The Role of Artificial Intelligence in Patient Education: A Bladder Cancer Consultation with ChatGPT
Bibliographic record
Abstract
Objectives: ChatGPT is a large language model that is able to generate human-like text. The aim of this study was to evaluate ChatGPT as a potential supplement to urological clinical practice by exploring its capacity, efficacy and accuracy when delivering information on frequently asked questions from patients with bladder cancer. Methods: We proposed 10 hypothetical questions to ChatGPT to simulate a doctor–patient consultation for patients recently diagnosed with bladder cancer. The responses were then assessed using two predefined scales of accuracy and completeness by Specialist Urologists. Results: ChatGPT provided coherent answers that were concise and easily comprehensible. Overall, mean accuracy scores for the 10 questions ranged from 3.7 to 6.0, with a median of 5.0. Mean completeness scores ranged from 1.3 to 2.3, with a median of 1.8. ChatGPT was also cognizant of its own limitations and recommended all patients should adhere closely to medical advice dispensed by their healthcare provider. Conclusions: This study provides further insight into the role of ChatGPT as an adjunct consultation tool for answering frequently asked questions from patients with bladder cancer diagnosis. Whilst it was able to provide information in a concise and coherent manner, there were concerns regarding the completeness of information conveyed. Further development and research into this rapidly evolving tool are required to ascertain the potential impacts of AI models such as ChatGPT in urology and the broader healthcare landscape.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".