Abstract Ventilator-associated pneumonia (VAP) is one of the most serious infections in intensive care units, affecting many patients who are on mechanical ventilation. It increases the risk of death, prolongs hospital stays, and raises healthcare costs. Diagnosing VAP is difficult because its signs and symptoms overlap with other lung problems, and there is no single reliable test to confirm it. Current tools like clinical scores, chest X-rays, and culture tests are often slow or inaccurate. Machine learning (ML), which can analyze large and complex hospital data, offers a new way to improve VAP detection and management. By combining information from vital signs, ventilator settings, lab results, imaging, and clinical notes, ML models can recognize patterns that doctors might miss. This could help in identifying high-risk patients earlier, making faster diagnoses, guiding antibiotic use, and predicting outcomes. Open databases such as MIMICs are now available to train and test these models. This review focuses on the current state of ML applications for VAP, including screening, diagnosis, outcome prediction, and decision support, while discussing challenges, limitations, and future prospects for clinical implementation. Although early studies show promise, challenges remain, such as the lack of a single diagnosis, differences in hospital practices, and limited testing of models in real-world settings. Future efforts should focus on developing models that are accurate and can be directly integrated into hospital systems. With further research, ML could become an important tool for improving VAP care by supporting quicker decisions, reducing delays, and helping patients recover better.
Gautam et al. (2026) studied this question.