ReviewDigital health
Artificial intelligence-assisted phenotyping of sepsis: Research progress, clinical challenges, and translational prospects.
Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sepsis remains a leading cause of mortality in intensive care units worldwide, a challenge exacerbated by pathophysiological and clinical heterogeneity that limits the effectiveness of uniform management strategies, motivating the development of phenotype-guided approaches to diagnosis and treatment. Artificial intelligence (AI)-assisted phenotyping can stratify patients with sepsis into distinct subpopulations with differential immune profiles and heterogeneous treatment responses. This narrative review synthesizes recent advances in AI-assisted sepsis phenotyping, focusing on three persistent research bottlenecks: terminological inconsistency, fragmented integration between prognostic stratification and treatment-response phenotyping, and ambiguous clinical translation pathways. A standardized nomenclature is proposed to improve cross-study comparability, accompanied by a delineation of mainstream AI methodological frameworks, critical care datasets, and multilevel validation systems tailored for intensive care unit scenarios. Eight complementary research dimensions are mapped, including transcriptomic endotyping, single-cell profiling, electronic health record-derived clinical phenotyping, dynamic trajectory modeling, organ dysfunction stratification, biomarker panels, multi-omic integration, and treatment-response phenotyping, with treatment-response phenotyping highlighted as the highest-priority translational frontier. Critical analysis of predominant translational barriers, such as limited model generalizability, insufficient interpretability, poor workflow compatibility, and regulatory uncertainty, is presented alongside stage-specific actionable roadmaps designed to promote real-world clinical deployment. By constructing a unified interdisciplinary framework, this review defines key future research priorities to accelerate the evidence-based transition from algorithmic prototypes to bedside precision sepsis management.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.