ReviewFrontiers in microbiology2025
AI-driven optimization of bioremediation strategies for river pollution: a comprehensive review and future directions.
Review in Frontiers in microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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
6 citing papers in PubMed.
- Multi-omics insights into bacterial and fungal bioremediation of Potentially Toxic Elements (PTEs): a critical overview of their applications.World journal of microbiology & biotechnology · 2026Review
- Microbial consortia for pesticide biodegradation: mechanisms, cross-class pathways, and translational challenges.Biodegradation · 2026Review
- Harnessing bacterial power and omics technologies for sustainable plastic waste biodegradation.Biodegradation · 2026Review
- Editorial for Special Issue "Biotechnology for Environmental Remediation".Microorganisms · 2026Article
- Integrative Bioinformatics Approaches in Environmental Biotechnology: A Review.TheScientificWorldJournal · 2026Review
- Machine learning approaches for data-driven hydrocarbon bioaugmentation and phytoremediation: the role of multi-omics insights.Frontiers in microbiology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This narrative review explores the transformative potential of artificial intelligence (AI) in optimizing bioremediation systems for river pollution control while addressing the challenges and limitations associated with its implementation. The review begins by examining traditional and emerging bioremediation methods, highlighting their limitations and the pressing need for innovative solutions. It then delves into the application of AI technologies in pollution monitoring and bioremediation optimization, providing examples and success stories from existing studies. The challenges of AI-driven bioremediation, including ethical concerns, technological constraints, and the need for responsible deployment, are critically analyzed. Emphasis is placed on fostering interdisciplinary collaboration to overcome these barriers. The review also presents future directions and actionable recommendations, including integrating AI with traditional approaches, addressing technological and policy gaps, and ensuring sustainable management of river ecosystems. Ultimately, this review stresses the revolutionary potential of AI in enhancing bioremediation systems and advocates for urgent action to address the challenges involved, paving the way for sustainable and effective river pollution control strategies.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.