Harnessing artificial intelligence in sepsis care: advances in early detection, personalized treatment, and real-time monitoring
Sepsis remains a leading cause of morbidity and mortality worldwide due to its rapid progression and heterogeneous nature. This review explores the potential of Artificial Intelligence (AI) to transform sepsis management, from early detection to personalized treatment and real-time monitoring. AI, p...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2024.1510792/full |
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| author | Fang Li Shengguo Wang Zhi Gao Maofeng Qing Shan Pan Yingying Liu Chengchen Hu |
| author_facet | Fang Li Shengguo Wang Zhi Gao Maofeng Qing Shan Pan Yingying Liu Chengchen Hu |
| author_sort | Fang Li |
| collection | DOAJ |
| description | Sepsis remains a leading cause of morbidity and mortality worldwide due to its rapid progression and heterogeneous nature. This review explores the potential of Artificial Intelligence (AI) to transform sepsis management, from early detection to personalized treatment and real-time monitoring. AI, particularly through machine learning (ML) techniques such as random forest models and deep learning algorithms, has shown promise in analyzing electronic health record (EHR) data to identify patterns that enable early sepsis detection. For instance, random forest models have demonstrated high accuracy in predicting sepsis onset in intensive care unit (ICU) patients, while deep learning approaches have been applied to recognize complications such as sepsis-associated acute respiratory distress syndrome (ARDS). Personalized treatment plans developed through AI algorithms predict patient-specific responses to therapies, optimizing therapeutic efficacy and minimizing adverse effects. AI-driven continuous monitoring systems, including wearable devices, provide real-time predictions of sepsis-related complications, enabling timely interventions. Beyond these advancements, AI enhances diagnostic accuracy, predicts long-term outcomes, and supports dynamic risk assessment in clinical settings. However, ethical challenges, including data privacy concerns and algorithmic biases, must be addressed to ensure fair and effective implementation. The significance of this review lies in addressing the current limitations in sepsis management and highlighting how AI can overcome these hurdles. By leveraging AI, healthcare providers can significantly enhance diagnostic accuracy, optimize treatment protocols, and improve overall patient outcomes. Future research should focus on refining AI algorithms with diverse datasets, integrating emerging technologies, and fostering interdisciplinary collaboration to address these challenges and realize AI’s transformative potential in sepsis care. |
| format | Article |
| id | doaj-art-26b3b6b282b846328b1697fce146329e |
| institution | Kabale University |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-26b3b6b282b846328b1697fce146329e2025-01-06T05:13:09ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-01-011110.3389/fmed.2024.15107921510792Harnessing artificial intelligence in sepsis care: advances in early detection, personalized treatment, and real-time monitoringFang Li0Shengguo Wang1Zhi Gao2Maofeng Qing3Shan Pan4Yingying Liu5Chengchen Hu6Department of General Surgery, Chongqing General Hospital, Chongqing, ChinaDepartment of Stomatology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaDepartment of Stomatology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaDepartment of Stomatology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaDepartment of Stomatology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaDepartment of Stomatology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaDepartment of Stomatology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaSepsis remains a leading cause of morbidity and mortality worldwide due to its rapid progression and heterogeneous nature. This review explores the potential of Artificial Intelligence (AI) to transform sepsis management, from early detection to personalized treatment and real-time monitoring. AI, particularly through machine learning (ML) techniques such as random forest models and deep learning algorithms, has shown promise in analyzing electronic health record (EHR) data to identify patterns that enable early sepsis detection. For instance, random forest models have demonstrated high accuracy in predicting sepsis onset in intensive care unit (ICU) patients, while deep learning approaches have been applied to recognize complications such as sepsis-associated acute respiratory distress syndrome (ARDS). Personalized treatment plans developed through AI algorithms predict patient-specific responses to therapies, optimizing therapeutic efficacy and minimizing adverse effects. AI-driven continuous monitoring systems, including wearable devices, provide real-time predictions of sepsis-related complications, enabling timely interventions. Beyond these advancements, AI enhances diagnostic accuracy, predicts long-term outcomes, and supports dynamic risk assessment in clinical settings. However, ethical challenges, including data privacy concerns and algorithmic biases, must be addressed to ensure fair and effective implementation. The significance of this review lies in addressing the current limitations in sepsis management and highlighting how AI can overcome these hurdles. By leveraging AI, healthcare providers can significantly enhance diagnostic accuracy, optimize treatment protocols, and improve overall patient outcomes. Future research should focus on refining AI algorithms with diverse datasets, integrating emerging technologies, and fostering interdisciplinary collaboration to address these challenges and realize AI’s transformative potential in sepsis care.https://www.frontiersin.org/articles/10.3389/fmed.2024.1510792/fullartificial intelligencesepsis managementearly detectionpersonalized treatmentreal-time monitoring |
| spellingShingle | Fang Li Shengguo Wang Zhi Gao Maofeng Qing Shan Pan Yingying Liu Chengchen Hu Harnessing artificial intelligence in sepsis care: advances in early detection, personalized treatment, and real-time monitoring Frontiers in Medicine artificial intelligence sepsis management early detection personalized treatment real-time monitoring |
| title | Harnessing artificial intelligence in sepsis care: advances in early detection, personalized treatment, and real-time monitoring |
| title_full | Harnessing artificial intelligence in sepsis care: advances in early detection, personalized treatment, and real-time monitoring |
| title_fullStr | Harnessing artificial intelligence in sepsis care: advances in early detection, personalized treatment, and real-time monitoring |
| title_full_unstemmed | Harnessing artificial intelligence in sepsis care: advances in early detection, personalized treatment, and real-time monitoring |
| title_short | Harnessing artificial intelligence in sepsis care: advances in early detection, personalized treatment, and real-time monitoring |
| title_sort | harnessing artificial intelligence in sepsis care advances in early detection personalized treatment and real time monitoring |
| topic | artificial intelligence sepsis management early detection personalized treatment real-time monitoring |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2024.1510792/full |
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