ArticleFrontiers in cell and developmental biology2026
AI dialogues in cartilage repair: which guides evidence-based decisions better?
Article in Frontiers in cell and developmental biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: This study compares ChatGPT, DeepSeek, and Google Search in addressing cartilage repair-related questions across two domains-cartilage tissue engineering (CTE) and cartilage repair surgery (CRS)-using a dual-axis framework that integrates classification, blinded quality scoring, and readability analysis. Methods: Google Search was queried for the top 20 frequently asked questions (FAQs) in each domain (CTE in 2023, CRS in 2024). The identical Top-10 Google-derived FAQs per domain were subsequently submitted to all three platforms-Google, ChatGPT (GPT-4 API), and DeepSeek (V3 API)-enabling a matched three-way comparison. Questions and answer sources were classified using a modified Rothwell taxonomy. Answer quality was independently evaluated by three blinded raters using the Accuracy-Safety-Hallucination (ASH) framework. Readability was assessed via the Flesch-Kincaid formula. Results: In the CTE domain, DeepSeek achieved the highest Accuracy (median 5.00, IQR 4.67-5.00) and significantly outperformed Google (median 4.00, Bonferroni-corrected p = 0.036), while ChatGPT (median 3.67) did not differ significantly from either platform. In the CRS domain, both ChatGPT (median 5.00) and DeepSeek (median 5.00) significantly outperformed Google (median 4.17; p = 0.024 and p = 0.045, respectively), with Safety significantly favoring both LLMs (Cochran's Q p = 0.018). ChatGPT and DeepSeek did not differ significantly in Accuracy in either domain. Readability analysis paradoxically favored Google (Grade Level 12.6-13.2 vs. 15.7-17.4 for LLMs), attributable to extreme snippet brevity inflating formulaic scores rather than genuine comprehensibility. Conclusion: ChatGPT and DeepSeek outperform Google Search in accuracy and safety, yet their value lies in complementary functional roles rather than direct competition. ChatGPT's policy- and education-oriented framing and strong CRS safety profile position it as a practical tool for patient education. DeepSeek's technical depth and academically concentrated sourcing make it better suited for clinical decision support and research. Google offers the highest readability and closely mirrors patient concerns but carries measurable safety risks in surgical contexts. These findings advocate for stakeholder-specific AI tool matching rather than one-size-fits-all recommendations.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.