Advances in Closed-Loop Artificial Intelligence for Healthcare

Abstract

Artificial intelligence (AI) is increasingly used in healthcare to support clinical decision-making through clinical decision support systems (CDSS). Human-in-the-loop (HITL) approaches introduce clinician oversight to improve model interpretability, reliability, and adaptability, while explainable AI (XAI) helps clinicians understand model behaviour. This review categorises HITL AI approaches in healthcare into pre-deployment and post-deployment stages and provides a dedicated review focusing specifically on post-deployment HITL systems. It also introduces the concept of closed-loop AI, where real-time expert feedback can refine AI outputs without requiring model retraining. A systematic review following PRISMA guidelines was conducted using the Scopus and PubMed databases for studies published between 2020 and July 2025. From 3466 identified records, 3012 remained after duplicate removal. After title and abstract screening, 1630 articles were assessed through full-text review, and 15 studies met the predefined inclusion criteria related to HITL, post-deployment adaptation, and interactive XAI in healthcare. The selected studies indicate growing interest in post-deployment HITL systems that allow clinicians to refine AI outputs, provide real-time feedback, and support adaptive CDSS. These findings highlight a shift toward human-centred, closed-loop AI frameworks that integrate expert feedback into deployed systems to improve transparency, trust, and responsiveness in clinical decision-making.

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