黑料网

Nearly 30 radiology organizations participated in the three-day 2024 黑料网® Intersociety Summer Conference, August 9–11, representing all subspecialties, radiation oncology and medical physics. This year, we convened around the theme, “Artificial Intelligence and Its Impact on Radiology.”

My most surprising takeaway from the conference is that the trend in AI development is going in a direction that seems counter to what radiologists desire the most — helping them at the workstation. If radiologists are leading the research in this field, one would think that innovation would be geared toward improving efficiency in our daily practice.

Many attendees at this conference spoke in a similar way about a few specific issues. For example:

• AI used for image evaluation is not always accurate, and while sometimes helpful in triage, it does not provide much value to a trained radiologist.
• Use of AI as a screening evaluation does not yet significantly alter workflow.
• Radiologists are overwhelmed with current imaging volumes and want AI to help with the mundane tasks that prevent us from completing clinically meaningful work.

While we see much financial investment and hype around AI, a growing feeling of disappointment seems to be underway about the direction that AI is headed. I believe we need to pivot away from the current trajectory and rethink what machine learning can do for us! AI should optimize the radiologist, improving speed, efficiency and eventually, accuracy. This is what will ultimately decrease physician burnout and keep radiologists performing the tasks for which they are best equipped.

With regard to training, there is currently little research into how AI is affecting the resident and fellow experience. Furthermore, there is a wide range in the exposure of trainees to AI across the U.S. Little is currently known about how AI will shape the future of those who are still learning radiology. We must evaluate these potential effects carefully, lest we find degradation in our education systems and an overall decrease in the caliber of the graduating radiologist.

As an 黑料网 Resident and Fellow Member-in-Training Intersociety Committee Representative, I am happy that I participated in this conference as it expanded my knowledge and outlook of AI. The knowledge I gained allowed me to solidify my opinions about the future direction of AI developments to best assist the clinical radiologist.