ArticleCureus2025
Comparing Real and ChatGPT-Generated Radiographs for Training Deep Learning Models to Diagnose Knee Osteoarthritis.
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
introductionOsteoarthritis (OA) is a degenerative joint disease characterized by progressive cartilage loss, bone remodeling, and chronic pain. The growing global burden of OA motivates the evaluation of artificial intelligence (AI) approaches for automating radiographic diagnosis. PURPOSE: This study aimed to compare AI models trained on real radiographs, ChatGPT-generated radiographs, and a combined dataset to assess whether synthetic imaging can improve OA detection.
methodsThree binary classifiers were trained using knee radiographs: Model A (ChatGPT-generated images only), Model B (real images only), and Model C (real + synthetic). All models were developed using PyTorch in Google Colab and evaluated on 1,656 held-out real radiographs. Performance metrics (accuracy, sensitivity, specificity, precision, F1 score, and AUROC (area under the receiver operating characteristic)) were computed. Between-model comparisons used two-sided McNemar's tests on paired predictions; 95% confidence intervals were estimated by bootstrap resampling. Grade-specific comparisons were Holm-Bonferroni adjusted (with unadjusted p-values also reported).
resultsModels B and C outperformed Model A across overall performance, while Model A showed higher specificity. Model C demonstrated slightly higher discrimination than Model B (AUROC 0.782 vs 0.758), with overlapping 95% confidence intervals. Sensitivity for grade 1 and grade 4 OA was higher for Model C than for Model B in unadjusted comparisons, but these differences did not remain statistically significant after Holm-Bonferroni adjustment.
conclusionChatGPT-generated radiographs alone were insufficient for reliable training of OA diagnostic models. When used as a supplement to real radiographs, synthetic images produced small, directionally favorable changes in discrimination and grade-specific sensitivity, supporting their use as an adjunct for dataset expansion rather than a replacement for clinical imaging.
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