Assessing AI-Powered Patient Education: A Case Study in Radiology.

RATIONALE AND OBJECTIVES: With recent advancements in the power and accessibility of artificial intelligence (AI) Large Language Models (LLMs) patients might increasingly turn to these platforms to answer questions regarding radiologic examinations and procedures, despite valid concerns about the accuracy of information provided. This study aimed to assess the accuracy and completeness of information provided by the Bing Chatbot-a LLM powered by ChatGPT-on patient education for common radiologic exams. MATERIALS AND METHODS: We selected three common radiologic examinations and procedures: computed tomography (CT) abdomen, magnetic resonance imaging (MRI) spine, and bone biopsy. For each, ten questions were tested on the chatbot in two trials using three different chatbot settings. Two reviewers independently assessed the chatbot's responses for accuracy and completeness compared to an accepted online resource, radiologyinfo.org. RESULTS: Of the 360 reviews performed, 336 (93%) were rated "entirely correct" and 24 (7%) were "mostly correct," indicating a high level of reliability. Completeness ratings showed that 65% were "complete" and 35% were "mostly complete." The "More Creative" chatbot setting produced a higher proportion of responses rated "entirely correct" but there were otherwise no significant difference in ratings based on chatbot settings or exam types. The readability level was rated eighth-grade level. CONCLUSION: The Bing Chatbot provided accurate responses answering all or most aspects of the question asked of it, with responses tending to err on the side of caution for nuanced questions. Importantly, no responses were inaccurate or had potential to cause harm or confusion for the user. Thus, LLM chatbots demonstrate potential to enhance patient education in radiology and could be integrated into patient portals for various purposes, including exam preparation and results interpretation.

Race and Gender Influences the Presentation of Eosinophilic Esophagitis.

Our understanding of the influence of race and gender on the presentation of eosinophilic esophagitis (EoE) is incomplete. To address this gap, we examined the effect of race and gender on the presentation of EoE. In this retrospective study, we reviewed the medical records of 755 EoE patients and recorded their demographic, clinical, endoscopic, and histologic information. Descriptive statistics were used to characterize the cohort. Multivariate logistic regression was used to identify predictors of race and gender after accounting for potential confounders. There was a bimodal distribution for age at diagnosis of EoE. Approximately 43% had pediatric onset EoE, while 57% had adult onset EoE. Male (68%) predominance was observed. Dysphagia (57%) and abdominal pain (20%) were among the most common presenting symptoms. Multivariate analysis revealed that African Americans (AAs) were diagnosed earlier [aOR: 0.96 (95% CI: 0.95-0.99); P = 0.01] and had significantly lower odds of manifesting furrows [aOR: 0.30 (95% CI: 0.12-0.77); P = 0.01] as compared with Whites. Males were diagnosed earlier [aOR 0.98 (0.97-0.99; P = 0.04] and had higher odds of having abnormal endoscopic findings [aOR: 1.43 (1.05-1.97); P = 0.02] when compared with females. Race and gender influence the presentation of EoE. Future studies aimed at investigating the interplay between race, gender, and molecular mechanisms of EoE are warranted.