radiology community recognizes AI’s core benefits. It saves time by automating repetitive tasks. It also leverages pattern recognition to identify areas of concern that the radiologist might miss.
But the initial, pre-pandemic question of “What immediate value can AI bring to the radiologist?” has given way to new questions. They underscore how much AI must evolve to provide broader, more substantive value — to the radiologist, the healthcare institution, and the patient.
In addition to its established time-saving abilities and quantitative measurements, AI has begun to provide qualitative visualizations and guidance associated with suspected malignancies. While encouraging, this development raises a question: What is the rationale behind this qualitative guidance? A radiologist develops a reading methodology over a long career. Now, AI is introducing additional data points that may not even be relevant to that radiologist’s diagnosis. Suddenly, AI’s purported time-saving benefit is debatable. How does the radiologist contextualize this new data and incorporate it into a reporting style?
If AI offered imaging descriptors that elaborated on the rationale for its diagnosis, the radiologist could better decide whether to agree with AI’s findings. This more informed decision-making could improve the identification of false positives and increase diagnostic accuracy. AI-generated imaging descriptors also would help the radiologist explain a diagnosis to radiologists-in-training, who will ultimately incorporate AI into their routine reading and reporting practices.
Radiologists also want AI’s findings to be integrated into their routine diagnostic workflow. This integration would solidify AI’s practical value, which healthcare institutions eye closely. But AI has a potentially different value for a clinician in the emergency room than in the intensive care unit, and a different value when treating inpatients versus outpatients. Can we examine how AI’s value differs with reading environment? Furthermore, how often must AI find something that the radiologist would miss to be considered valuable? Should the radiologist only use AI for studies that he or she is unsure about? Must AI be applied to every case for it to have value?
