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ObjectivesLipedema is a chronic disorder characterized by pain and disproportionate fat distribution, and its diagnosis is frequently overlooked. The aim of this study was to evaluate and compare the responses generated by contemporary artificial intelligence models-ChatGPT-5o, Gemini-3, and Perplexity AI-to structured clinical questions developed in accordance with the 2024 S2k Lipedema Guideline. The models were analyzed in terms of clinical accuracy, readability, and reference reliability to assess their performance in delivering guideline-based medical information.MethodsThis cross-sectional and comparative study was conducted by submitting 30 structured clinical questions, prepared on the basis of the relevant guideline, to three large language models. Responses collected on 10 February 2026, were evaluated using a seven-point Likert scale (reliability) and a five-point scale (accuracy). Text readability was assessed using six established indices, including the Flesch Reading Ease Score (FRES), Flesch-Kincaid Grade Level (FKGL), and Gunning Fog Index (GFOG). Reference reliability was examined by analyzing hallucination tendencies as defined in the literature.ResultsA statistically significant difference in reliability was observed among the models (p = .041); Perplexity (4.95 ± 1.20) achieved significantly higher scores than ChatGPT-5o (4.38 ± 1.05) (p = .038). In readability analyses, Perplexity (12.80 ± 2.10) required a significantly higher educational level according to FKGL scores compared to both ChatGPT-5o (p = .041) and Gemini-3 (p = .036). Regarding reference reliability, ChatGPT-5o outperformed Perplexity in source verifiability (p = .031), bibliographic precision (p = .044), and total RHS scores (p = .027), emerging as the most robust model in this domain. No statistically significant differences were found among the models in terms of clinical accuracy and usefulness (p > .05). Inter-rater agreement was excellent (Kappa: 0.92-0.97).ConclusionIn this study, ChatGPT-5o distinguished itself in reference quality, whereas Perplexity demonstrated superior reliability. However, the complex linguistic structures accompanying efforts to maintain high medical accuracy may constitute a significant barrier for individuals with limited e-health literacy. Although these systems show strong potential as medical information resources, they cannot yet replace expert physician oversight in terms of patient safety. A balanced approach between technical reliability and patient-centered simplification remains necessary.

BackgroundLipedema is a chronic, progressive adipose tissue disorder affecting mainly women, characterized by bilateral, disproportionate fat accumulation in the lower extremities. The condition is often associated with pain, heaviness, and functional limitations. While the adipose tissue changes in lipedema are well-described, its impact on muscle mass, strength, and functional performance remains underexplored. This study aimed to evaluate the prevalence of sarcopenia and its relationship with lipedema severity.Materials and methodsA cross-sectional observational study was conducted on 48 women with clinically diagnosed lower-extremity lipedema. Diagnosis followed the International Lipoedema Association and German S2k guidelines. Sarcopenia was assessed using a multidimensional approach, including ultrasonographic rectus femoris thickness, handgrip strength, the Five Times Sit-to-Stand Test, and four-m walking speed. The lipedema stage was determined using morphological criteria. Statistical analyses evaluated the relationships between sarcopenia, functional parameters, and lipedema stage.ResultsParticipants had a mean age of 47.2 ± 8.4 years and a BMI of 33.0 ± 4.3 kg/m2. Sarcopenia was identified in 33.3% of participants, with 14.6% classified as severe. Those with sarcopenia exhibited lower rectus femoris thickness and slower walking speed (p < .05). Advancing lipedema stage correlated with reduced muscle thickness, weaker handgrip strength, slower gait, and prolonged Five Times Sit-to-Stand Test duration (p < .05). Stage 3 patients demonstrated the highest prevalence of sarcopenia, indicating progressive impairment in muscle mass and functional performance with disease severity (p < .05). No significant associations were found between age or BMI and muscle parameters (p > .05).ConclusionsSarcopenia is prevalent in women with lower-extremity lipedema and increases with disease stage. Comprehensive musculoskeletal assessment should be integrated into lipedema management to address functional impairment and optimize patient care.

ObjectivesGenerative artificial intelligence (AI) models capable of producing photorealistic medical images are increasingly proposed for patient education, clinical illustration, and trainee instruction. However, their ability to accurately represent anatomically distinct disease subtypes remains unclear. This study evaluated the diagnostic accuracy of a widely used generative AI model in producing images corresponding to the five anatomical lipedema types defined by the Schmeller classification.MethodsIn this prospective audit, ChatGPT’s image-generation interface was prompted to create 60 images for each lipedema type (Types I–V),yielding 300 images. Prompts were standardized and limited to the subtype label without additional descriptors. Two clinicians independently classified each image into one of the five lipedema types or as indeterminate, blinded to the original prompt; disagreements were resolved by a third clinician. Diagnostic performance was assessed using a confusion matrix and per-type sensitivity, specificity, positive predictive value(PPV), negative predictive value (NPV),F1-score,and one-vs-rest receiver operating characteristic area under the curve (ROC AUC). Overall accuracy and Cohen’s κ statistics were also calculated.ResultsAll 300 images were evaluable. The model generated anatomically consistent images for Types I,II, and III (sensitivity = 1.00 for each). Specificity was 1.00 for Types I and II but 0.50 for Type III because all images requested as Types IV and V were classified as Type III. Consequently, the model failed to generate any images consistent with Type IV(arm-predominant) or Type V(calf-isolated) lipedema (sensitivity = 0.00 for both). Overall accuracy was 0.600. Unweighted and quadratic-weighted Cohen’s κ values were 0.500 and 0.667, respectively. Micro- and macro-averaged ROC AUC were both 0.750.ConclusionThe model reproduces severity gradients within lower-extremity lipedema but systematically collapses anatomically distinct subtypes into the dominant Type III phenotype, failing to depict arm-predominant and calf-isolated disease. Current generative AI systems may therefore encode lipedema as a single visual phenotype rather than a distributed anatomical entity, limiting their reliability for medical education and clinical communication.