Evidence map›Paper›PMID 39502485›Full record

ArticleDigital health

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

Xinjian Cai, Lili Zhan, Yiteng Lin

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Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Xinjian CaiDepartment of Clinical Laboratory Medicine, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China.ORCID https://orcid.org/0009-0003-0006-0396
Lili ZhanDepartment of Clinical Laboratory Medicine, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China.ORCID https://orcid.org/0009-0006-5721-858X
Yiteng LinDepartment of Clinical Laboratory Medicine, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

blood cell morphologyChatGPTclinical laboratoryGPT-4Omorphology recognition

Identifiers

PMID39502485
PMCPMC11536573

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.