[AI in Intensive Care Medicine - Focus on Early Detection of Sepsis]

Sepsis remains the third leading cause of death worldwide after cardiovascular and malignant diseases, claiming approximately 11 million lives annually. In Germany alone, around 280000 people develop sepsis each year, with more than 150 deaths daily. Despite advances in therapy, survival still depends primarily on one factor: time. Early initiation of antimicrobial therapy and source control remain critical. Artificial intelligence (AI) promises to identify subtle, pre-symptomatic physiological changes that precede overt clinical signs of sepsis. By continuously analyzing vast streams of data from monitors, laboratory results, and electronic health records, AI models can detect complex, subclinical patterns beyond human perception, potentially predicting sepsis hours in advance.Machine learning (ML) algorithms, particularly supervised learning models, are trained on historical patient data (vital signs, laboratory values, and clinical notes) paired with outcomes indicating whether and when sepsis occurred. Once validated on unseen cases, these models can provide real-time risk assessments in clinical practice. Compared with traditional scores such as SIRS, qSOFA, MEWS, or NEWS, AI systems can integrate multiple variables dynamically and adjust risk estimates continuously.Several studies have shown that ML-based models outperform classical scores in prediction accuracy, identifying sepsis significantly earlier. However, challenges remain: lack of generalizability across hospitals, risk of false alarms, limited interpretability, and insufficient clinical validation. While early implementations demonstrate improved patient outcomes, robust randomized controlled trials are still needed to confirm that AI-driven sepsis prediction truly saves lives and merits integration into clinical guidelines.