Liver Cysts and Artificial Intelligence: Is AI Really a Patient-Friendly Support?
Bibliographic record
Abstract
Background: With the advancement of AI-powered online tools, patients are increasingly turning to AI for guidance on healthcare-related issues. Methods: Acting as patients, we posed eight direct questions concerning a common clinical condition—liver cysts—to four AI chatbots: ChatGPT, Perplexity, Copilot, and Gemini. The responses were collected and compared both among the chatbots and with the current literature, including the most recent guidelines. Results: Overall, the responses from the four chatbots were generally consistent with the literature, with only a few inaccuracies noted. For questions addressing “grey areas” in clinical research, all chatbots provided generalized answers. ChatGPT, Copilot, and Gemini highlighted the lack of conclusive evidence in the literature, while Perplexity offered speculative correlations not supported by data. Importantly, all chatbots recommended consulting a healthcare professional. While Perplexity, Copilot, and Gemini included references in their responses, not all cited sources were academic or of medium/high evidence quality. An analysis of Flesch Readability Ease Scores and Estimated Reading Grade Levels indicated that ChatGPT and Gemini provided the most readable and comprehensible responses. Conclusions: The integration of chatbots into real-world healthcare scenarios requires thorough testing to prevent potentially serious consequences from misuse. While undeniably innovative, this technology presents significant risks if implemented improperly.
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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 | 0.010 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".