WORCESTER MEDICINE
Pixels, Patterns, and Patients: Radiology Residency in the AI Revolution Continued 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.
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Winter 2023
