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Dr. Geraldine McGinty, Chair of ACR, talks to Aidoc - The three ways AI will transform radiology and medicine

Outcomes, Time to Treatment, Triage, Turnaround Time

For the past few years, the question of: “will machines replace jobs in medicine?” has been an increasingly hot topic. People speculate that with the rise of medical AI, radiology will be one of the most heavily impacted fields due to its highly computerized practice that is rooted in image interpretation of numerous CT, mammography, and MRI scans.

During an interview with Dr. Geraldine McGinty, the first woman in 100 years to be elected as Chair of the American College of Radiology, the answer becomes clearer.

Those who embrace win the race.

Despite the hype suggesting that AI will replace radiologists, Dr. McGinty states that simply won’t be the case. Rather, radiologists not embracing AI will be replaced by radiologists who do.

The word embrace has sensitive connotations. Most people think embracing AI means believing that algorithms are better than doctors at their jobs and that the world will value automation and efficiency over human-to-human interactions. These ideas incite highly emotional debates.

For Dr. McGinty, the term embrace underscores understanding the professional opportunities with machine learning. She encourages department chairs and head radiologists to think of an imaging value chain— adopting a diagnostic business model that incorporates digitization tools to provide value for patients. Shifting from a mindset of “reading images” to an organization of machine learning tools, she argues, can aggregate information with greater accuracy, faster speed, and lower costs in medical decision making.  

The future of work is a collaboration with AI, not a disregard of it.  

According to Dr. McGinty, here are three ways that AI can transform the future of work in radiology and other fields of medicine:

1.) Accelerate Interpretation and Diagnoses

Radiologists play a crucial role in interpreting images and advising physicians on how to utilize the results to direct patient care. Machine learning tools may enable radiologists to prioritize images that have a higher potential to have abnormal findings that are acute and require attention urgently.

While AI tools can support radiologists they will not replace radiologists who will ultimately make the final diagnosis. The important role that radiologist play in communicating their interpretations to doctors and patients remains, even as radiologists adopt new tools.  But using AI to support the radiologist’s interpretation and workflow can facilitate more communication with referring colleagues and a greater opportunity to connect with patients.

2.)  Simplify Administrative Procedures

Radiologists and other healthcare professionals are burdened by increasing administrative complexity and paperwork. AI systems can potentially simplify these administrative hassles and, by creating more seamless procedures, AI can potentially help reduce radiologist burnout.

3.) Increasing diversity and access to care

The potential benefits of AI to enhance access to care in underserved areas are exciting when combined with existing teleradiology capabilities. If done right, AI can become an equalizing force in the field of radiology to bring the power of imaging diagnosis to all patients.

Dr. McGinty’s perspective helps us clearly understand the effects of AI adoption in medical imaging but doesn’t neglect the fact that AI should be treated responsibly and with caution. AI that serves physicians and patients has the potential to transform radiology into an even more advanced and inclusive specialty – whose adoption will serve as the leading example for other medical fields to come.

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