ReviewMedical gas research2026
Clinical applications of artificial intelligence-driven nitric oxide: a bibliometric and scientific mapping analysis.
Review in Medical gas research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.