We analyzed the medical condition of 360 women affected by lipedema of the lower limbs in stages 1, 2, and 3. The data were assessed for the whole population and compared between different clinical stages, distinguishing between obese and non-obese patients. The most frequent clinical signs were pain when pinching the skin, subcutaneous nodules, and patellar fat pads. The most frequently painful site of the lower limbs was the medial lower third of the thigh. The pain score obtained on lower limb points increased progressively with the clinical stage. In all points evaluated, the thickness of the subcutaneous tissue increased with the clinical stage. Analyzing the data on the lower medial third of the leg and considering only patients with type 3 lipedema, the difference between stages was statistically significant after correction for age and BMI. We found higher levels of C-reactive protein at more severe clinical stages, and the difference was significant after correction for age and BMI between the stages. Overall, the prevalence of alterations of glucose metabolism was 34%, with a progressive increase in prevalence with the clinical stage. The most frequent comorbidities were vitamin D insufficiency, chronic venous disease, allergies, dyslipidemia, headache, and depression of mood. Interestingly, in comparison with the general population, we found higher prevalence of chronic autoimmune thyroiditis and polycystic ovary syndrome. Finally, the clinical stage and the involvement of the upper limbs or obesity suggest a worse clinical, anthropometric, and endocrine–metabolic profile.

Background: In the evolving field of maxillofacial surgery, integrating advanced technologies like Large Language Models (LLMs) into medical practices, especially for trauma triage, presents a promising yet largely unexplored potential. This study aimed to evaluate the feasibility of using LLMs for triaging complex maxillofacial trauma cases by comparing their performance against the expertise of a tertiary referral center. Methods: Utilizing a comprehensive review of patient records in a tertiary referral center over a year-long period, standardized prompts detailing patient demographics, injury characteristics, and medical histories were created. These prompts were used to assess the triage suggestions of ChatGPT 4.0 and Google GEMINI against the center’s recommendations, supplemented by evaluating the AI’s performance using the QAMAI and AIPI questionnaires. Results: The results in 10 cases of major maxillofacial trauma indicated moderate agreement rates between LLM recommendations and the referral center, with some variances in the suggestion of appropriate examinations (70% ChatGPT and 50% GEMINI) and treatment plans (60% ChatGPT and 45% GEMINI). Notably, the study found no statistically significant differences in several areas of the questionnaires, except in the diagnosis accuracy (GEMINI: 3.30, ChatGPT: 2.30; p = 0.032) and relevance of the recommendations (GEMINI: 2.90, ChatGPT: 3.50; p = 0.021). A Spearman correlation analysis highlighted significant correlations within the two questionnaires, specifically between the QAMAI total score and AIPI treatment scores (rho = 0.767, p = 0.010). Conclusions: This exploratory investigation underscores the potential of LLMs in enhancing clinical decision making for maxillofacial trauma cases, indicating a need for further research to refine their application in healthcare settings.

This study evaluates the quality and readability of informed consent documents generated by AI platforms ChatGPT-4 and Bard Gemini Advanced compared to those written by a first-year oral surgery resident for common oral surgery procedures. The evaluation, conducted by 18 experienced oral and maxillofacial surgeons, assessed consents for accuracy, completeness, readability, and overall quality. ChatGPT-4 consistently outperformed both Bard and human-written consents. ChatGPT-4 consents had a median accuracy score of 4 [IQR 4-4], compared to Bard's 3 [IQR 3-4] and human's 4 [IQR 3-4]. Completeness scores were higher for ChatGPT-4 (4 [IQR 4-5]) than Bard (3 [IQR 3-4]) and human (4 [IQR 3-4]). Readability was also superior for ChatGPT-4, with a median score of 4 [IQR 4-5] compared to Bard and human consents, both at 4 [IQR 4-4] and 4 [IQR 3-4], respectively. The Gunning Fog Index for ChatGPT-4 was 17.2 [IQR 16.5-18.2], better than Bard's 23.1 [IQR 20.5-24.7] and the human consents' 20 [IQR 19.2-20.9]. Overall, ChatGPT-4's consents received the highest quality ratings, underscoring AI's potential in enhancing patient communication and the informed consent process. The study suggests AI can reduce misinformation risks and improve patient understanding, but continuous evaluation, oversight, and patient feedback integration are crucial to ensure the effectiveness and appropriateness of AI-generated content in clinical practice.