ReviewMolecules (Basel, Switzerland)2026
Mapping the Convergence of Frontier Technologies for Major Environmental Challenges: A Chemical and Molecular Perspective on the Use of AI for Climate Action and Antimicrobial Resistance.
Review in Molecules (Basel, Switzerland), 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
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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.
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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.
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0 citing papers in PubMed.
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The planet faces the critical interconnected challenges of climate change and antimicrobial resistance (AMR); these two crises mutually reinforce each other, threatening global health and ecosystem stability. This study conducts a systematic documentary analysis to map the convergence and identify the structural gaps between two key technological domains: artificial intelligence (AI) for climate action and molecular methods for AMR. The methodology was based on a corpus of 179 scientific documents indexed in Scopus (2010-2025), analyzed with data science tools to identify trends, collaborations, and impact. Quantitative results revealed clear leadership by the United States, accounting for 37.4% of publications, followed by China (26.8%); this leadership reflects the concentration of high-throughput molecular surveillance infrastructure and data science clusters essential for monitoring the environmental resistome. In terms of scientific impact, Spain showed the highest average, with 32.8 citations per article. The most influential work, a review on food security and sustainability, accumulated 275 citations. Network analysis identified authors such as Zhu, Yongguan, with 240 citations in total, as central nodes in international collaborations. Thematically, metagenomics and machine learning emerged as mature and interconnected research cores. This analysis confirms a solid yet still fragmented relationship between the two fields. The analysis reveals that, while metagenomic tools dominate the current literature, a gap persists in correlating genotypic resistance potential with functional phenotypic expression under changing climatic stressors. The results confirm a solid yet still fragmented foundation, highlighting the need for hybrid platforms that transition from descriptive bibliometrics to functional integration for designing systemic solutions. Future work should prioritize the development of hybrid platforms, such as intelligent biosensors, and collaborative governance frameworks that accelerate effective responses to these dual crises.
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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.