Fecal microbiota transplantation (FMT) is a gut microbial-modulation strategy that has been investigated for the treatment of a variety of human diseases, including obesity-associated metabolic disorders. This study appraises current literature and provides an overview of the effectiveness and limitations of FMT as a potential therapeutic strategy for obesity and metabolic syndrome (MS). Five electronic databases and two gray literature sources were searched up to 10 December 2018. All interventional and observational studies that contained information on the relevant population (adult patients with obesity and MS), intervention (receiving allogeneic FMT) and outcomes (metabolic parameters) were eligible. From 1096 unique citations, three randomized placebo-controlled studies (76 patients with obesity and MS, body mass index = 34.8 ± 4.1 kg/m², fasting plasma glucose = 5.8 ± 0.7 mmol/L) were included for review. Studies reported mixed results with regards to improvement in metabolic parameters. Two studies reported improved peripheral insulin sensitivity (rate of glucose disappearance, RD) at 6 weeks in patients receiving donor FMT versus patients receiving the placebo control. In addition, one study observed lower HbA1c levels in FMT patients at 6 weeks. No differences in fasting plasma glucose, hepatic insulin sensitivity, body mass index (BMI), or cholesterol markers were observed between two groups across all included studies. While promising, the influence of FMT on long-term clinical endpoints needs to be further explored. Future studies are also required to better understand the mechanisms through which changes in gut microbial ecology and engraftment of microbiota affect metabolic outcomes for patients with obesity and MS. In addition, further research is needed to better define the optimal fecal microbial preparation, dosing, and method of delivery.

The formulation of clinical recommendations pertaining to bariatric surgery is essential in guiding healthcare professionals. However, the extensive and continuously evolving body of literature in bariatric surgery presents considerable challenge for staying abreast of latest developments and efficient information acquisition. Artificial intelligence (AI) has the potential to streamline access to the salient points of clinical recommendations in bariatric surgery. The study aims to appraise the quality and readability of AI-chat-generated answers to frequently asked clinical inquiries in the field of bariatric and metabolic surgery. Remote. Question prompts inputted into AI large language models (LLMs) and were created based on pre-existing clinical practice guidelines regarding bariatric and metabolic surgery. The prompts were queried into 3 LLMs: OpenAI ChatGPT-4, Microsoft Bing, and Google Bard. The responses from each LLM were entered into a spreadsheet for randomized and blinded duplicate review. Accredited bariatric surgeons in North America independently assessed appropriateness of each recommendation using a 5-point Likert scale. Scores of 4 and 5 were deemed appropriate, while scores of 1-3 indicated lack of appropriateness. A Flesch Reading Ease (FRE) score was calculated to assess the readability of responses generated by each LLMs. There was a significant difference between the 3 LLMs in their 5-point Likert scores, with mean values of 4.46 (SD .82), 3.89 (.80), and 3.11 (.72) for ChatGPT-4, Bard, and Bing (P < .001). There was a significant difference between the 3 LLMs in the proportion of appropriate answers, with ChatGPT-4 at 85.7%, Bard at 74.3%, and Bing at 25.7% (P < .001). The mean FRE scores for ChatGPT-4, Bard, and Bing, were 21.68 (SD 2.78), 42.89 (4.03), and 14.64 (5.09), respectively, with higher scores representing easier readability. LLM-based AI chat models can effectively generate appropriate responses to clinical questions related to bariatric surgery, though the performance of different models can vary greatly. Therefore, caution should be taken when interpreting clinical information provided by LLMs, and clinician oversight is necessary to ensure accuracy. Future investigation is warranted to explore how LLMs might enhance healthcare provision and clinical decision-making in bariatric surgery.