From the first diagnostic radiology report in 1896 describing abdominal findings, to the ubiquity of radiography in hospitals in the mid-20th century, to the discovery and use of CT and MRI and implementation of cross-sectional imaging, and most recently the digitization of images and image transfer through PACS, radiology has always been a part of change in healthcare.

Artificial intelligence (AI) in radiology will no doubt bring additional changes to the profession. AI will lead to significant changes in the workforce and training of radiologists, as well as in the overall radiology market, and will pose questions that need to be answered.


Speaking with radiologists in the United States, there are essentially two camps. There are a fair number who, rightly or wrongly, believe AI will be an adjunct to radiologists and not necessarily a replacement. There is another camp, however, that is far more pessimistic.

These radiologists have a ‘sky is falling’ mentality and believe far fewer radiologists will be needed in the future. Many leaders in the field are in the former camp and believe AI will be an adjunct. By looking at colleagues in pathology, we can gleam some insight into the role of automation in repetitive interpretive tasks in medicine. For a long time, pathologists would manually interpret blood samples as part of a complete blood cell count, or a CBC.

However, in the mid-20th century, much of this process began to be automated through the development and use of a Coulter counter. This machine would automatically perform CBCs, with only a small number (5-10%) requiring manual interpretation. Pathologists today do not routinely perform CBCs and have focused their work on histochemical and immunohistological interpretations.

In the same vein, radiologists spend majority of their time interpreting radiology studies. If a significant fraction of this is automated, what will the radiologist do with the additional time? One avenue is of course direct consultation with patients. There is obviously the possibility of change, but right now, however, the payment structures in the United States are not aligned to easily support such an endeavor.

There are secondary implications of this as well. An often-overlooked aspect of AI in radiology is the issue of residency positions. Are we training too many radiologists in the United States? Right now, approximately 1100 new radiologists are minted in the US annually. Should there be fewer residents? Some leaders in the camp with the belief that AI will be an adjunct to radiologists have said that “AI won’t replace radiologists, but radiologists who use AI will replace those who don’t.” Even in that setting, the implication is that there could be fewer radiologists. Regardless, radiologists will need to gain new skills and work patterns [1]. This, of course, requires changing radiology training, which, today, is predominantly spent on image interpretation.

The workforce situation is different in other parts of the world. In the United Kingdom, the Royal College of Radiologists (RCR), through their workforce surveys, has said that there is a shortage of clinical radiologists, and trending toward a greater shortage [2]. The situation is starker in other nations. At RSNA 2018, RSNA president Dr. Vijay Rao of Thomas Jefferson University, described a visit to the radiologist department in a large hospital in Johannesburg, South Africa. She described seeing boxes of plain chest radiographs scattered about. The radiologist working in South Africa mentioned how there were not enough radiologists to interpret chest x-rays [3]. AI could help immensely with the shortfall in radiologists in these and similar countries. What is a cause for concern in the United States may be a blessing elsewhere.


Consolidation in health care sector in the United States has been steadily ongoing for nearly two decades. Hospitals are buying out or merging with nearby hospitals to create multi-hospital organizations. Radiology has not been immune to this consolidation. A recent article in the Journal of the American College of Radiology showed that the percentage of radiologists working in 100-plus person practices has increased over the past four years, while those practicing in practices with 99-or-less radiologists has decreased over the same time period [4]. It is through this change through which AI will be implemented.

Right now, many of the initiatives to implement AI in radiology are performed in large academic medical centers (AMCs), who themselves have been consolidating with nearby facilities. Because of access to larger amounts of data (among a host reasons), large AMCs and large private practices have a better framework with which to create, test, and implement AI in radiology relative to their smaller competitors. Will smaller practices partner up with larger entities as they see the larger entities better able to leverage AI for financial gain, thereby only increasing consolidation? The jury is still out.


These are only some of the questions that need to be asked, debated, and answers researched when discussing the effect of AI in the practice of radiology, particularly as it relates to the radiology workforce and practice types within the United States. Whatever the outcome for
radiologists, I am optimistic that AI in radiology will be a blessing for patients: It flattens the hierarchy between patient and doctor, allows doctors to focus on more complex cases, and makes imaging more accessible to more individuals.


Davenport TH, Dreyer KJ. AI will change radiology, but it won’t replace radiologists. Harvard Business Review 27 March 2018. Available at:

The Royal College of Radiologists. Clinical radiology UK workforce census 2018 report. RCR. April 2019. Available at: https://www.rcr. publication_files/clinical-radiology-ukworkforce-census-report-2018.pdf.

Pearson D. Q&A: 20 minutes with 2018 RSNA president Vijay Rao, MD. Radiology Business 25 November 2018. Available at: topics/leadership/qa-20-minutes2018-rsna-president-vijay-rao-md.

Rosenkrantz AB, Fleishon HB, Silva E, Bender CE, Duszak R. Radiology practice consolidation: Few but bigger groups over time. Journal of the American College of Radiology [article in press]. Available at: