Evidence map›Paper›PMID 42180415›Full record

ArticleGynecologic oncology reports2026

Can ChatGPT Take My Call? evaluating AI in gynecologic oncology telephone triage.

Ann Marie Mercier, Sharonne Holtzman, Aashna Saini, Lisa J Mordell, Andrew Vallejo, Kristen Zeligs, Stephanie Williams, Caitlin Carr, Stephanie V Blank

Abstract read
In one paragraph

Article in Gynecologic oncology reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Ann Marie MercierDepartment of Obstetrics, Gynecology, and Reproductive Science, Division of Gynecologic Oncology, Icahn School of Medicine at Mount Sinai, United States.
Sharonne HoltzmanDepartment of Obstetrics, Gynecology, and Reproductive Science, Division of Gynecologic Oncology, Icahn School of Medicine at Mount Sinai, United States.
Aashna SainiDepartment of Obstetrics, Gynecology, and Reproductive Science, Division of Gynecologic Oncology, Icahn School of Medicine at Mount Sinai, United States.
Lisa J MordellDepartment of Obstetrics, Gynecology, and Reproductive Science, Icahn School of Medicine at Mount Sinai, United States.
Andrew VallejoDepartment of Obstetrics, Gynecology, and Reproductive Science, Division of Gynecologic Oncology, Icahn School of Medicine at Mount Sinai, United States.
Kristen ZeligsDepartment of Obstetrics, Gynecology, and Reproductive Science, Division of Gynecologic Oncology, Icahn School of Medicine at Mount Sinai, United States.
Stephanie WilliamsDepartment of Obstetrics, Gynecology, and Reproductive Science, Icahn School of Medicine at Mount Sinai, United States.
Caitlin CarrDepartment of Obstetrics, Gynecology, and Reproductive Science, Division of Gynecologic Oncology, Icahn School of Medicine at Mount Sinai, United States.
Stephanie V BlankDepartment of Obstetrics, Gynecology, and Reproductive Science, Division of Gynecologic Oncology, Icahn School of Medicine at Mount Sinai, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: As access to artificial intelligence (AI) expands, patients and clinicians increasingly rely on these platforms for medical information and guidance. This study assessed ChatGPT's ability to triage and respond to gynecologic oncology (GO) patient telephone calls. Methods: In this cross-sectional study, 30 patient scenarios received by on-call GO fellows were evaluated using ChatGPT-4o. Four physicians independently rated responses for accuracy, comprehensiveness, clarity, relevance, and applicability using a 5-point Likert scale (1 = very poor; 5 = very good) and assessed the presence of misinformation (yes/no). Wilcoxon signed-rank tests compared mean ratings with a predefined threshold of 3.0 ("acceptable"), with significance set at p < 0.05. The prevalence of misinformation was calculated with 95% confidence intervals (CI). Agreement with provider recommendations was assessed using Cohen's kappa. Misclassification patterns (over-triage vs under-triage) were summarized. Results: Among 30 scenarios, exact agreement between AI and fellows occurred in 26 cases (86.7%). Misinformation was identified in 4 responses (3.3%; 95% CI, 0.6-16.7%). Misclassifications included three cases (10.0%) over-triaged to clinic within one week and one case (3.3%) under-triaged to stay home rather than be seen within one week. In all 10 scenarios where the fellows' recommended emergency department evaluation, AI issued the same recommendation. Agreement between AI and fellow triage decisions was high (κ = 0.87). Across all categories, physician ratings of AI responses significantly exceeded the acceptable threshold (p < 0.001). Conclusion: ChatGPT demonstrated high reliability in triaging and responding to GO patient telephone calls, producing clear, accurate, and clinically appropriate guidance. Its consistent performance in urgent scenarios and tendency toward conservative triage suggest that AI may serve as a valuable adjunct to support on-call personnel and enhance after-hours triage workflows in GO.

Indexed as

Artificial intelligenceChatGPTClinical decision supportTelephone triage

Identifiers

PMID42180415
PMCPMC13196423

What OpenQuestion holds

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Registered trials

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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.