Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating external knowledge retrieval to improve factual consistency and reduce hallucinations. Despite growing interest, its use in healthcare remains fragmented. This paper presents a Systematic Literature Review (SLR) following PRISMA guidelines, synthesizing 30 peer-reviewed studies on RAG in clinical domains, focusing on three of its most prevalent and promising applications in diagnostic support, electronic health record (EHR) summarization, and medical question answering. We synthesize the existing architectural variants (naïve, advanced, and modular) and examine their deployment across these applications. Persistent challenges are identified, including retrieval noise (irrelevant or low-quality retrieved information), domain shift (performance degradation when models are applied to data distributions different from their training set), generation latency, and limited explainability. Evaluation strategies are compared using both standard metrics and clinical-specific metrics, FactScore, RadGraph-F1, and MED-F1, which are particularly critical for ensuring factual accuracy, medical validity, and clinical relevance. This synthesis offers a domain-focused perspective to guide researchers, healthcare providers, and policymakers in developing reliable, interpretable, and clinically aligned AI systems, laying the groundwork for future innovation in RAG-based healthcare solutions.

ChatGPT, developed by OpenAI, is a large language model (LLM) that leverages artificial intelligence (AI) and deep learning (DL) to generate human-like responses. This paper provides a broad, systematic review of ChatGPT’s applications in healthcare, particularly in enhancing patient engagement through medical history collection, symptom assessment, and decision support for improved diagnostic accuracy. It assesses ChatGPT’s potential across multiple organ systems and specialties, highlighting its value in clinical, educational, and administrative contexts. This analysis reveals both the benefits and limitations of ChatGPT, including health literacy promotion and support for clinical decision-making, alongside challenges such as the risk of inaccuracies, ethical considerations around informed consent, and regulatory hurdles. A quantified summary of key findings shows ChatGPT’s promise in various applications while underscoring the risks associated with its integration in medical practice. Through this comprehensive approach, this review aims to provide healthcare professionals, researchers, and policymakers with a balanced view of ChatGPT’s potential and limitations, emphasizing the need for ongoing updates to keep pace with evolving medical knowledge.

Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating external knowledge retrieval to improve factual consistency and reduce hallucinations. Despite growing interest, its use in healthcare remains fragmented. This review fills that gap through an analysis of 30 peer-reviewed studies on RAG in clinical domains, focusing on three of its most prevalent and promising applications in diagnostic support, electronic health record (EHR) summarization, and medical question answering. We synthesize existing architectural variants (naïve, advanced, and modular) and examine their deployment across these applications. Persistent challenges are identified, including retrieval noise (irrelevant or low-quality retrieved information), domain shift (performance degradation when models are applied to data distributions different from their training set), generation latency, and limited explainability. Evaluation strategies are compared using both standard metrics and clinical-specific metrics, FactScore, RadGraph-F1, and MED-F1, which are particularly critical for ensuring factual accuracy, medical validity, and clinical relevance. This synthesis offers a domain-focused perspective to guide researchers, healthcare providers, and policymakers in developing reliable, interpretable, and clinically aligned AI systems, laying the groundwork for future innovation in RAG-based healthcare solutions.

Kidney disease poses a significant global health challenge, affecting millions and straining healthcare systems due to limited nephrology resources. This paper examines the transformative potential of Generative AI (GenAI), Large Language Models (LLMs), and Large Vision Models (LVMs) in addressing critical challenges in kidney care. GenAI supports research and early interventions through the generation of synthetic medical data. LLMs enhance clinical decision-making by analyzing medical texts and electronic health records, while LVMs improve diagnostic accuracy through advanced medical image analysis. Together, these technologies show promise for advancing patient education, risk stratification, disease diagnosis, and personalized treatment strategies. This paper highlights key advancements in GenAI, LLMs, and LVMs from 2018 to 2024, focusing on their applications in kidney care and presenting common use cases. It also discusses their limitations, including knowledge cutoffs, hallucinations, contextual understanding challenges, data representation biases, computational demands, and ethical concerns. By providing a comprehensive analysis, this paper outlines a roadmap for integrating these AI advancements into nephrology, emphasizing the need for further research and real-world validation to fully realize their transformative potential.

Radiomics enables quantitative medical image analysis by converting imaging data into structured, high-dimensional feature representations for predictive modeling. Despite methodological developments and encouraging retrospective results, radiomics continue to face persistent challenges related to feature instability, limited reproducibility, validation bias, and restricted clinical translation. Existing reviews largely focus on application-specific outcomes or isolated pipeline components, with limited analysis of how interdependent design choices across acquisition, preprocessing, feature engineering, modeling, and evaluation collectively affect robustness and generalizability. This survey provides an end-to-end analysis of radiomics pipelines, examining how methodological decisions at each stage influence feature stability, model reliability, and translational validity. This paper reviews radiomic feature extraction, selection, and dimensionality reduction strategies; classical machine and deep learning–based modeling approaches; and ensemble and hybrid frameworks, with emphasis on validation protocols, data leakage prevention, and statistical reliability. Clinical applications are discussed with a focus on evaluation rigor rather than reported performance metrics. The survey identifies open challenges in standardization, domain shift, and clinical deployment, and outlines future directions such as hybrid radiomics–artificial intelligence models, multimodal fusion, federated learning, and standardized benchmarking.