AI-Driven Decision Support Systems for Neonatal Care: A Reinforcement Learning Approach - Anusha, Sigirisetty; Gowthami, Chopparapu; Onapakala, Kiran; V. Suryanarayana, V. Suryanarayana; Vijaya, Vaddithandra; Ravi, Rongala (Journal of Neonatal Surgery, 2025)
Neonatal surgical care involves high-stakes decision-making under conditions of uncertainty, urgency, and limited physiological feedback. Clinicians often rely on experience and generalised guidelines to decide whether to proceed with surgical intervention—an approach that may not always capture the nuances of individual patient presentations. Artificial Intelligence (AI), particularly Reinforcement Learning (RL), offers a promising avenue to improve consistency and adaptiveness in such critical care scenarios. Unlike traditional machine learning models, RL learns optimal decision strategies by balancing rewards and risks through iterative feedback, making it suitable for sequential and high-impact clinical environments. This study presents a conceptual AI-driven Decision Support System (DSS) that leverages RL principles to assist clinicians in binary surgical decisions for neonates. The system employs manually constructed clinical state–action mappings, expert-informed reward logic, and an explainable Q-table rather than relying on patient data or simulations. It features a three-layered architecture, visual decision flowchart, and event tree to support transparent reasoning. Through hypothetical clinical scenarios and clinician-oriented workflow modelling, the system demonstrates potential for low-resource settings and academic prototyping. While preliminary, the model offers a scalable, modifiable foundation for future integration with real-world clinical platforms, aiming to enhance decision quality in neonatal surgical care.