Thyroid Nodule Experts Evaluating ChatGPT’s Assessment of Thyroid Nodules Classified by the Bethesda System for Reporting Thyroid Cytopathology
Bibliographic record
Abstract
ImportanceChatGPT has emerged as a medical resource through advanced language processing. Patients with thyroid nodules classified under The Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) may use it to complement discussions with physicians.ObjectiveWe aimed to determine whether ChatGPT's recommendations on managing thyroid nodules classified by TBSRTC align with those of experienced thyroid specialists.Setting/ParticipantsA multidisciplinary panel of 5 thyroid cancer specialists, including otolaryngologists and endocrinologists, from 3 university-affiliated teaching hospitals in Montreal, Canada, evaluated the responses.Intervention/ExposureChatGPT-3.5 was prompted with 4 questions for each of the 6 Bethesda categories regarding the meaning and management of thyroid nodules, generating 24 responses for evaluation.Main Outcome/MeasuresWe assessed ChatGPT's accuracy against the latest American Thyroid Association (ATA) guidelines using a 4-point Likert scale (<50%, 50-74%, 75-89%, >90%). Additionally, specialists rated their comfort or reluctance in recommending ChatGPT as a complementary tool for patient discussions.ResultsOf the 24 ChatGPT-generated responses, 19 (79.2%) demonstrated moderate to good consistency with the ATA guidelines. The mean consistency score was 3.38/4 and median was 3.5. Consensus (IQR ≤ 1) was achieved in 23 out of 24 responses (95.8%), reflecting strong inter-rater reliability. Consistency scores were highest in Bethesda I-III and declined progressively in higher-risk categories, with the lowest mean score observed in Bethesda VI. Similarly, an upward trend in clinician reluctance was observed from Bethesda I through VI, indicating greater caution in recommending ChatGPT responses for patients suspicious for or diagnosed with malignancy (Bethesda V-VI).Conclusion and RelevanceWhile ChatGPT's responses generally align with specialist recommendations, they are not fully reliable. ChatGPT lacks the ability to serve as an independent or accurate source of medical advice for thyroid nodule management. It remains a useful complement for patient discussions, especially in low-risk scenarios, but further improvements are necessary to make it a safe, reliable component of patient care in complex cases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".