A wealth of biomedical information and literature, complex electronic health records (EHRs), disjointed guidance, and time pressures associated with the care process are all affecting clinical decision-making. The objective of this review was to discuss the potential of generative AI-driven knowledge retrieval systems for clinical decision-making and to describe some of the technical, compliance, ethical, and implementation challenges and limitations. This purposively selected, 42-source structured narrative review with scoping review elements was conducted based on publications retrieved from PubMed, Scopus, IEEE Xplore, Google Scholar, WHO/FDA, Web of Science, and major clinical informatics journals from 2020-2026. Study results demonstrate that retrieval-augmented generation (RAG) systems can provide accurate and relevant information by grounding content generated by large language models (LLMs) in clinical guidelines, biomedical literature, drug databases, EHR data, and institutional protocols. The results showed that RAG systems yielded better biomedical system performance than the baseline LLM, with an OR of 1.35 (95% CI: 1.19, 1.53). There are potential impacts, however, such as hallucinations, incomplete retrieval, incomplete and comprehensive datasets, privacy breaches, and lack of multilingual validation. The study concludes that evidence-based, auditable, locally adaptable, and supervised by licensed clinician retrieval systems with generative AI can support safer, faster, and more relevant decision-making processes in clinical settings. Future studies should involve prospective multi-site implementation, a clear retrieval pipeline, multilingual datasets, EHR integration, ongoing monitoring, and clinical governance models to ensure safe use.