Bug Wars: Artificial Intelligence Strikes Back in Sepsis Management
Sepsis remains a leading global cause of mortality, with delayed recognition and empirical antibiotic overuse fueling poor outcomes and rising antimicrobial resistance. This systematic scoping review evaluates the current landscape of artificial intelligence (AI) and machine learning (ML) applicatio...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/15/15/1890 |
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| author | Georgios I. Barkas Ilias E. Dimeas Ourania S. Kotsiou |
| author_facet | Georgios I. Barkas Ilias E. Dimeas Ourania S. Kotsiou |
| author_sort | Georgios I. Barkas |
| collection | DOAJ |
| description | Sepsis remains a leading global cause of mortality, with delayed recognition and empirical antibiotic overuse fueling poor outcomes and rising antimicrobial resistance. This systematic scoping review evaluates the current landscape of artificial intelligence (AI) and machine learning (ML) applications in sepsis care, focusing on early detection, personalized antibiotic management, and resistance forecasting. Literature from 2019 to 2025 was systematically reviewed following PRISMA-ScR guidelines. A total of 129 full-text articles were analyzed, with study quality assessed via the JBI and QUADAS-2 tools. AI-based models demonstrated robust predictive performance for early sepsis detection (AUROC 0.68–0.99), antibiotic stewardship, and resistance prediction. Notable tools, such as InSight and KI.SEP, leveraged multimodal clinical and biomarker data to provide actionable, real-time support and facilitate timely interventions. AI-driven platforms showed potential to reduce inappropriate antibiotic use and nephrotoxicity while optimizing outcomes. However, most models are limited by single-center data, variable interpretability, and insufficient real-world validation. Key challenges remain regarding data integration, algorithmic bias, and ethical implementation. Future research should prioritize multicenter validation, seamless integration with clinical workflows, and robust ethical frameworks to ensure safe, equitable, and effective adoption. AI and ML hold significant promise to transform sepsis management, but their clinical impact depends on transparent, validated, and user-centered deployment. |
| format | Article |
| id | doaj-art-9b16e0a9a09f4174826d78bfce81cf5c |
| institution | Kabale University |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-9b16e0a9a09f4174826d78bfce81cf5c2025-08-20T03:36:26ZengMDPI AGDiagnostics2075-44182025-07-011515189010.3390/diagnostics15151890Bug Wars: Artificial Intelligence Strikes Back in Sepsis ManagementGeorgios I. Barkas0Ilias E. Dimeas1Ourania S. Kotsiou2Laboratory of Human Pathophysiology, Department of Nursing, School of Health Sciences, University of Thessaly, 41500 Larissa, GreeceDepartment of Respiratory Medicine, Faculty of Medicine, School of Health Sciences, University of Thessaly, 41110 Larissa, GreeceLaboratory of Human Pathophysiology, Department of Nursing, School of Health Sciences, University of Thessaly, 41500 Larissa, GreeceSepsis remains a leading global cause of mortality, with delayed recognition and empirical antibiotic overuse fueling poor outcomes and rising antimicrobial resistance. This systematic scoping review evaluates the current landscape of artificial intelligence (AI) and machine learning (ML) applications in sepsis care, focusing on early detection, personalized antibiotic management, and resistance forecasting. Literature from 2019 to 2025 was systematically reviewed following PRISMA-ScR guidelines. A total of 129 full-text articles were analyzed, with study quality assessed via the JBI and QUADAS-2 tools. AI-based models demonstrated robust predictive performance for early sepsis detection (AUROC 0.68–0.99), antibiotic stewardship, and resistance prediction. Notable tools, such as InSight and KI.SEP, leveraged multimodal clinical and biomarker data to provide actionable, real-time support and facilitate timely interventions. AI-driven platforms showed potential to reduce inappropriate antibiotic use and nephrotoxicity while optimizing outcomes. However, most models are limited by single-center data, variable interpretability, and insufficient real-world validation. Key challenges remain regarding data integration, algorithmic bias, and ethical implementation. Future research should prioritize multicenter validation, seamless integration with clinical workflows, and robust ethical frameworks to ensure safe, equitable, and effective adoption. AI and ML hold significant promise to transform sepsis management, but their clinical impact depends on transparent, validated, and user-centered deployment.https://www.mdpi.com/2075-4418/15/15/1890artificial intelligencemachine learningpredictionsepsis |
| spellingShingle | Georgios I. Barkas Ilias E. Dimeas Ourania S. Kotsiou Bug Wars: Artificial Intelligence Strikes Back in Sepsis Management Diagnostics artificial intelligence machine learning prediction sepsis |
| title | Bug Wars: Artificial Intelligence Strikes Back in Sepsis Management |
| title_full | Bug Wars: Artificial Intelligence Strikes Back in Sepsis Management |
| title_fullStr | Bug Wars: Artificial Intelligence Strikes Back in Sepsis Management |
| title_full_unstemmed | Bug Wars: Artificial Intelligence Strikes Back in Sepsis Management |
| title_short | Bug Wars: Artificial Intelligence Strikes Back in Sepsis Management |
| title_sort | bug wars artificial intelligence strikes back in sepsis management |
| topic | artificial intelligence machine learning prediction sepsis |
| url | https://www.mdpi.com/2075-4418/15/15/1890 |
| work_keys_str_mv | AT georgiosibarkas bugwarsartificialintelligencestrikesbackinsepsismanagement AT iliasedimeas bugwarsartificialintelligencestrikesbackinsepsismanagement AT ouraniaskotsiou bugwarsartificialintelligencestrikesbackinsepsismanagement |