Artificial intelligence-driven predictive analytics for postoperative management and recovery in trauma patients

Area of Science:

• Trauma Care
• Artificial Intelligence
• Machine Learning
• Precision Medicine

Background:

• Post-traumatic care is shifting from reactive protocols to predictive, personalized approaches.
• Artificial intelligence (AI) and machine learning (ML) are key technologies driving this evolution.
• The focus is on predicting complications before they manifest.

Purpose of the Study:

• To review how AI and ML are redefining postoperative management in trauma care.
• To examine the predictive capabilities of AI/ML in anticipating complications.
• To highlight advances in AI/ML applications for trauma patient management.

Main Methods:

• Review of recent literature (2023-2025) on AI/ML in trauma care.
• Analysis of studies validating AI algorithms for complication prediction.
• Examination of AI applications across different trauma-related conditions.

Main Results:

• Gradient boosting algorithms show superior prediction of trauma-induced coagulopathy.
• Interpretable models are emerging for venous thromboembolism risk in traumatic brain injury.
• Real-time sepsis prediction tools are being developed, accounting for trauma-specific inflammation.
• A limitation is the reliance on retrospective, single-center data, necessitating external validation.

Conclusions:

• AI is a driver of precision medicine in trauma, enabling anticipation of adverse events.
• Diverse AI modalities (computer vision, NLP) are being leveraged.
• Implementation barriers include data interoperability and model generalizability, requiring future focus.