Beyond Prediction: Reinforcement Learning as the Defining Leap in Healthcare AI

• URL: http://arxiv.org/abs/2508.21101v1
• Date: Thu, 28 Aug 2025 07:05:24 GMT
• Title: Beyond Prediction: Reinforcement Learning as the Defining Leap in Healthcare AI
• Authors: Dilruk Perera, Gousia Habib, Qianyi Xu, Daniel J. Tan, Kai He, Erik Cambria, Mengling Feng,
• Abstract summary: Reinforcement learning (RL) marks a fundamental shift in how artificial intelligence is applied in healthcare.<n>Instead of merely predicting outcomes, RL actively decides interventions with long term goals.<n>This paper explores RL's rise in healthcare as more than a set of tools, rather a shift toward agentive intelligence in clinical environments.
• Score: 38.11241251343041
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement learning (RL) marks a fundamental shift in how artificial intelligence is applied in healthcare. Instead of merely predicting outcomes, RL actively decides interventions with long term goals. Unlike traditional models that operate on fixed associations, RL systems learn through trial, feedback, and long-term reward optimization, introducing transformative possibilities and new risks. From an information fusion lens, healthcare RL typically integrates multi-source signals such as vitals, labs clinical notes, imaging and device telemetry using temporal and decision-level mechanisms. These systems can operate within centralized, federated, or edge architectures to meet real-time clinical constraints, and naturally span data, features and decision fusion levels. This survey explore RL's rise in healthcare as more than a set of tools, rather a shift toward agentive intelligence in clinical environments. We first structure the landscape of RL techniques including model-based and model-free methods, offline and batch-constrained approaches, and emerging strategies for reward specification and uncertainty calibration through the lens of healthcare constraints. We then comprehensively analyze RL applications spanning critical care, chronic disease, mental health, diagnostics, and robotic assistance, identifying their trends, gaps, and translational bottlenecks. In contrast to prior reviews, we critically analyze RL's ethical, deployment, and reward design challenges, and synthesize lessons for safe, human-aligned policy learning. This paper serves as both a a technical roadmap and a critical reflection of RL's emerging transformative role in healthcare AI not as prediction machinery, but as agentive clinical intelligence.

Related papers

• Causal Reinforcement Learning based Agent-Patient Interaction with Clinical Domain Knowledge [3.2434118923825483]
  Reinforcement Learning (RL) faces significant challenges in adaptive healthcare interventions, such as dementia care, where data is scarce.<n>We present a novel framework called Causal structure-aware Reinforcement Learning (CRL) that explicitly integrates causal discovery and reasoning into policy optimization.
  arXiv  Detail & Related papers  (2025-11-17T22:38:03Z)
• Guardian-regularized Safe Offline Reinforcement Learning for Smart Weaning of Mechanical Circulatory Devices [19.512275639322638]
  We study the sequential decision-making problem for automated weaning of mechanical circulatory support devices in cardiogenic shock patients.<n> offline reinforcement learning has proven to be successful in sequential decision-making tasks.<n>We develop an end-to-end machine learning framework with two key contributions.
  arXiv  Detail & Related papers  (2025-11-08T19:32:31Z)
• Part I: Tricks or Traps? A Deep Dive into RL for LLM Reasoning [53.85659415230589]
  This paper systematically reviews widely adoptedReinforcement learning techniques.<n>We present clear guidelines for selecting RL techniques tailored to specific setups.<n>We also reveal that a minimalist combination of two techniques can unlock the learning capability of critic-free policies.
  arXiv  Detail & Related papers  (2025-08-11T17:39:45Z)
• Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications [59.721265428780946]
  Large Language Models (LLMs) in medicine have enabled impressive capabilities, yet a critical gap remains in their ability to perform systematic, transparent, and verifiable reasoning.<n>This paper provides the first systematic review of this emerging field.<n>We propose a taxonomy of reasoning enhancement techniques, categorized into training-time strategies and test-time mechanisms.
  arXiv  Detail & Related papers  (2025-08-01T14:41:31Z)
• Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities [62.05713042908654]
  This paper provides a review of advances in Large Language Models (LLMs) alignment through the lens of inverse reinforcement learning (IRL)<n>We highlight the necessity of constructing neural reward models from human data and discuss the formal and practical implications of this paradigm shift.
  arXiv  Detail & Related papers  (2025-07-17T14:22:24Z)
• Keeping Medical AI Healthy: A Review of Detection and Correction Methods for System Degradation [6.781778751487079]
  This review presents a forward-looking perspective on monitoring and maintaining the "health" of AI systems in healthcare.<n>We highlight the urgent need for continuous performance monitoring, early degradation detection, and effective self-correction mechanisms.<n>This work aims to guide the development of reliable, robust medical AI systems capable of sustaining safe, long-term deployment in dynamic clinical settings.
  arXiv  Detail & Related papers  (2025-06-20T19:22:07Z)
• Artificial Intelligence-Driven Clinical Decision Support Systems [5.010570270212569]
  The chapter emphasizes that creating trustworthy AI systems in healthcare requires careful consideration of fairness, explainability, and privacy.<n>The challenge of ensuring equitable healthcare delivery through AI is stressed, discussing methods to identify and mitigate bias in clinical predictive models.<n>The discussion advances in an analysis of privacy vulnerabilities in medical AI systems, from data leakage in deep learning models to sophisticated attacks against model explanations.
  arXiv  Detail & Related papers  (2025-01-16T16:17:39Z)
• Towards Next-Generation Medical Agent: How o1 is Reshaping Decision-Making in Medical Scenarios [46.729092855387165]
  We study the choice of the backbone LLM for medical AI agents, which is the foundation for the agent's overall reasoning and action generation.<n>Our findings demonstrate o1's ability to enhance diagnostic accuracy and consistency, paving the way for smarter, more responsive AI tools.
  arXiv  Detail & Related papers  (2024-11-16T18:19:53Z)
• Detecting Shortcut Learning for Fair Medical AI using Shortcut Testing [62.9062883851246]
  Machine learning holds great promise for improving healthcare, but it is critical to ensure that its use will not propagate or amplify health disparities. One potential driver of algorithmic unfairness, shortcut learning, arises when ML models base predictions on improper correlations in the training data. Using multi-task learning, we propose the first method to assess and mitigate shortcut learning as a part of the fairness assessment of clinical ML systems.
  arXiv  Detail & Related papers  (2022-07-21T09:35:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.