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TRUVACE RECORD VERSION
record: TRV-2026-2874
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-13T00:02:22.193844Z
status: published
lens: trace
sector: health
headline: Diagnostic agreement between ChatGPT-based interpretation of laboratory-generated flow cytometry graphical outputs and hematologist consensus in suspected acute leukemia
dek: Accurate, rapid, and standardized interpretation of multiparametric flow cytometry findings is essential for clinical decision-making in patients evaluated for suspected acute leukemia. This study aimed to evaluate the diagnostic performance of ChatGPT, a general-purpose large language model, in interpreting de-identified, laboratory-generated flow cytometry graphical outputs from patients with suspected acute leukemia and to assess its agreement with the consensus interpretation of two hematologists in terms of…
gain_title: In a retrospective study of 560 patients with suspected acute leukemia, ChatGPT (GPT-5.5) reading flow cytometry plots matched hematologist consensus diagnoses with 78.8% accuracy and substantial agreement (kappa 0.712).
problem_title: In a retrospective study of 560 suspected acute leukemia cases, ChatGPT (GPT-5.5) overestimated blast percentage by a mean 3.92 points and differed systematically from hematologists on AML (p = 0.008), T-ALL (p = 0.023) and no-pathology cases (p = 0.041).
trace_subject: ChatGPT interpretation of flow cytometry plots in suspected acute leukemia
gain_reading: In a retrospective study of 560 patients with suspected acute leukemia, ChatGPT (GPT-5.5) reading flow cytometry plots matched hematologist consensus diagnoses with 78.8% accuracy and substantial agreement (kappa 0.712).
gain_evidence: ChatGPT achieved an overall diagnostic accuracy of 78.8% (95% CI, 75.2%-81.9%), with substantial agreement with hematologist consensus (Cohen's κ = 0.712; 95% CI, 0.664-0.757).
problem_reading: In a retrospective study of 560 suspected acute leukemia cases, ChatGPT (GPT-5.5) overestimated blast percentage by a mean 3.92 points and differed systematically from hematologists on AML (p = 0.008), T-ALL (p = 0.023) and no-pathology cases (p = 0.041).
problem_evidence: Bland-Altman analysis demonstrated a mean positive bias of 3.92 percentage points, indicating systematic overestimation by ChatGPT | McNemar's test identified significant systematic differences between ChatGPT and hematologist classifications for no pathology (p = 0.041), AML (p = 0.008), and T-ALL (p = 0.023).
quick_read: Researchers tested ChatGPT Plus running GPT-5.5 on de-identified PDF images of flow cytometry dot plots from 560 patients undergoing first diagnostic work-up for suspected acute leukemia. Against the consensus of two hematologists, the model chose the right diagnosis in 78.8% of cases from five categories: no pathology, MDS, AML, B-ALL or T-ALL.

Agreement was substantial (Cohen's kappa 0.712), and blast percentage estimates correlated strongly with the hematologists' (r = 0.818). But the chatbot overestimated blasts by an average of 3.92 percentage points and differed systematically from experts on AML, T-ALL and no-pathology classification.

The authors conclude the approach shows promise for preliminary classification and quality assurance, while stating that hematologist validation remains essential where diagnostic stakes are high, such as AML and T-ALL calls, blast counts and hematogone discrimination.
limitation: Expert hematologist validation remains essential, particularly for AML and T-ALL classification, blast percentage estimation and hematogone discrimination, where ChatGPT differed systematically from experts or overestimated blasts.
tag: Dual reading
key_points: The study was a retrospective observational validation of 560 patients who had bone marrow aspiration and flow cytometric immunophenotyping at initial evaluation. | Images came from a three-tube acute leukemia panel with 28 antibody-fluorochrome combinations; raw FCS files were not provided to the model. | Per-diagnosis sensitivity ranged from 69.2% to 87.3% and positive predictive value from 60.0% to 93.5%. | Sensitivity was highest for MDS, while positive predictive value was highest for no pathology. | Blast percentage estimates correlated with hematologists' at r = 0.818, with a mean overestimate of 3.92 percentage points. | Hematogone detection reached 89.5% accuracy, 70.3% sensitivity and 92.4% specificity.
rundown: Between May 4 and 15, 2026, the team uploaded laboratory-generated PDF images directly through the ChatGPT Plus web interface. Each case used the same standardized prompt asking for a blast percentage, a single most likely diagnosis and whether hematogones were present. The first response for every case was archived and encoded without adjudication.

The reference standard was the consensus diagnosis of two hematologists. Diagnosis-specific performance used one-versus-rest sensitivity, specificity, predictive values, accuracy and F1-score, with Cohen's kappa for overall agreement, Pearson correlation and Bland-Altman analysis for blasts.

Results showed 78.8% overall accuracy (95% CI 75.2%-81.9%) and kappa of 0.712. Significant systematic differences from hematologists appeared for no pathology (p = 0.041), AML (p = 0.008) and T-ALL (p = 0.023).

Hematogone detection was more specific than sensitive: 92.4% specificity against 70.3% sensitivity, at 89.5% accuracy. The authors say expert validation remains essential for high-consequence scenarios.
sources:
- peer_reviewed | Cytometry Part B: Clinical Cytometry | https://doi.org/10.1002/cyto.b.70097 | 2026-11-11
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