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| Main Authors: | , , , , |
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| Format: | Artículo científico |
| Language: | en |
| Published: |
Biology
2025
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| Online Access: | https://pubmed.ncbi.nlm.nih.gov/40427709/ |
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| _version_ | 1868266198737616896 |
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| author | Miller, Tymoteusz Michoński, Grzegorz Durlik, Irmina Kozlovska, Polina Biczak, Paweł |
| author_facet | Miller, Tymoteusz Michoński, Grzegorz Durlik, Irmina Kozlovska, Polina Biczak, Paweł Miller, Tymoteusz Michoński, Grzegorz Durlik, Irmina Kozlovska, Polina Biczak, Paweł |
| collection | PubMed - marine biology |
| contents | Artificial Intelligence in Aquatic Biodiversity Research: A PRISMA-Based Systematic Review. Miller, Tymoteusz Michoński, Grzegorz Durlik, Irmina Kozlovska, Polina Biczak, Paweł Freshwater ecosystems are increasingly threatened by climate change and anthropogenic activities, necessitating innovative and scalable monitoring solutions. Artificial intelligence (AI) has emerged as a transformative tool in aquatic biodiversity research, enabling automated species identification, predictive habitat modeling, and conservation planning. This systematic review follows the PRISMA framework to analyze AI applications in freshwater biodiversity studies. Using a structured literature search across Scopus, Web of Science, and Google Scholar, we identified 312 relevant studies published between 2010 and 2024. This review categorizes AI applications into species identification, habitat assessment, ecological risk evaluation, and conservation strategies. A risk of bias assessment was conducted using QUADAS-2 and RoB 2 frameworks, highlighting methodological challenges, such as measurement bias and inconsistencies in the model validation. The citation trends demonstrate exponential growth in AI-driven biodiversity research, with leading contributions from China, the United States, and India. Despite the growing use of AI in this field, this review also reveals several persistent challenges, including limited data availability, regional imbalances, and concerns related to model generalizability and transparency. Our findings underscore AI's potential in revolutionizing biodiversity monitoring but also emphasize the need for standardized methodologies, improved data integration, and interdisciplinary collaboration to enhance ecological insights and conservation efforts. |
| format | Artículo científico |
| id | pubmed_40427709 |
| institution | PubMed |
| language | en |
| publishDate | 2025 |
| publisher | Biology |
| record_format | pubmed |
| spellingShingle | Artificial Intelligence in Aquatic Biodiversity Research: A PRISMA-Based Systematic Review. Miller, Tymoteusz Michoński, Grzegorz Durlik, Irmina Kozlovska, Polina Biczak, Paweł Artificial Intelligence in Aquatic Biodiversity Research: A PRISMA-Based Systematic Review. Miller, Tymoteusz Michoński, Grzegorz Durlik, Irmina Kozlovska, Polina Biczak, Paweł Freshwater ecosystems are increasingly threatened by climate change and anthropogenic activities, necessitating innovative and scalable monitoring solutions. Artificial intelligence (AI) has emerged as a transformative tool in aquatic biodiversity research, enabling automated species identification, predictive habitat modeling, and conservation planning. This systematic review follows the PRISMA framework to analyze AI applications in freshwater biodiversity studies. Using a structured literature search across Scopus, Web of Science, and Google Scholar, we identified 312 relevant studies published between 2010 and 2024. This review categorizes AI applications into species identification, habitat assessment, ecological risk evaluation, and conservation strategies. A risk of bias assessment was conducted using QUADAS-2 and RoB 2 frameworks, highlighting methodological challenges, such as measurement bias and inconsistencies in the model validation. The citation trends demonstrate exponential growth in AI-driven biodiversity research, with leading contributions from China, the United States, and India. Despite the growing use of AI in this field, this review also reveals several persistent challenges, including limited data availability, regional imbalances, and concerns related to model generalizability and transparency. Our findings underscore AI's potential in revolutionizing biodiversity monitoring but also emphasize the need for standardized methodologies, improved data integration, and interdisciplinary collaboration to enhance ecological insights and conservation efforts. |
| title | Artificial Intelligence in Aquatic Biodiversity Research: A PRISMA-Based Systematic Review. |
| url | https://pubmed.ncbi.nlm.nih.gov/40427709/ |