Background Large language models such as ChatGPT, DeepSeek, and Gemini are increasingly used to generate patient-facing medical content. While their factual accuracy has been explored, the readability of these outputs remains less well understood. Readability is a crucial component of health communication, particularly for older adults and those with lower health literacy. This study aimed to evaluate and compare the readability of patient information leaflets generated by three large language models - ChatGPT, DeepSeek, and Gemini - on the topics of Alzheimer's disease, vascular dementia, and delirium, using five validated readability metrics. Materials and methods We conducted a cross-sectional comparative study of patient information leaflets generated by three large language models on the topics of Alzheimer’s disease, vascular dementia, and delirium. Each model was prompted using identical queries, and the resulting texts were evaluated using five established readability metrics: Flesch Reading Ease, Flesch-Kincaid Grade Level, Gunning Fog Index, Simple Measure of Gobbledygook (SMOG) Index, and Automated Readability Index. Readability scores were compared using Kruskal-Wallis tests to identify statistically significant differences between models. Results ChatGPT consistently produced the most readable content, with the highest Flesch Reading Ease scores and the lowest grade-level indices. DeepSeek generated text that was markedly more complex and less accessible. Gemini performed intermediately, sometimes matching ChatGPT in specific indices but not consistently across all metrics. The difference in Flesch Reading Ease scores between models was statistically significant (H = 7.20, p = 0.027). Other metrics showed trends that approached significance. Conclusions There are meaningful differences in the readability of patient information generated by different large language models. ChatGPT appears to produce content that is more suitable for patient understanding, particularly in the context of older adult care. These findings highlight the need for careful evaluation of readability when using generative AI in clinical communication. Future research should incorporate expert review of content accuracy and appropriateness alongside readability.

Background/Objectives: Avascular necrosis of the femoral head is a debilitating condition that, if left untreated, leads to progressive arthritis necessitating total hip replacement (THR). In the younger adult population, there is a drive towards joint-preserving procedures, particularly where alternative techniques such as core decompression or vascularised bone grafting are anticipated to fail. Proximal femoral osteotomy is a technique that aims to remove the necrotic segment from the weight bearing area. The presented review aims to examine the efficacy of this technique in the management of avascular necrosis of the femoral head, reporting both rates of conversion to total hip replacement and patient reported outcomes. Methods: This systematic review was conducted according to PRISMA guidelines. A search was conducted of PubMed, Ovid Medline, EMBASE, and the Cochrane Library using pre-defined search terms. Data were extracted, and descriptive data presented. Quality of each study was assessed using the NIH quality assessment tool for case series studies. Results: Fifty-three studies with data for 2686 osteotomies are presented. Progression of radiological arthrosis was present in 40% of cases, with 20.3% of patients having undergone conversion to THR at a mean of 75.4 months (range 20–132 months). Patient-reported outcome measures were recorded in 1416 patients, of which the Harris Hip Score was the most commonly utilised. This score improved from a mean of 58.3 to 84.4 at a mean follow-up of 102 months. Conclusions: Osteotomy represented a valid head-preserving technique in the armamentarium against avascular necrosis of the femoral head, with conversion to THR required in 20.3% of patients at 7 years. In those patients who did not require THR, PROMS were similar to the arthroplasty population.

Introduction: Readable patient information is central to informed consent, shared decision-making, and treatment adherence. With the emergence of large language models (LLMs), such as ChatGPT, Gemini, and DeepSeek, there is growing interest in their role in generating health education content. However, the readability of such AI-generated patient information leaflets (PILs) has not been systematically compared with that of professionally authored materials. Objective: This study aimed to compare the readability of PILs generated by three generative artificial intelligence (AI) platforms with those produced by the Royal College of Surgeons of England (RCS England) for three common orthopedic procedures: Carpal tunnel release, total hip replacement, and total knee replacement. Materials and Methods: A total of 12 PILs (four per procedure) were analyzed using five validated readability metrics: Flesch reading ease, Flesch-Kincaid grade level, Gunning Fog Index, simple measure of Gobbledygook (SMOG) Index, and Coleman-Liau Index. Each AI model was prompted with a standardized instruction to generate a leaflet for the specified procedure. The RCS England leaflets served as the professional benchmark. Results: Across all metrics and procedures, RCS England leaflets demonstrated superior readability, with Flesch Reading Ease scores above 70 and Flesch-Kincaid Grade Levels between 5.52 and 7.15. In contrast, AI-generated leaflets frequently exceeded recommended complexity thresholds, with Grade Levels often above 12 and Gunning Fog and SMOG scores indicating post-secondary reading requirements. ChatGPT outputs were the most linguistically complex, while Gemini and DeepSeek produced intermediate but still suboptimal readability. Conclusion: While LLMs offer promising avenues for scalable health communication, current AI-generated PILs do not consistently meet recommended readability standards. Professionally authored leaflets remain more accessible for the average patient. These findings highlight the ongoing need for clinician oversight and quality assurance when integrating AI into patient education materials. Keywords: Patient information leaflets, readability, artificial intelligence, large language models, health literacy, orthopedic surgery, ChatGPT, Gemini, Deepseek.

Background Artificial intelligence (AI) and large language models (LLMs) offer transformative potential in healthcare communication, with the National Health Service (NHS) Long Term Plan envisioning digital tools to support accessible, patient-centred information. However, whether LLM-generated health materials are sufficiently readable for patient use remains uncertain, particularly in oral medicine, where conditions like xerostomia, oral candidiasis, and sialolithiasis are common. Objective This study compared the readability of patient information leaflets generated by three LLMs with the NHS UK patient leaflets on common oral medicine conditions, to assess their suitability for public health communication. Methods A cross-sectional analysis was conducted, in which each LLM was prompted to produce patient leaflets for xerostomia, oral candidiasis, and sialolithiasis. Outputs were compared to the NHS UK leaflets on identical topics. Texts were analysed using established readability metrics: Flesch Reading Ease Score (FRES), Flesch-Kincaid Grade Level (FKGL), and Gunning Fog Index. Results were summarised descriptively without formal statistical testing due to the exploratory study design. Results The NHS UK leaflets consistently demonstrated superior readability across all conditions and metrics, with lower FKGL scores (5.9-6.3) and higher FRES scores (70.5-72.4), indicating suitability for readers aged 11-14 years (Key Stage 3). Among LLMs, ChatGPT produced the most readable outputs, with FKGL scores ranging from 6.8 to 7.2. DeepSeek outputs were moderately more complex (FKGL: 8.3-8.7), while Gemini generated the most complex texts (FKGL: 9.7-10.2), often exceeding recommended reading levels for patient materials. Conclusion While LLMs, especially ChatGPT, show promise in generating patient information, their outputs remain less readable than professionally authored NHS materials. Given that nearly half of UK adults may struggle with complex health texts, the higher reading levels required for LLM-generated content could impede patient understanding and exacerbate health inequalities. As AI becomes more integrated into healthcare communication, ensuring that AI-generated materials meet established readability standards is essential to support equitable, patient-centred care.