Clinical applications of artificial intelligence-driven nitric oxide: a bibliometric and scientific mapping analysis

JOURNAL/mgres/04.03/01612956-202609000-00002/figure1/v/2026-01-06T135433Z/r/image-tiff Nitric oxide, a pivotal endogenous signaling molecule, plays crucial roles in cardiovascular regulation, immune response, and neuromodulation. The rapid advancement of artificial intelligence technologies offers novel approaches to optimize real-time nitric oxide monitoring, dosing regimens, and toxicity prediction. Current interdisciplinary research on the artificial intelligence-driven nitric oxide intersection remains fragmented, with a lack of systematic investigations into knowledge architecture, technological evolution, and translational barriers. This study addressed this critical gap by presenting the knowledge graph-based analysis of artificial intelligence-driven nitric oxide system. A total of 384 relevant articles (2005-2024) were retrieved in the Web of Science Core Collection and analyzed using CiteSpace, VOSviewer, and Bibliometrix R package. Annual publications demonstrated a biphasic growth, accelerating after 2017 in tandem with breakthroughs in artificial intelligence architectures. Although China and the United States were dominated in this field, international collaborations exhibited a core-periphery structure. Research themes predominantly focused on cardiovascular and respiratory diseases, with underdeveloped applications in neuroimmunology and infectious diseases. Highly cited literature that emphasized photodynamic therapy and disease risk assessment revealed insufficient integration between artificial intelligence algorithms and fundamental nitric oxide mechanisms. Keyword evolution analysis identified a paradigm shift from traditional mechanisms (e.g., "blood pressure," "inflammation") to technology-driven approaches (e.g., "machine learning, " "deep learning"). Clinical translation has faced challenges, including data heterogeneity, algorithm interpretability, and deficiencies in multicenter validation. This pioneering study systematically delineates the knowledge framework and translational bottlenecks in artificial intelligence-driven nitric oxide convergence. Future research should prioritize artificial intelligence modeling of nitric oxide dynamic metabolism, the development of explainable algorithms, and prospective clinical trials to bridge the laboratory-to-clinic gap.