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Transforming sepsis management : AI-driven innovations in early detection and tailored therapies

Papareddy, Praveen LU orcid ; Lobo, Thamar Jessurun ; Holub, Michal ; Bouma, Hjalmar ; Maca, Jan ; Strodthoff, Nils and Herwald, Heiko LU orcid (2025) In Critical care (London, England) 29. p.1-16
Abstract

Sepsis remains a leading cause of mortality worldwide, driven by its clinical complexity and delayed recognition. Artificial intelligence (AI) offers promising solutions to improve sepsis care through earlier detection, risk stratification, and personalized treatment strategies. Key applications include AI-driven early warning systems, subphenotyping based on clinical and biological data, and decision support tools that adapt to real-time patient information. The integration of diverse data types, such as structured clinical data, unstructured notes, waveform signals, and molecular biomarkers, enhances the precision and timeliness of interventions. However, challenges such as algorithmic bias, limited external validation, data quality... (More)

Sepsis remains a leading cause of mortality worldwide, driven by its clinical complexity and delayed recognition. Artificial intelligence (AI) offers promising solutions to improve sepsis care through earlier detection, risk stratification, and personalized treatment strategies. Key applications include AI-driven early warning systems, subphenotyping based on clinical and biological data, and decision support tools that adapt to real-time patient information. The integration of diverse data types, such as structured clinical data, unstructured notes, waveform signals, and molecular biomarkers, enhances the precision and timeliness of interventions. However, challenges such as algorithmic bias, limited external validation, data quality issues, and ethical considerations continue to hinder clinical implementation. Future directions focus on real-time model adaptation, multi-omics integration, and the development of generalist medical AI capable of personalized recommendations. Successfully addressing these barriers is essential for AI to deliver on its potential to transform sepsis management and support the transition toward precision-driven critical care.

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author
; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Humans, Sepsis/diagnosis, Artificial Intelligence/trends, Early Diagnosis, Precision Medicine/methods
in
Critical care (London, England)
volume
29
article number
366
pages
1 - 16
publisher
BioMed Central (BMC)
external identifiers
  • pmid:40830514
ISSN
1364-8535
DOI
10.1186/s13054-025-05588-0
language
English
LU publication?
yes
additional info
© 2025. The Author(s).
id
8bd9edf6-0a0e-4c6a-bc18-363518995642
date added to LUP
2025-08-21 07:22:25
date last changed
2025-08-21 07:36:08
@article{8bd9edf6-0a0e-4c6a-bc18-363518995642,
  abstract     = {{<p>Sepsis remains a leading cause of mortality worldwide, driven by its clinical complexity and delayed recognition. Artificial intelligence (AI) offers promising solutions to improve sepsis care through earlier detection, risk stratification, and personalized treatment strategies. Key applications include AI-driven early warning systems, subphenotyping based on clinical and biological data, and decision support tools that adapt to real-time patient information. The integration of diverse data types, such as structured clinical data, unstructured notes, waveform signals, and molecular biomarkers, enhances the precision and timeliness of interventions. However, challenges such as algorithmic bias, limited external validation, data quality issues, and ethical considerations continue to hinder clinical implementation. Future directions focus on real-time model adaptation, multi-omics integration, and the development of generalist medical AI capable of personalized recommendations. Successfully addressing these barriers is essential for AI to deliver on its potential to transform sepsis management and support the transition toward precision-driven critical care.</p>}},
  author       = {{Papareddy, Praveen and Lobo, Thamar Jessurun and Holub, Michal and Bouma, Hjalmar and Maca, Jan and Strodthoff, Nils and Herwald, Heiko}},
  issn         = {{1364-8535}},
  keywords     = {{Humans; Sepsis/diagnosis; Artificial Intelligence/trends; Early Diagnosis; Precision Medicine/methods}},
  language     = {{eng}},
  month        = {{08}},
  pages        = {{1--16}},
  publisher    = {{BioMed Central (BMC)}},
  series       = {{Critical care (London, England)}},
  title        = {{Transforming sepsis management : AI-driven innovations in early detection and tailored therapies}},
  url          = {{http://dx.doi.org/10.1186/s13054-025-05588-0}},
  doi          = {{10.1186/s13054-025-05588-0}},
  volume       = {{29}},
  year         = {{2025}},
}