Table 10_AI dialogues in cartilage repair: which guides evidence-based decisions better?.x

Table 10_AI dialogues in cartilage repair: which guides evidence-based decisions better? is a dataset by Sen Yang Xiao, last updated on 2026-05-29. It contains results from a study comparing ChatGPT, DeepSeek, and Google Search on cartilage repair questions across tissue engineering and surgery domains. The dataset includes matched comparisons, blinded quality scoring using the ASH framework, and readability analysis.

Use Cases

• Compare AI model accuracy and safety in medical contexts based on the Accuracy-Safety-Hallucination (ASH) framework scores.
• Analyze readability differences between AI-generated and search engine answers based on Flesch-Kincaid Grade Level scores.
• Benchmark performance of different AI platforms (ChatGPT, DeepSeek) on domain-specific medical FAQs.
• Study the application of a dual-axis framework integrating classification and quality scoring for medical AI evaluation.

Strengths

• Data is derived from a matched three-way comparison of three platforms (ChatGPT, DeepSeek, Google Search) on identical questions.
• Quality assessment involved independent evaluation by three blinded raters using a structured framework.
• Dataset is licensed under CC-BY-4.0, permitting open sharing and adaptation.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small at 10.5 KB, indicating limited scope.

Provenance

Source

Sen Yang Xiao via figshare

Collection Method

Study queried Google Search for top FAQs, then submitted identical questions to ChatGPT (GPT-4 API), DeepSeek (V3 API), and Google for comparison.

Time Range

Questions sourced for cartilage tissue engineering in 2023 and cartilage repair surgery in 2024.

Freshness

Last updated 2026-05-29 05:53:32; freshness should be verified.