Use of ChatGPT in Pediatric Urology and its Relevance in Clinical Practice: Is it useful?
Bibliographic record
Abstract
Abstract Introduction Artificial intelligence (AI) can be described as the combination of computer sciences and linguistics, objective building machines capable of performing various tasks that otherwise would need Human Intelligence. One of the many AI based tools that has gained popularity is the Chat-Generative Pre-Trained Transformer (ChatGPT). Due to the popularity and its massive media coverage, incorrect and misleading information provided by ChatGPT will have a profound impact on patient misinformation. Furthermore, it may cause mistreatment and misdiagnosis as ChatGPT can mislead physicians on the decision-making pathway. Objective Eevaluate and assess the accuracy and reproducibility of ChatGPT answers regarding common pediatric urological diagnoses. Methods ChatGPT 3.5 version was used. The questions asked for the program involved Primary Megaureter (pMU), Enuresis and Vesicoureteral Reflux (VUR). There were three queries for each topic, adding up to 9 in total. The queries were inserted into ChatGPT twice, and both responses were recorded to examine the reproducibility of ChatGPT’s answers. After that analysis, both questions were combined, forming a single answer. Afterwards, those responses were evaluated qualitatively by a board of three specialists with a deep expertise in the field. A descriptive analysis was performed. Results ChatGPT demonstrated general knowledge on the researched topics, including the definition, diagnosis, and treatment of Enuresis, VUR and pMU. Regarding Enuresis, the provided definition was partially correct, as the generic response allowed for misinterpretation. As for the definition of VUR, the response was considered appropriate. And for pMU it was partially correct, lacking essential aspects of its definition such as the diameter of the dilatation of the ureter. Unnecessary exams were suggested, for both Enuresis and pMU. Regarding the treatment of the conditions mentioned, it specified treatments to Enuresis that are known to be ineffective, such as bladder training. Discussion AI has a wide potential to bring several benefits to medical knowledge, improving decision-making and patient education. However, following the reports on the literature, we found a lack of genuine clinical experience and judgment from ChatGPT, performing well in less complex questions, yet with a steep decrease on its performance as the complexity of the queries increase. Therefore, providing wrong answers to crucial topics. Conclusion ChatGPT responses present a combination of accurate and relevant information, but also incomplete, ambiguous and, occasionally, misleading details, especially regarding the treatment of the investigated diseases. Because of that, it is not recommended to make clinical decisions based exclusively on ChatGPT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.030 | 0.180 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".