The Clinical Integration of ChatGPT Through an Augmented Patient Encounter in a Real-World Urological Cohort: A Feasibility Study
Bibliographic record
Abstract
Background/Objectives: To evaluate the viability of using ChatGPT in a real clinical environment for patient education during informed consent for flexible cystoscopy, assessing its practicality, patient perceptions, and clinician evaluations within a urological cohort. Methods: A prospective feasibility study was conducted at a single institution involving patients with haematuria who attended an in-person clinic review with access to ChatGPT-4o mini. Using predetermined prompts regarding haematuria, we evaluated the accuracy, consistency, and suitability of the ChatGPT information. Responses were appraised for errors, omission of key information, and suitability for patient education. The functionality, usability, and quality of ChatGPT for patient education were assessed by three urologists using the Patient Education Materials Assessment Tool (PEMAT) and DISCERN tools. Readability was assessed using the Flesch–Kincaid tests. Further clinician questionnaires evaluated ChatGPT’s accuracy, reproducibility, and integration potential. Results: Ten patients were recruited, but one patient was excluded because he refused to use ChatGPT due to language barriers. All patients found ChatGPT to be useful, but most believed it could not entirely replace the doctor, especially for obtaining informed consent. There were no significant errors. The mean PEMAT score for understandability was 77.8%, and actionability was 63.8%. The mean DISCERN score was 57.7, corresponding to a ‘good’ quality score. The Flesch Reading Ease score was 30.2, with the writing level comparable to US grade level 13. Conclusions: ChatGPT offers valuable support for patient education, delivering accurate and comprehensive information. However, challenges with readability, contextual understanding, and actionability highlight the need for development and careful integration. Generative artificial intelligence (AI) should augment, not replace, clinician–patient interactions, emphasising ethical considerations and patient trust. This study provides a basis for further exploration of AI’s role in healthcare.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".