Evaluating an AI Chatbot “Prostate Cancer Info” for Providing Quality Prostate Cancer Screening Information: Cross-Sectional Study
Bibliographic record
Abstract
Background: Generative artificial intelligence (AI) chatbots may be useful tools for supporting shared prostate cancer (PrCA) screening decisions, but the information produced by these tools sometimes lack quality or credibility. "Prostate Cancer Info" is a custom GPT chatbot developed to provide plain-language PrCA information only from websites of key authorities on cancer and peer-reviewed literature. Objective: The objective of this paper was to evaluate the accuracy, completeness, and readability of Prostate Cancer Info's responses to frequently asked PrCA screening questions. Methods: A total of 23 frequently asked PrCA questions were individually input into Prostate Cancer Info. Responses were recorded in Microsoft Word and reviewed by 2 raters for their accuracy and completeness. Readability of content was determined by pasting responses into a web-based Flesch Kincaid Reading Ease Scores calculator. Results: Responses to all questions were accurate and culturally appropriate. In total, 17 of the 23 questions (74%) had complete responses. The average readability of responses was 64.5 (SD 8.7; written at an 8th-grade level). Conclusions: Generative AI chatbots, such as Prostate Cancer Info, are great starting places for learning about PrCA screening and preparing men to engage in shared decision-making but should not be used as independent sources of PrCA information because key information may be omitted. Men are encouraged to use these tools to complement information received from a health care provider.
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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 | 0.028 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".