Transforming sepsis management: AI-driven innovations in early detection and tailored therapies
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00843989%3A_____%2F25%3AE0111857" target="_blank" >RIV/00843989:_____/25:E0111857 - isvavai.cz</a>
Alternative codes found
RIV/61383082:_____/25:00001525 RIV/61988987:17110/25:A2603D83 RIV/00216208:11110/25:10501161
Result on the web
<a href="https://ccforum.biomedcentral.com/articles/10.1186/s13054-025-05588-0" target="_blank" >https://ccforum.biomedcentral.com/articles/10.1186/s13054-025-05588-0</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1186/s13054-025-05588-0" target="_blank" >10.1186/s13054-025-05588-0</a>
Alternative languages
Result language
angličtina
Original language name
Transforming sepsis management: AI-driven innovations in early detection and tailored therapies
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30221 - Critical care medicine and Emergency medicine
Result continuities
Project
—
Continuities
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Critical care
ISSN
1364-8535
e-ISSN
1466-609X
Volume of the periodical
29
Issue of the periodical within the volume
article 366
Country of publishing house
GB - UNITED KINGDOM
Number of pages
16
Pages from-to
1-16
UT code for WoS article
001553712000001
EID of the result in the Scopus database
2-s2.0-105013656845