Pixels, Patterns, and Patients: Radiology Residency in the AI Revolution

As the Industrial Revolution once replaced the rhythmic trots of horses with the rhythmic hums of machines, artificial intelligence (AI) is replacing the manual intricacies of medicine with algorithms that promise to reshape our understanding of health and disease. Cutting across disciplines and industries, AI is not merely an evolution; it’s a revolution, changing the very foundation upon which systems operate. Medicine, always one of the first consumers of new technology, is itself on the precipice of revolution brought by AI. Radiology, perhaps more than any other field rooted in technology and innovation, is at the epicenter of this seismic shift. The potential of AI in enhancing diagnosis, treatment, and overall patient care is immense. But like any powerful tool, its true value can only be harnessed when understood in depth. As the adoption of deep learning tools in diagnostic imaging surges, the subtleties and potential errors of AI underscore the need for radiologists who excel not only in diagnostic acumen but also in liaising with computer scientists and software engineers. It becomes crucial, then, for radiology residents—future stalwarts of the discipline— to delve deep into the intricacies, challenges, and promises of AI.

Integrating AI into the radiology resident education curriculum is an exciting but challenging new endeavor. A study led by Emory University in early 2023 revealed that 83% of surveyed radiology residents across 21 U.S. residency programs desired the inclusion of AI and machine learning education in their curriculum; less than 20%, though, had actually received any formal AI education or research experience (1). At the University of Massachusetts, our residency program has found several ways to integrate AI into our training.

We have a wide variety of conferences from AI radiology subject matter experts, as well as didactic sessions, online and in-person AI conferences, and journal clubs to help navigate through the dilemmas and intricacies of AI. These sessions serve as dedicated spaces for exploring not only the mechanics of machine learning but also the ethical and professional conundrums that AI introduces to the field. The discussions facilitated by this forum enable residents to build a multidimensional understanding of AI, incorporating technical knowledge with ethics.

Going beyond theory, the radiology department has integrated AI in practice. There are multiple AI tools being trialed by the attending radiologists. One tool in particular, Aidoc, has been integrated into the clinical process for multiple disciplines. Aidoc (AI-doc) is a sophisticated deeplearning convolutional neural network tool (2), currently used predominantly for annotating acute pathologies, including pulmonary embolisms and intracranial hemorrhages. Having processed tens of thousands of studies, the tool’s prowess in pulmonary embolism detection has reached unparalleled precision. If Aidoc identifies a potential embolism not mentioned in a radiology report, the system immediately flags the discrepancy for a thorough review. Aidoc’s capability to scan any CT study encompassing parts of the lungs has led to the serendipitous discovery of multiple pulmonary embolisms— incidents that would typically fly under the radar in conventional reviews. Senior residents, equipped with access to Aidoc and its suite of algorithms, witness firsthand the algorithm’s remarkable efficacy and its nuanced

inaccuracies. Aidoc’s value transcends merely flagging pathologies. It serves as a springboard for intellectual exploration and dialogue, urging users to reflect upon both its errors and unexpected revelations. The unearthing of such incidental findings, while revolutionary, ushers in a host of challenges and inquiries, especially regarding their clinical relevance and ensuing management. For instance, the algorithm might occasionally, albeit mistakenly, detect a pulmonary embolism in a pulmonary vein. While such inaccuracies are becoming rarer as the algorithm evolves, they underscore the vital insight that the algorithm is a complement, not a substitute, to a radiologist’s expertise. Therefore, its outcomes must always be met with discernment and critical thinking.

Language Learning Models (LLMs) represent a fusion of technology and linguistics, designed to grasp, and generate human-like text based on patterns from vast amounts of data. These models have rapidly become part of the general public’s mind as LLMs like ChatGPT and Bard enter daily use for many people. Likewise, LLMs will become more integrated into medicine (3) and, particularly, radiology. LLMs are becoming useful tools for residents, aiding in developing differential diagnoses. By seamlessly analyzing provided clinical information, they generate comprehensive lists of potential diagnoses. This not only facilitates quicker and more informed decision-making but also nurtures analytical and critical thinking skills among residents. Additionally, early pioneers have anecdotally begun using LLMs to start automating portions of the dictated report, generating, for instance, automatic summary impressions, saving the radiologists time. LLMs promise to help merge traditional knowledge with the prowess of modern technology.

The integration of AI into radiology isn’t just inevitable; it’s transformative. It promises not just enhanced time efficiency and streamlined workflows, but it also carves a path for the emergence of adept radiologists who can harness AI’s full potential. The confluence of AI and radiology heralds a synergy that pushes the boundaries of what’s possible, setting new standards for top-tier healthcare delivery. Radiology residents, poised to be the vanguards of this discipline, must delve deep into the intricacies, challenges, and vast horizons of AI. This ensures that this groundbreaking technology is directed with discernment, commitment to ethical practices, and a relentless pursuit of exceptional patient care. +

Michael Purcaro, MD/PhD, MS, is a computer scientist by training, and currently in the second year of his radiology residency at UMass Med. Email: michael.purcaro@umassmed.edu

References:

1. Salastekar NV, et. al. “Artificial Intelligence/Machine Learning Education in Radiology: Multi-institutional Survey of Radiology Residents in the United States.” Acad. Radiol. 2023.

2. Ojeda P, et. al. “The utility of deep learning: evaluation of a convolutional neural network for detection of intracranial bleeds on non-contrast head computed tomography studies.” MEdical IMaging 2019: Image Processing, 2019.

3. Lee P, etl .al. “The AI Revolution in Medicine: GPT-4 and Beyond.” Pearson, 2023.