Background: Febrile neutropenia (FN) is a common and potentially life-threatening complication in immunocompromised patients, particularly those undergoing hematopoietic stem cell transplantation (HSCT). Timely identification and intervention are essential to prevent progression to sepsis or multi-organ dysfunction. While conventional monitoring relies on intermittent vital sign assessment, artificial intelligence (AI)-driven remote patient monitoring (RPM) offers a promising adjunct to enhance early detection and clinical decision-making. Case Insight: We present a case involving a young adult female post-HSCT who was monitored using an AI-enabled RPM system. Over a 29-day post-transplant period, the system generated real-time alerts indicating clinical deterioration, including febrile episodes, hypotension, tachycardia, and respiratory distress. These alerts were substantiated by clinical and diagnostic evaluations, leading to prompt escalation of antibiotics and supportive care. The AI system’s continuous surveillance and predictive capability enabled earlier recognition of complications, even when standard bedside monitoring showed minimal change. Conclusion: This paper highlights the potential use of AI-integrated RPM systems to transform the management of FN by enabling earlier intervention especially in high-risk immunocompromised patients. Although the preliminary observations are promising, further prospective studies are certainly required to evaluate and assess the accuracy, clinical outcomes, and resource implications of integration of AI into routine critical care monitoring protocols.

Background This comprehensive review consolidates existing research about postoperative outcomes after coronary artery bypass grafting (CABG), a prevalent yet high-risk cardiac procedure. Notwithstanding improvements in surgical methods and perioperative management, CABG patients often encounter multisystem problems, including cardiac arrhythmias, pulmonary dysfunction, renal impairment, neurological impairments, and infections. These problems correlate with extended hospitalizations, elevated readmission rates, and increased healthcare expenditures. Materials and Methods A thorough literature search was performed in the Cochrane Library, MEDLINE, EMBASE, and CINAHL databases in accordance with PRISMA-ScR criteria and the Arksey and O’Malley methodological approach. Studies comprising adult CABG patients with follow-up durations of up to six months were incorporated. Data extraction concentrated on complication profiles, detection timeframes, hospital stay duration, and recovery metrics. Results Narrative synthesis indicated a significant correlation between the early identification of problems and decreased hospital durations. The review highlighted significant deficiencies in the current literature, including inconsistencies in monitoring techniques, insufficient focus on long-term outcomes, and inadequate representation of older and female patients. Conclusion Recent evidence indicates that artificial intelligence-based monitoring systems could improve early intervention and recovery. This analysis highlights the necessity for standardized postoperative care protocols and additional research into long-term recovery, quality of life, and cost-effective surveillance measures to enhance CABG results.