International Journal of Technology and Applied Science

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The Evolving Role of AI Models in Community-acquired Pneumonia Risk Prediction and Management

Author(s) Adellailluminata Shubila Ngambeki, Deepika Kapoor
Country India
Abstract Accurate risk assessment and prognostication are essential for optimizing treatment outcomes in community-acquired pneumonia (CAP). Over the years, multiple risk prediction models and severity scores have been developed and validated to enhance clinical judgment, including widely used tools such as PSI, CURB-65, DS CRB-65, PRS, CARPE DIEM, SOFA, qSOFA, SeF, SeF-ML, and NEWS-2. Refinements in these systems include modifications of traditional scoring methods, integration of novel biomarkers, and, more recently, the application of artificial intelligence (AI) and machine learning algorithms to improve predictive accuracy and clinical utility. This review emphasizes the potential of AI-driven approaches to strengthen clinical decision-making in CAP for both inpatient and outpatient management. Evidence from recent studies (2019–2025) highlights how AI can optimize risk stratification, refine prognostication, and ultimately improve patient outcomes across diverse populations with comorbidities. Nonetheless, significant hurdles remain, including model complexity, lack of generalizable trainable datasets, limited clinical integration, and ethical concerns. Addressing these challenges is imperative to fully harness AI as a transformative tool in CAP management.
Keywords Artificial intelligence, Machine learning, community-acquired pneumonia (CAP), biomarkers, chest X-ray (CXR), electronic health record (EHR).
Field Biology > Medical / Physiology
Published In Volume 16, Issue 12, Array 2025
Published On 2025-12-31
Cite This The Evolving Role of AI Models in Community-acquired Pneumonia Risk Prediction and Management - Adellailluminata Shubila Ngambeki, Deepika Kapoor - IJTAS Volume 16, Issue 12, Array 2025.
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