Actionable companion · Assessment & reasoning

From AI evidence to responsible practice: Artificial Intelligence, the ChatGPT Large Language Model: Assessing the Accuracy of Responses to the Gynaecological Endoscopic Surgical Education and Assessment (GESEA) Level 1-2 knowledge tests

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not findings or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Age / stage not established. Verify the participants, intervention and comparator before applying this study in a new setting.

Original authors’ abstract, reproduced under the linked source license:

Background In 2022, OpenAI launched ChatGPT 3.5, which is now widely used in medical education, training, and research. Despite its valuable use for the generation of information, concerns persist about its authenticity and accuracy. Its undisclosed information source and outdated dataset pose risks of misinformation. Although it is widely used, AI-generated text inaccuracies raise doubts about its reliability. The ethical use of such technologies is crucial to uphold scientific accuracy in research. Objective This study aimed to assess the accuracy of ChatGPT in doing GESEA tests 1 and 2. Materials and Methods The 100 multiple-choice theoretical questions from GESEA certifications 1 and 2 were presented to ChatGPT, requesting the selection of the correct answer along with an explanation. Expert gynaecologists evaluated and graded the explanations for accuracy. Main outcome measures ChatGPT showed a 59% accuracy in responses, with 64% providing comprehensive explanations. It performed better in GESEA Level 1 (64% accuracy) than in GESEA Level 2 (54% accuracy) questions. Conclusion ChatGPT is a versatile tool in medicine and research, offering knowledge, information, and promoting evidence-based practice. Despite its widespread use, its accuracy has not been validated yet. This study found a 59% correct response rate, highlighting the need for accuracy validation and ethical use considerations. Future research should investigate ChatGPT’s truthfulness in subspecialty fields such as gynaecologic oncology and compare different versions of chatbot for continuous improvement. What is new? Artificial intelligence (AI) has a great potential in scientific research. However, the validity of outputs remains unverified. This study aims to evaluate the accuracy of responses generated by ChatGPT to enhance the critical use of this tool.

What kind of evidence is this?

Conversational & pedagogical agents · Design requires full-text appraisal

Stage: Age / stage not established. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

ChatGPT (OpenAI, San Francisco, CA, United States) is a large language model trained on an extensive dataset sourced from various channels, including online platforms, literature, and scholarly articles until year 2021. When presented with queries, ChatGPT demonstrates the ability to deliver well crafted, conversational, and easily digestible responses. Developers used reinforcement learning through human feedback to refine the model’s capacity to interpret a broad spectrum of commands and written directives calibrated based on human preferences as positive reinforcement. Additionally, the model underwent training to align with user objectives while mitigating biases and preventing the generation of dangerous or detrimental responses. The origin of the dataset used to train ChatGPT remains undisclosed.

Limitations and uncertainty · source passage

Background In 2022, OpenAI launched ChatGPT 3.5, which is now widely used in medical education, training, and research. Despite its valuable use for the generation of information, concerns persist about its authenticity and accuracy. Its undisclosed information source and outdated dataset pose risks of misinformation. Although it is widely used, AI-generated text inaccuracies raise doubts about its reliability. The ethical use of such technologies is crucial to uphold scientific accuracy in research. Objective This study aimed to assess the accuracy of ChatGPT in doing GESEA tests 1 and 2. Materials and Methods The 100 multiple-choice theoretical questions from GESEA certifications 1 and 2 were presented to ChatGPT, requesting the selection of the correct answer along with an explanation. Expert gynaecologists evaluated and graded the explanations for accuracy. Main outcome measures ChatGPT showed a 59% accuracy in responses, with 64% providing comprehensive explanations. It performed better in GESEA Level 1 (64% accuracy) than in GESEA Level 2 (54% accuracy) questions. Conclusion ChatGPT is a versatile tool in medicine and research, offering knowledge, information, and promoting evidence-based practice. Despite its widespread use, its accuracy has not been validated yet. This study found a 59% correct response rate, highlighting the need for accuracy validation and ethical use considerations. Future research should investigate ChatGPT’s truthfulness in subspecialty fields such as gynaecologic oncology and compare different versions of chatbot for continuous improvement. What is new? Artificial intelligence (AI) has a great potential in scientific research. However, the validity of outputs remains unverified. This study aims to evaluate the accuracy of responses generated by ChatGPT to enhance the critical use of this tool.

Design the system before the pilot

Define whether the agent asks questions, gives hints, explains or assesses. Test hallucinated feedback, excessive praise, misleading confidence and responses when the learner is confused. A conversational interface alone does not establish autonomous agency.

Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.

A 90-day district pilot

Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
Days 16–30 · Design. Treat the paper as background evidence until its population is verified in the full text. Do not assign an age-specific intervention or extend adult results to children. Name an accountable owner and set the baseline, success threshold and budget before implementation.
Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
Days 61–90 · Decide. Extract the participant ages or education stage, recruitment, task and comparator before selecting a local pilot. If these cannot be established, retain the paper for design discussion rather than age-band efficacy claims. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

M Pavone; L Palmieri; N Bizzarri; A Rosati; F Campolo; C Innocenzi; C Taliento; S Restaino; U Catena; G Vizzielli; C Akladios; M.M. Ianieri; J Marescaux; R Campo; F Fanfani; G Scambia. Artificial Intelligence, the ChatGPT Large Language Model: Assessing the Accuracy of Responses to the Gynaecological Endoscopic Surgical Education and Assessment (GESEA) Level 1-2 knowledge tests. Facts, Views & Vision in ObGyn (2024-12-01). https://doi.org/10.52054/fvvo.16.4.052

Companion prepared 2026-11-01. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.