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עמוד בית
Fri, 08.05.26

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April 2026
Amit Toledano MD, Ehud Raz Gatt MD, Asaf Laks MD, Biana Dubinsky-Pertzov MD MPH, Adi Einan-Lifshitz MD, Eran Pras MD, Asaf Shemer MD

Background: The rapid evolution of large language models warrants updated benchmarking in ophthalmology to determine whether newer versions offer clinically meaningful improvements over earlier models and human comparators.

Objectives: To evaluate the diagnostic accuracy of ChatGPT-4o and ChatGPT-5 in ophthalmic cases and to compare it with previously reported results of ChatGPT-3.5, residents, and specialists.

Methods: This retrospective cohort study was conducted in one academic tertiary medical center. We reviewed data of patients admitted to the ophthalmology department from June 2022 to January 2023. We then created two clinical cases for each patient. The first was according to medical history alone (Hx). The second added the clinical examination (Hx and Ex). For each case, we asked for the three most likely diagnoses from ChatGPT-4o and ChatGPT-5. We then compared the accuracy rates (at least one correct diagnosis) with previous results of ChatGPT-3.5, residents, and specialists.

Results: A total of 63 cases were analyzed, first using history alone and then with examination findings. Based on history alone, GPT-5 and GPT-4o correctly identified 73% and 70% of cases, respectively, outperforming GPT-3.5 (54%, P < 0.05) and approaching the accuracy of residents (75%) and attending physicians (71%, P < 0.05). When physical examination was included, diagnostic accuracy rose to 94% for GPT-5 and 89% for GPT-4o, surpassing GPT-3.5 (68%, P < 0.05) and closely matching or exceeding human performance (residents 94%, attendings 87%).

Conclusions: ChatGPT-4o and ChatGPT-5 significantly outperformed GPT-3.5 and achieved diagnostic accuracy similar or even higher to clinicians in diagnosing ophthalmology cases.

November 2019
December 2018
Dvir Shalem, Asaf Shemer, Ora Shovman MD, Yehuda Shoenfeld MD FRCP MACR and Shaye Kivity MD

Background: Guillain-Barré syndrome (GBS) is an autoimmune disease of the peripheral nervous system with a typical presentation of acute paralysis and hyporeflexia. Intravenous immunoglobulin (IVIG) and plasma exchange (PLEX) are treatments that have proven to expedite recuperation and recovery of motor function.

Objectives: To describe our experience at one tertiary medical center treating GBS with IVIG and to compare the efficacy of IVIG as the sole treatment versus combined therapy of IVIG and plasma exchange.

Methods: We reviewed the records of all patients diagnosed with GBS and treated with IVIG at the Sheba Medical Center from 2007 to 2015 and collected data on patient demographics, disease onset and presentation, and treatments delivered. The motor disability grading scale (MDGS) was used to evaluate the motor function of each patient through the various stages of the disease and following therapy.

Results: MDGS improvement from admission until discharge was statistically significant (P < 0.001), as was the regainment of motor functions at 3 and 12 months follow-up compared to the status during the nadir of the disease. The effectiveness of second-line treatment with IVIG following PLEX failure and vice versa was not statistically significant (P > 0.15).

Conclusions: The majority of patients included in this study experienced a significant and rapid improvement of GBS following treatment with IVIG. Combined therapy of PLEX and IVIG was not proven to be effective in patients who encountered a failure of the first-line treatment.

July 2018
Asaf Shemer B.Med.Sc, Liron Talmi MD, Dror S. Shouval MD, Gil Har-Zahav MD and Raz Somech MD PhD
January 2018
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