Objectives: Large language models (LLMs) demonstrate remarkable potential in healthcare communication. However, whether they can process complex, high-volume medical information, such as stroke-related content, remains insufficiently validated. This study aimed to evaluate the natural language processing capabilities of LLMs in handling such content and to develop an evaluation instrument. Methods: A survey compared educational materials generated by two LLMs (ChatGPT 4.0 and Claude 3) with neurologist-authored content on stroke. The materials were based on two clinical scenarios representing distinct stroke etiologies: cardioembolism and large-artery atherosclerosis. They were evaluated in terms of accuracy, legality, ethics, comprehensiveness, and information delivery. Scores for comprehensiveness and information delivery were compared according to participants’ agreement with the use of LLMs in healthcare. Results: ChatGPT received the highest scores across all domains, except for legality in Scenario 2. In Scenario 1, the ranking for accuracy and summarization of clinical information was, from highest to lowest, ChatGPT, Claude, and the neurologist (η2 = 0.140, p < 0.001; η2 = 0.175, p < 0.001). The same hierarchy was observed in Scenario 2 for accuracy (η2 = 0.077, p < 0.001) and summarization (η2 = 0.194, p < 0.001). Participants who agreed with the use of LLMs in healthcare assigned higher scores for the comprehensiveness (Scenario 1, p = 0.005; Scenario 2, p = 0.007) and information delivery (Scenario 1, p = 0.003; Scenario 2, p = 0.026) of ChatGPT-generated materials than participants who did not agree. Conclusions LLMs demonstrated adequate capability to convey complex content, such as stroke-related information, in an accessible and understandable manner for non-experts.

Coronavirus disease 2019 (COVID-19) is a serious infectious disease with multisystem alteration including neurological complications. COVID-19 associated encephalitis is a potentially fatal viral infection of the brain. Diffusion-weighted images (DWI) are a useful evaluation modality of cytotoxic edema in patients with encephalitis. We report on the reversible DWI change in a patient with COVID-19 associated encephalitis, who had diffused cytotoxic edema in the affected temporal lobe and accompanied status epilepticus. Sequential imaging showed that the cytotoxic edema in DWI confirmed recovery to normal after 12 days in the presence of cortical edema in fluid-attenuated inversion recovery image after aggressive neurocritical management. Thus, prompt, proper management is indispensable during the acute period and DWI may be a valuable tool for reflecting the clinical course of COVID-19 associated encephalitis.