ArticleDigital health
Assessing the accuracy and clinical utility of GPT-4O in abnormal blood cell morphology recognition.
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.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- From Gram Stain to Decision Support: Performance of Multimodal Large Language Models in Blood Culture Microscopy.Journal of imaging informatics in medicine · 2026Article
- Article
- Comparative Evaluation of Five Multimodal Large Language Models for Medical Laboratory Image Recognition: Impact of Prompting Strategies on Diagnostic Accuracy.Diagnostics (Basel, Switzerland) · 2026Article
- Rapidly Benchmarking Large Language Models for Diagnosing Comorbid Patients: Comparative Study Leveraging the LLM-as-a-Judge Method.JMIRx med · 2025Article
- Assessing the Accuracy, Completeness and Safety of ChatGPT-4o Responses on Pressure Injuries in Infants: Clinical Applications and Future Implications.Nursing reports (Pavia, Italy) · 2025Article
- Multimodal reasoning agent for enhanced ophthalmic decision-making: a preliminary real-world clinical validation.Frontiers in cell and developmental biology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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
Identifiers
What OpenQuestion holds
Registered trials
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.