Artificial intelligence and microbiome research: Evolution of hotspots, research trends, and thematic-based narrative review

Artificial intelligence and microbiome: A bibliometric analysis

Abstract

Artificial intelligence (AI) and microbiome have emerged in recent years as transformative fields with far-reaching implications for various biomedical domains. This paper presents a comprehensive bibliometric analysis examining the intersection of AI and the microbiome (AIM). The study aims to provide information on this interdisciplinary field's research landscape, trends, and emerging topics. Using a systematic approach, data-driven studies were extracted from the Scopus database on 23 November 2023 and analyzed using the VOSviewer and Bibliometrix applications. The regression coefficient of 0.94 and the yearly growth rate of 7.46% in AIM production indicate a consistent increase over time. Identification of essential contributors, organizations, and nations illuminated cooperative networks and research hotspots. The trend themes are the gut microbiome, disease prediction, machine learning, transfer learning, categorization, big data, artificial neural networks, chronic rhinosinusitis, epidemiology, COPD, and bronchoalveolar lavage. These hot issues in AIM reflect the present emphasis on research and developments in our knowledge of the microbiome's function in health, sickness, and individualized treatment. The findings give researchers, policymakers, and industry experts a thorough picture of the research environment and guide future paths in AIM's fascinating and promising subject.