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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/61383082:_____/25:00001525 RIV/61988987:17110/25:A2603D83 RIV/00216208:11110/25:10501161

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30221 - Critical care medicine and Emergency medicine

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Critical care

ISSN

1364-8535

e-ISSN

1466-609X

Svazek periodika

29

Číslo periodika v rámci svazku

article 366

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

16

Strana od-do

1-16

Kód UT WoS článku

001553712000001

EID výsledku v databázi Scopus

2-s2.0-105013656845