Pushing the Evolution of Radiology: Informatics and What it Means for the Future Radiologist and Radiology Trainee

Tina Shiang, MD

It is an exciting time to be in the field of radiology. Our generation of radiologists and radiology trainees has witnessed several progressive technological advances that forever changed the way we practice medicine. In the 1900s, the internet was born and with the help of Google and various other digital resources, we have the largest spectrum of information available at just the click of a mouse (1). The 2000s brought widespread adoption of modern electronic medical records, popularization of the picture archiving and communication system, and improved standardization and integration of different health systems promoting the current model of a collaborative, team-based approach to patient care (1). Now, in the age of big data and machine learning algorithms, computer vision is offering breakthroughs in automation, optimization and efficiency that are transforming radiology and health care delivery. The possibilities are limitless, to the point where some fear artificial intelligence will make radiologists obsolete and disrupt the integrity of the medical imaging world (2,3).

Informatics is propelling major changes and is easily the hottest topic in radiology. AI algorithms have made great strides, as evidenced by the 136 U.S. Food and Drug Administration-cleared AI algorithms compiled by the American College of Radiology Data Science Institute (4). Just to name a few, these include intracranial hemorrhage detection in neuroradiology, pulmonary embolism detection in chest imaging and subtle fracture detection in musculoskeletal imaging (4). All of these have great potential in optimizing image pattern recognition and assisting the radiologist in more accurate and quicker diagnoses.

In addition, with the ongoing challenges posed by the COVID-19 pandemic, utilization of virtual learning and the remote workforce has truly taken off. On-demand video lectures such as APDR National Virtual Noon Conferences, AUR Diagnostic Radiology Resident Core Curriculum Lecture Series, virtual readouts, and virtual didactic and multidisciplinary conferences are being rapidly embraced to accommodate learning in the socially distanced setting. Remote work and virtual consultations through telemedicine provide more flexibility, convenience and safety to health care workers who are continuing to battle against the virus (2).

Despite the promising results of medical AI systems, “general artificial intelligence” that could “replicate average human intelligence” is still far from replacing the radiologist (3). The mastery that the trained radiologist has in deciphering complex image patterns and prescribing “clinical relevance in image interpretation” remains extremely difficult for computers to match, let alone surpass (3). However, AI can reduce the burden of time-consuming, mundane tasks often termed “scut work” such as protocoling or tracking down clinicians to communicate critical results. This would in turn buy the radiologist time and increased productivity to perform more valuable radiologic work that requires clinical expertise (3). No longer is AI constrained to the massive computing power of supercomputers. By harnessing average, cost-effective computers, AI can be accessible to everyone.

I believe the future of radiology will involve the seamless integration of technology through informatics. The radiologist-computer team will push the limits of human imagination and help us “tackle problems that doctors cannot detect and solve alone (3,5).” Informatics is changing the way radiology is practiced, and radiologists should have a voice in guiding its development. I strongly encourage radiologists and trainees to embrace every opportunity to learn, experience and immerse themselves in exploring what informatics has to offer. Although there is no imminent apocalyptic threat to radiologists’ job security, radiologists who can effectively leverage AI will have a significant edge over those who cannot or choose not to (3). As futurist Kevin Kelly astutely pointed out, “You’ll be paid in the future based on how well you work with robots (3).” We should see AI as a promising partner rather than the nefarious enemy. It should be high priority to determine the best way we can utilize innovative applications in informatics to augment the radiologist and improve patient care (3,5).

My informatics journey and future plans

I had a late start in informatics, but it is never too late to pursue a new passion. Throughout residency, I strived to improve efficiency, reduce medical errors and find ways to enhance resident education. I led several quality improvement projects and collaborated with my residency program to optimize CT protocoling and improve the transparency of MRI protocols. These initiatives helped improve our clinical workflow, reduce operational inefficiencies and enhance the training of future radiologists at my institution. Through these projects, I realized many of the problems I wanted to solve necessitates bridging the gap between medical and computer science fields. After seeking out advice from practicing clinical-informaticists and an automation engineer, it became evident we had to speak the same language and develop a common ground for understanding medical and technical knowledge to collaborate effectively. To this end, this year I am taking the National Imaging Informatics Curriculum Course-Radiology to improve my understanding of imaging informatics fundamentals and plan to further explore opportunities during informatics fellowship.

My vision is to combine my clinical knowledge in radiology, technical knowledge of computer systems and skills gained through collaborations to create optimal technical solutions for clinical problems. I want to be at the forefront of shaping the role of informatics in radiology and as a future MSK-informaticist, I hope my colleagues in radiology share my enthusiasm in integrating radiology and informatics.

References:

1. Evans RS. Electronic Health Records: Then, Now, and in the Future. Yearbook of Medical Informatics. 2016;Suppl 1(Suppl 1):S48-S61. doi:10.15265/ IYS-2016-s006

2. Guilford-Blake, Roxanna. “Wait. Will AI Replace Radiologists After All?” Radiology Business, 18 Feb 2020, https://www.radiologybusiness.com/topics/ artificial-intelligence/wait-will-ai-replace-radiologists-after-all

3. Chan S, Siegel EL. Will machine learning end the viability of radiology as a thriving medical specialty? British Journal of Radiology. 2019; 92(1094):20180416. doi: 10.1259/bjr.20180416. Epub 2018 Nov 1. PMID: 30325645.

4. “FDA-Cleared AI Algorithms.” ACR Data Science Institute. https://models.acrdsi.org/

5. Reardon S. Rise of robot radiologists. Nature. 2019; 576(7787):S54-S58. doi: 10.1038/d41586- 019-03847-z. PMID: 31853073.

Tina Shiang, MD, is a PGY-5 Diagnostic Radiology Resident and Chief Resident at University of Massachusetts in Worcester, Mass. She is a future musculoskeletal radiology and intervention fellow at Brigham and Women’s Hospital and informatics fellow at the Center for Evidence Based Medicine in Boston. Email: tina.shiang@umassmemorial.org