Predicting Glaucoma Before Onset Using a Large Language Model Chatbot

Huang, Xiaoqin; Raja, Hina; Madadi, Yeganeh; Delsoz, Mohammad; Poursoroush, Asma; Kahook, Malik Y.; Yousefi, Siamak

doi:10.1016/j.ajo.2024.05.022

American Journal of Ophthalmology

2024

DOI: 10.1016/j.ajo.2024.05.022

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Predicting Glaucoma Before Onset Using a Large Language Model Chatbot

Xiaoqin Huang,

Hina Raja,

Yeganeh Madadi

et al.

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Cited by 5 publications

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Comment on: Predicting Glaucoma Before Onset Using a Large Language Model Chatbot

Wang

2024

American Journal of Ophthalmology

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Comment on: Predicting Glaucoma Before Onset Using a Large Language Model Chatbot

Wang

2024

American Journal of Ophthalmology

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ChatGPT-4o′s performance on pediatric Vesicoureteral reflux

Akyol Onder,

Ensari,

Ertan

2024

Journal of Pediatric Urology

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Assessing the accuracy and clinical utility of GPT-4O in abnormal blood cell morphology recognition

Cai,

Zhan,

Lin

2024

DIGITAL HEALTH

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Objectives To evaluate the accuracy and clinical utility of GPT-4O in recognizing abnormal blood cell morphology, a critical component of hematologic diagnostics. Methods GPT-4O's blood cell morphology recognition capabilities were assessed by comparing its performance with hematologists. A total of 70 images from the Chinese National Center for Clinical Laboratories, External Quality Assessment (EQA) from 2022 to 2024 were analyzed. Two experienced hematology experts evaluated GPT-4O's recognition accuracy using a Likert scale. Results GPT-4O achieved an overall accuracy of 70% in blood cell morphology recognition, significantly lower than the 95.42% accuracy of hematologists (p < 0.05). For peripheral blood smears and bone marrow smears, GPT-4O's accuracy was 77.14% and 62.86% respectively. Likert scale evaluations revealed further discrepancies, with GPT-4O scoring 288.50 out of 350, compared to higher manual scores. GPT-4O accurately recognized certain intracellular inclusions such as Howell-Jolly bodies and Auer rods, while it misidentified fragmented red blood cells as neutrophilic metamyelocytes and oval-shaped red blood cells as sickle cells. Additionally, GPT-4O had difficulty accurately identifying intracellular granules and distinguishing cell nuclei and cytoplasm. Conclusion GPT-4O's performance in recognizing abnormal blood cell morphology is currently inadequate compared to hematologists. Despite its potential as a supplementary tool, significant improvements in its recognition algorithms and an expanded dataset are necessary for it to be reliable for clinical use. Future research should focus on enhancing GPT-4O's diagnostic accuracy and addressing its current limitations.

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Predicting Glaucoma Before Onset Using a Large Language Model Chatbot

Cited by 5 publications

References 21 publications

Comment on: Predicting Glaucoma Before Onset Using a Large Language Model Chatbot

Comment on: Predicting Glaucoma Before Onset Using a Large Language Model Chatbot

ChatGPT-4o′s performance on pediatric Vesicoureteral reflux

Assessing the accuracy and clinical utility of GPT-4O in abnormal blood cell morphology recognition

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