Introduction
Sepsis remains a major global health challenge due to its complexity and delayed recognition. Artificial intelligence (AI) shows promise in improving early detection, risk prediction, and personalized treatment using real-time patient data. AI applications include early warning systems, clinical decision support, and patient sub-phenotyping using diverse data sources such as clinical records, waveforms, and biomarkers. However, challenges like bias, limited validation, data quality, and ethical concerns still limit clinical adoption. Future priorities include multi-omics integration, real-time adaptive models, and generalist medical AI to support precision-based sepsis care.
Sepsis affects millions worldwide and, despite improved survival, many patients suffer long-term complications. Early recognition and treatment are essential due to rapid disease progression driven by immune dysregulation, organ dysfunction, and coagulopathy.
Clinical challenges persist due to patient variability, comorbidities, and rising antimicrobial resistance. Updated Surviving Sepsis Campaign guidelines emphasize rapid diagnosis, early antimicrobials, hemodynamic support, and timely source control—ideally within the first hour—to reduce mortality. Ongoing research aims to improve diagnostics, address endothelial and coagulation dysfunction, and tailor treatment to individual patient biology.
Role of AI in sepsis care
Advancements in AI are creating new possibilities for precision medicine in sepsis, addressing challenges of early diagnosis and individualized treatment. AI models can support early detection, risk prediction, and personalized therapy by analyzing large clinical and molecular datasets. However, real-world evidence of improved outcomes is still limited.
This review highlights three key use cases of AI in sepsis care and discusses challenges in clinical adoption, regulation, and implementation. It focuses on adult sepsis based on current clinical definitions. Unlike previous reviews that mainly examine detection models or specific algorithms, this review provides a broader perspective—from prediction and phenotyping to therapeutic guidance and practical deployment—while outlining future research needs.
Use cases for AI in sepsis care
AI has the potential to improve sepsis care by enabling earlier intervention, better patient grouping, and personalized treatment. However, most applications are still experimental and require more validation before routine clinical use.
- AI in Early Detection
AI-powered early warning systems can help identify sepsis before symptoms become severe. Models like InSight, TREWS, and SERA show strong predictive ability, sometimes detecting sepsis hours earlier. While some studies report reduced hospital stays and mortality, real-world evidence remains limited. Validation challenges, risk of alert fatigue, and inconsistent performance across settings remain key barriers. - AI-Driven Phenotyping
AI-powered early warning systems can help identify sepsis before symptoms become severe. Models like InSight, TREWS, and SERA show strong predictive ability, sometimes detecting sepsis hours earlier. While some studies report reduced hospital stays and mortality, real-world evidence remains limited. Validation challenges, risk of alert fatigue, and inconsistent performance across settings remain key barriers. - Personalized Treatment with AI Decision Support
AI tools can support personalized therapy by predicting treatment response and guiding decisions on fluids, vasopressors, or antibiotics. Systems like the AI Clinician use reinforcement learning to recommend dosing strategies and are beginning to show feasibility in early trials. However, widespread implementation remains limited due to technical, clinical, and regulatory barriers. This is currently the least developed use case.
Challenges for AI in sepsis care
Challenges in Using AI for Sepsis Care
AI adoption in sepsis faces significant barriers. These challenges can be grouped into three broad areas: foundational, methodological, and deployment-related—each affecting clinical trust, safety, and real-world usefulness.
- Foundational Issues
Sepsis Definition
AI models struggle because sepsis is complex and lacks a universal gold-standard definition. While Sepsis-3 and SOFA-based criteria are increasingly preferred, inconsistencies across datasets, clinical settings, and endpoints make comparisons difficult. Many datasets lack the full SOFA parameters, leading to alternative labeling methods like eSOFA. These differences, though subtle, significantly affect model training, benchmarking, and reproducibility. - Evaluation and Validation Challenges
Most models are evaluated retrospectively, with performance often expressed as AUC—but real-world deployment requires metrics like sensitivity and PPV at actionable alert thresholds. Even high-performing models may fail when applied to new patient populations or hospital systems. External validation across diverse settings is essential to confirm reliability, yet remains limited.A key example is the Epic Sepsis Model, which despite widespread use, missed most true cases when independently tested—highlighting risks of deploying unvalidated tools and the need for robust testing before clinical use.
- Regulation, Ethics, and Clinical Deployment
Unlike drugs or medical devices, most AI tools for sepsis are deployed without prospective trials. Less than 10% of FDA-approved AI systems have such data. Running randomized trials for AI is complex, especially when models operate silently in the background, raising challenges around consent, clinical equipoise, ethics, and regulatory classification.Alternative approaches such as causal inference and target-trial emulation offer interim evidence, but remain hypothesis-generating not replacements for randomized trials.
Emerging frameworks now call for AI systems especially those influencing high-stakes decisions like fluids, antibiotics, or vasopressors to undergo similar validation standards as other medical interventions.
Encouragingly, recent multi-site and prospective studies (e.g., NAVOY® Sepsis, Sepsis Watch, and LLM-based ICU tools) demonstrate that validated AI can improve early detection and workflow integration though reproducibility across settings remains a challenge.
Future Directions for AI in Sepsis Care
AI will continue to advance sepsis care by improving early detection, personalized treatment, and real-time clinical decision-making. As data integration improves, AI tools will support more precise and timely interventions.
- Expanding Data Inputs
- Routine Clinical Data
Most existing models use vitals and lab values. While neural network–based predictors show promise, comparison across studies remains difficult. Standardized datasets and benchmarking are needed to measure real progress. - Unstructured Text
Including clinical notes and reports can improve prediction accuracy. Modern NLP models and LLMs (like GPT-based tools) can extract meaningful insights from EHR text; however, current performance and guideline adherence challenges must be addressed before clinical use. - Omics Data
Genomic, proteomic, and metabolomic insights could help identify biomarkers and treatment targets. While promising, most omics data are not yet routine or rapid enough for bedside use. Advances in point-of-care diagnostic platforms may help bridge this gap. - Waveform Data
High-resolution ECG, PPG, and arterial pressure data can predict sepsis earlier than standard measurements. Deep learning applied to raw waveform streams may enable early detection and better patient stratification.
- Routine Clinical Data
- Multimodal Integration
Combining clinical, molecular, imaging, and sensor data can support dynamic phenotyping and personalized treatment. Modular architectures and pretrained encoders make multimodal AI more feasible without requiring complete data for every patient. - Toward Generalist Medical AI
Future systems may act as full clinical assistants—answering patient-specific questions, guiding therapies, predicting antibiotic resistance, and supporting stewardship. To reach this stage, models will require reasoning abilities, real-time learning, and strong validation in clinical settings.
Conclusion
Artificial intelligence offers significant potential to enhance sepsis care by improving early detection, refining patient phenotyping, and supporting clinical decision-making. These applications address different stages of the care pathway, yet they are interconnected and must progress together to fully optimize patient outcomes. Moving forward, AI systems will require robust prospective validation, regulatory alignment, and strong evidence that they work in real-world, diverse clinical settings.
Successful adoption will depend on models that are usable, interpretable, and seamlessly integrated into clinical workflows. Human-centered design, economic feasibility, and institutional readiness will be essential for implementation. Ultimately, AI should augment—not replace—clinical expertise by delivering timely, data-driven insights. With continued collaboration, iterative refinement, and a focus on patient-centered outcomes, AI has the potential to become a valuable component of high-quality sepsis care.
