ChatGPT Assisting Diagnosis of Neuro-ophthalmology Diseases Based on Case Reports.

Purpose: To evaluate the efficiency of large language models (LLMs) including ChatGPT to assist in diagnosing neuro-ophthalmic diseases based on case reports. Design: Prospective study. Subjects or Participants: We selected 22 different case reports of neuro-ophthalmic diseases from a publicly available online database. These cases included a wide range of chronic and acute diseases that are commonly seen by neuro-ophthalmic sub-specialists. Methods: We inserted the text from each case as a new prompt into both ChatGPT v3.5 and ChatGPT Plus v4.0 and asked for the most probable diagnosis. We then presented the exact information to two neuro-ophthalmologists and recorded their diagnoses followed by comparison to responses from both versions of ChatGPT. Main Outcome Measures: Diagnostic accuracy in terms of number of correctly diagnosed cases among diagnoses. Results: ChatGPT v3.5, ChatGPT Plus v4.0, and the two neuro-ophthalmologists were correct in 13 (59%), 18 (82%), 19 (86%), and 19 (86%) out of 22 cases, respectively. The agreement between the various diagnostic sources were as follows: ChatGPT v3.5 and ChatGPT Plus v4.0, 13 (59%); ChatGPT v3.5 and the first neuro-ophthalmologist, 12 (55%); ChatGPT v3.5 and the second neuro-ophthalmologist, 12 (55%); ChatGPT Plus v4.0 and the first neuro-ophthalmologist, 17 (77%); ChatGPT Plus v4.0 and the second neuro-ophthalmologist, 16 (73%); and first and second neuro-ophthalmologists 17 (17%). Conclusions: The accuracy of ChatGPT v3.5 and ChatGPT Plus v4.0 in diagnosing patients with neuro-ophthalmic diseases was 59% and 82%, respectively. With further development, ChatGPT Plus v4.0 may have potential to be used in clinical care settings to assist clinicians in providing quick, accurate diagnoses of patients in neuro-ophthalmology. The applicability of using LLMs like ChatGPT in clinical settings that lack access to subspeciality trained neuro-ophthalmologists deserves further research.

Double-hit and triple-hit high-grade B-cell lymphoma of the ocular adnexa.

High-grade B-cell lymphoma (HGBL) with c-MYC and BCL2 and/or BCL6 rearrangements, also known as a double-hit and triple-hit lymphoma, is an aggressive non-Hodgkin lymphoma affecting older adults. After formal recognition of this entity in the 2017 revision of the World Health Organization Classification of lymphoid neoplasms, only two well-documented cases of triple-hit lymphoma of the orbit appear in the literature. Herein, we describe a 70-year-old man with progressive diplopia, ophthalmoplegia, and rapidly enlarging temporal mass. Biopsy revealed a tumor morphologically consistent with HGBL, coexpressing CD20, CD10, BCL6, BCL2, and c-MYC on immunohistochemical analysis. Fluorescence in-situ hybridization showed rearrangements in c-MYC and BCL-2 genes, confirming double-hit HGBL. Systemic workup revealed Ann Arbor stage IV disease. This report reviews the existing literature on ocular adnexal double-hit and triple-hit lymphoma and provides an update on the diagnostic ancillary studies, prognostic implications, and latest management for this aggressive hematolymphoid malignancy.