AI Dialogue Evaluations for Cartilage Repair Questions

A comparative study evaluates ChatGPT, DeepSeek, and Google Search responses to cartilage repair questions. The dataset likely contains question classifications, quality scores, and readability metrics from a dual-axis framework. The author is Sen Yang Xiao, and the data was last updated on May 29, 2026.

Use Cases

• Benchmarking AI chatbot accuracy in medical domains based on the Accuracy-Safety-Hallucination (ASH) framework
• Analyzing readability differences between AI-generated and search-engine answers based on Flesch-Kincaid scores
• Comparing the functional roles of different AI platforms for stakeholder-specific tool matching as described in the conclusion

Strengths

• Comparative analysis includes three platforms (Google, ChatGPT, DeepSeek) across two domains (cartilage tissue engineering and cartilage repair surgery)
• Quality assessment uses a blinded, multi-rater system with three independent evaluators
• Results include specific median accuracy scores (e.g., DeepSeek median 5.00 in CTE) and statistical significance (Bonferroni-corrected p-values)

Limitations

• Column-level documentation is absent; field semantics must be inferred after download
• Row count is unknown, which may limit suitability assessment
• The dataset is 26.5 KB, indicating a very limited scope likely focused on summary results rather than raw data

Provenance

Source

figshare

Collection Method

Questions were sourced from Google Search FAQs, with answers generated by ChatGPT and DeepSeek APIs for matched comparison.

Time Range

Cartilage tissue engineering questions from 2023, cartilage repair surgery questions from 2024.

Freshness

Last updated 2026-05-29 05:53:28; freshness should be verified