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Review
. 2024 Mar 31;14(1):77-84.
doi: 10.18683/germs.2024.1419. eCollection 2024 Mar.

Implications of using artificial intelligence in the diagnosis of sepsis/sepsis shock

Gabriel-Petre Gorecki  1 , Dana-Rodica Tomescu  2 , Liana Pleș  3 , Anca-Maria Panaitescu  4 , Șerban Dragosloveanu  5 , Cristian Scheau  6 , Romina-Marina Sima  7 , Ionuț-Simion Coman  8 , Valentin-Titus Grigorean  9 , Daniel Cochior  10
Affiliations
Review

Implications of using artificial intelligence in the diagnosis of sepsis/sepsis shock

Gabriel-Petre Gorecki et al. Germs. .

Abstract

Introduction: Sepsis and septic shock represent severe pathological states, characterized by the systemic response to infection, which can lead to organ dysfunction and high mortality. Early diagnosis and rapid intervention are crucial for improving survival chances. However, the diagnosis of sepsis is complex due to its nonspecific symptoms and the variability of patient responses to infections.

Methods: The objective of this research was to analyze the implications of using artificial intelligence (AI) in the diagnosis of sepsis and septic shock. The research method applied in the analysis of the implications of using artificial intelligence (AI) in the diagnosis of sepsis and septic shock is the literature review.

Results: Among the benefits of using AI in the diagnosis of sepsis, it is noted that artificial intelligence can rapidly analyze large volumes of clinical data to identify early signs of sepsis, sometimes even before symptoms become evident to medical staff. AI models can use predictive algorithms to assess the risk of sepsis in patients, allowing for early interventions that can save lives. AI can contribute to the development of personalized treatment plans, adapting to the specific needs of each patient based on their medical history and response to treatment. The use of patient data to train AI models raises concerns regarding data privacy and security.

Conclusions: Artificial intelligence has the potential to revolutionize the diagnosis and treatment of sepsis, offering powerful tools for early identification and management of this critical condition. However, to realize this potential, close collaboration between researchers, clinicians, and technology developers is necessary, as well as addressing ethical and implementation challenges.

Keywords: Machine learning; biomarkers; clinical decision support systems; outcome prediction; predictive modeling.

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Conflict of interest statement

Conflicts of interest: All authors – none to declare.

Figures

Figure 1.
Figure 1.
PRISMA flow diagram of articles related to artificial intelligence in diagnosing sepsis and septic shock
See this image and copyright information in PMC

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