ArticleBMC nursing2026
Artificial intelligence for environmental sustainability across sectors: a scoping review and evidence map for nursing and health policy.
Article in BMC nursing, 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.
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Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHealth systems are expected to reduce environmental impacts while preserving safe, continuous and equitable care. Artificial intelligence (AI) may help optimize energy, water, waste and circular-economy processes, but the evidence is dispersed across sectors and its relevance to nursing and health policy is uncertain.
methodsWe conducted a cross-sector scoping review and evidence map of studies published from January 2020 to April 2025. Searches were conducted in Scopus and Web of Science, with backward reference-list checking. Eligible studies evaluated an AI-enabled intervention, decision-support pathway or optimization process and reported a quantified environmental sustainability endpoint, sustainability-relevant operational proxy or clearly linked circular-economy process outcome. Outcomes were mapped as real-world direct environmental endpoints, simulated or modeled direct endpoints, indirect operational proxies or technical enabling metrics. Full platform-specific search strategies are provided in Supplementary file 1 (Supplementary Table S1).
resultsAfter a tightened eligibility audit, 11 studies were included. No study evaluated AI-enabled sustainability interventions in routine healthcare delivery or nursing practice. Energy-management studies were concentrated in building heating, ventilation and air-conditioning control or microgrid scheduling. Direct environmental evidence was sparse and was either non-healthcare real-building evidence or simulated/modeled estimates, including building energy use, irrigation water use and route-derived emissions or carbon-cost indicators. Indirect evidence included demand-response flexibility, operating-cost reductions, productivity or profit improvements, operational-efficiency gains, battery reuse-pack performance and bioleaching process optimization. Technical metrics such as forecasting or classification accuracy were treated as implementation-enabling evidence and were not interpreted as environmental effects.
conclusionsThe current evidence base is better characterized as a cross-sector evidence map than as a focused healthcare effectiveness review. AI-enabled sustainability applications may offer candidate functions for health-system infrastructure and operations; however, nursing implications are interpretive, hypothesis-generating and not directly evidenced. Future healthcare studies should evaluate environmental endpoints alongside patient safety, infection prevention, nursing workload, equity and the lifecycle footprint of AI systems.
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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.