Evaluating the ability of artificial intelligence chatbots to respond to oral cancer questions: A descriptive study
Bibliographic record
Abstract
Background: Individual diagnosed with cancer have excessive informational needs regarding various aspects of their disease. This need pushes them to search for information from multiple sources including Artificial Intelligence chatbots. This study aimed to evaluate the accuracy of responses of Artificial Intelligence chatbots to questions regarding different aspects of oral cancers. Methods: Nineteen dichotomous (yes/no) questions were posed to 8 different Artificial Intelligence Chatbots (Chat GTP 3.5, Gemini, Perplexity, Claude, Chat GTP alternative, TextCortex, YouChat, and CoPilot). The responses were recorded and compared with the correct answers. Statistical analyses, including independent sample t-tests, were conducted to compare differences in mean scores. Results: Most (n=14, 73.7%), questions were answered correctly, and only 1 (5.3%) question got two incorrect responses. Only 4 (50.0%) AI chatbots answered all questions perfectly, and only one chatbot scored less than 80% for all questions. The combined mean score for the AI chatbots was 18.22 ± 1.3, and the differences in the mean score of responses between the AI chatbots and blueprint were statistically insignificant (p= 0.562). Conclusion: The general accuracy of responses to different oral cancer-related questions raised by the patients was high. However, not all AI chatbots give correct responses to all questions, therefore it is the role of health professionals to ensure that patients are educated well regarding their diseases and cautioned on relying on AI chatbots totally.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".