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The potential of large language models in medicine for education and decisionmaking purposes has been demonstrated as they achieve decent scores on medicalexams such as the United States Medical Licensing Exam (USMLE) and the MedQAexam. In this work, we evaluate the performance of ChatGPT-4 in the specializedfield of radiation oncology using the 38th American College of Radiology (ACR)radiation oncology in-training (TXIT) exam and the 2022 Red Journal Gray Zonecases. For the TXIT exam, ChatGPT-3.5 and ChatGPT-4 have achieved the scores of63.65% and 74.57%, respectively, highlighting the advantage of the latestChatGPT-4 model. Based on the TXIT exam, ChatGPT-4's strong and weak areas inradiation oncology are identified to some extent. Specifically, ChatGPT-4demonstrates better knowledge of statistics, CNS & eye, pediatrics, biology,and physics than knowledge of bone & soft tissue and gynecology, as per the ACRknowledge domain. Regarding clinical care paths, ChatGPT-4 performs better indiagnosis, prognosis, and toxicity than brachytherapy and dosimetry. It lacksproficiency in in-depth details of clinical trials. For the Gray Zone cases,ChatGPT-4 is able to suggest a personalized treatment approach to each casewith high correctness and comprehensiveness. Importantly, it provides noveltreatment aspects for many cases, which are not suggested by any human experts.Both evaluations demonstrate the potential of ChatGPT-4 in medical educationfor the general public and cancer patients, as well as the potential to aidclinical decision-making, while acknowledging its limitations in certaindomains. Because of the risk of hallucination, facts provided by ChatGPT alwaysneed to be verified.

Benchmarking ChatGPT-4 on ACR Radiation Oncology In-Training (TXIT) Exam  and Red Journal Gray Zone Cases: Potentials and Challenges for AI-Assisted  Medical Education and Decision Making in Radiation Oncology