Retrieval-augmented generation (RAG) is a powerful technique in artificial intelligence (AI) and machine learning that enhances the capabilities of large language models (LLMs) by integrating external data sources, allowing for more accurate, contextually relevant responses. In medical applications, RAG has the potential to improve diagnostic accuracy, clinical decision support, and patient care. This narrative review explores the application of RAG across various medical domains, including guideline interpretation, diagnostic assistance, clinical trial eligibility screening, clinical information retrieval, and information extraction from scientific literature. Studies highlight the benefits of RAG in providing accurate, up-to-date information, improving clinical outcomes, and streamlining processes. Notable applications include GPT-4 models enhanced with RAG to interpret hepatologic guidelines, assist in differential diagnosis, and aid in clinical trial screening. Furthermore, RAG-based systems have demonstrated superior performance over traditional methods in tasks such as patient diagnosis, clinical decision-making, and medical information extraction. Despite its advantages, challenges remain, particularly in model evaluation, cost-efficiency, and reducing AI hallucinations. This review emphasizes the potential of RAG in advancing medical AI applications and advocates for further optimization of retrieval mechanisms, embedding models, and collaboration between AI researchers and healthcare professionals to maximize RAG's impact on medical practice.

Myo‐inositol may be efficient to improve sperm parameters to increase the chance of fertility. Although, the data are controversial. This study aimed to assess the impact of Myo‐inositol supplements on semen quality and in vitro fertilization (IVF) outcomes. In this systematic review and meta‐analysis, a comprehensive search was conducted in PubMed, Web of Science, and Embase. The objective was to identify relevant human studies that investigated the effects of Myo‐inositol treatment on various sperm factors, such as sperm motility, sperm concentration, sperm morphology, viable spermatozoa, spermatozoa with DNA fragmentation, and pregnancy rate. Additionally, the testosterone levels of patients with Oligo‐astheno‐teratozoospermia (OAT) after Myo‐inositol application were considered. The findings of 16 selected studies from 2240 citations indicated significant improvements in several parameters of sperm after Myo‐inositol administration. Myo‐inositol treatment was associated with a notable increase in total sperm motility (SMD 0.90; 95% CI: 0.34 to 1.46; I ² = 0%, p = .001) and progressive sperm motility (SMD 1.48; 95% CI: 0.37 to 2.59; I ² = 0%, p = .008). Additionally, there was a significant improvement in testosterone levels (SMD 0.54; 95% CI: 0.34 to 0.73; I ² = 0%, p < .0001). Furthermore, Myo‐inositol therapy demonstrated a significant decrease in spermatozoa with DNA fragmentation (SMD −1.37; 95% CI: −2.43 to −0.32; I ² = 85%, p = .01). This study suggests that Myo‐inositol therapy has a positive impact on specific sperm parameters, such as total and progressive sperm motility, along with testosterone levels. These findings provide support for the potential benefits of Myo‐inositol in improving male fertility parameters related to sperm factors.

In the evolving field of neurological healthcare, deep learning technologies are gaining recognition for their potential to enhance diagnostic accuracy. Transformer-based models, particularly large language models (LLMs) such as OpenBioLLM, have shown promise in processing large datasets typical of neurological assessments. This study evaluates the diagnostic capabilities of OpenBioLLM in the realm of neurological conditions. The primary aim of this research is to assess the diagnostic accuracy, comprehensiveness, supplementation, and fluency of OpenBioLLM when applied to complex neurological case studies. Twenty-five complex neurology cases were selected from “Clinical Cases in Neurology.” OpenBioLLM was used to generate diagnoses and rationales for each case. Two independent medical doctors evaluated the responses based on accuracy, comprehensiveness, supplementation, and fluency, with discrepancies resolved by a third assessor. Statistical analyses included one-way ANOVA, Bartlett’s test, and Spearman’s rank correlation. OpenBioLLM achieved a mean accuracy score of 38%, a comprehensiveness score of 52%, a supplementation score of 24%, and a fluency score of 100%. The model could localize neurological lesions but often struggled with identifying the correct pathophysiological causes. Accuracy scores did not significantly vary by neurological disorder type. While OpenBioLLM shows potential in diagnosing neurological conditions, its performance metrics suggest it is not yet a reliable standalone tool. Future research should focus on fine-tuning the model and improving its reasoning capabilities to enhance diagnostic accuracy.