Clinical decision-making increasingly relies on data-driven tools, but most systems today are still predictive models that work at isolated time points. Reinforcement learning (RL) provides a different approach by optimizing sequences of actions under uncertainty. It’s often seen as a foundation for more “agentic”AI in healthcare. We conducted a systematic literature review of RL-based clinical decision support systems (CDSS) published between 2020 and January 2026. We reviewed 66 studies, looking at the clinical domain, decision type, RL methods, data, and system maturity. RL-based CDSS are mostly used in critical care, cardiology, oncology, and diabetes, focusing on therapeutic dosing optimization. Actor-critic and policy-gradient methods are mainly used in continuous physiological/device-control settings. Most systems are trained offline using historical data: 66.7% rely on observational clinical data, 21.2% use simulated environments, and 12.1% combine both. Overall, RL-based CDSS are still partially autonomous, they often prioritize autonomy and personalization over runtime oversight, interpretability, and evaluation. We suggest an “agentic readiness”framework to address these gaps and emphasize the need for better safeguards, clearer reporting, and more human-centered assessments.
Ali Najafi, Amirfarhad Farhadi, A. Zamanifar· Discover Data· 0 citations
The integration of Large Language Models (LLMs) with Reinforcement Learning (RL) for financial decision-making has grown rapidly in recent years, yet the literature remains fragmented and lacks systematic comparison across methods. In this survey we analyze 34 core studies (2023–2026), selected through a multi-stage process involving 84 initial candidates and 46 full-text reviews, and propose a three-paradigm taxonomy (feature-based, auxiliary-based, and policy-based) based on the functional role of LLMs within the RL pipeline. Analysis of these integration paradigms reveals an emergent architectural trade-off: while tighter policy-based coupling theoretically offers deeper contextual reasoning, it frequently introduces significant computational overhead and training instability. Conversely, simpler feature-based integration provides superior scalability and stability, though often at the expense of representational depth. Given the current benchmark fragmentation, the reported performance gains across these studies remain difficult to validate universally across different asset classes. Critical gaps identified include the insufficient handling of data leakage and look-ahead bias, standardized benchmarks, and limited alignment with regulatory frameworks such as MiFID II and the EU AI Act.
Ghusoon Hadi al-Aldaffaie, Alireza Taheri, Amirfarhad Farhadi et al.· Discover Artificial Intellig...· 0 citations
A novel Ante-hoc Explainable AI framework designed to bridge the interpretability-accuracy trade-off in high-stakes financial prognosis, specifically within credit scoring systems, which provides a verifiable and robust solution for modern, regulatory-compliant financial environments.
Deniz NoorMohammadzadehMaleki, Mahdi Baghaei Oskouei, Alireza Taheri et al.· AI and Ethics· 0 citations