Do large language model chatbots perform better than established patient information resources in answering patient questions? A comparative study on melanoma
Bibliographic record
Abstract
BACKGROUND: Large language models (LLMs) have a potential role in providing adequate patient information. OBJECTIVES: To compare the quality of LLM responses with established Dutch patient information resources (PIRs) in answering patient questions regarding melanoma. METHODS: Responses from ChatGPT versions 3.5 and 4.0, Gemini, and three leading Dutch melanoma PIRs to 50 melanoma-specific questions were examined at baseline and for LLMs again after 8 months. Outcomes included (medical) accuracy, completeness, personalization, readability and, additionally, reproducibility for LLMs. Comparative analyses were performed within LLMs and PIRs using Friedman's Anova, and between best-performing LLMs and gold-standard (GS) PIRs using the Wilcoxon signed-rank test. RESULTS: Within LLMs, ChatGPT-3.5 demonstrated the highest accuracy (P = 0.009). Gemini performed best in completeness (P < 0.001), personalization (P = 0.007) and readability (P < 0.001). PIRs were consistent in accuracy and completeness, with the general practitioner's website excelling in personalization (P = 0.013) and readability (P < 0.001). The best-performing LLMs outperformed the GS-PIR on completeness and personalization, yet it was less accurate and less readable. Over time, response reproducibility decreased for all LLMs, showing variability across outcomes. CONCLUSIONS: Although LLMs show potential in providing highly personalized and complete responses to patient questions regarding melanoma, improving and safeguarding accuracy, reproducibility and accessibility is crucial before they can replace or complement conventional PIRs.
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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 | 0.001 |
| 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".