SynthesisSensors (Basel, Switzerland)2024
Reinforcement Learning Algorithms and Applications in Healthcare and Robotics: A Comprehensive and Systematic Review.
Synthesis in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- Applications of Reinforcement Learning for Autonomous Surgical Robotics: A Systematic Review.Biomimetics (Basel, Switzerland) · 2026Review
- Process Improvement Before Artificial Intelligence and Automation: Building Trust With the Understand-Transform-Sustain Framework.Mayo Clinic proceedings. Innovations, quality & outcomes · 2026Article
- Personalized Type 1 Diabetes Management: Reinforcement Learning-Based Insulin Dosing and Glucose Forecasting.JMIR diabetes · 2026Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- Review
- Improving Cell Detection and Tracking in Microscopy Images Using YOLO and an Enhanced DeepSORT Algorithm.Sensors (Basel, Switzerland) · 2025Article
- Review
- Predicting outcomes using neural networks in the intensive care unit.World journal of clinical cases · 2025Article
- The Transformative Role of Artificial Intelligence in Dentistry: A Comprehensive Overview. Part 1: Fundamentals of AI, and its Contemporary Applications in Dentistry.International dental journal · 2025Review
- Lights and Shadows on Artificial Intelligence in Glaucoma: Transforming Screening, Monitoring, and Prognosis.Journal of clinical medicine · 2025Review
- Towards unbiased skin cancer classification using deep feature fusion.BMC medical informatics and decision making · 2025Article
- Current insights on predicting vestibular diseases using machine learning.Turkish journal of medical sciences · 2025Review
- Artificial intelligence and pediatric acute kidney injury: a mini-review and white paper.Frontiers in nephrology · 2025Review
- Artificial Intelligence in Chronic Disease Management for Aging Populations: A Systematic Review of Machine Learning and NLP Applications.International journal of general medicine · 2025Review
- Artificial intelligence in breast cancer diagnosis: A systematic literature review.Cambridge prisms. Precision medicine · 2025Review
- Reinforcement Learning-Based Control for Collaborative Robotic Brain Retraction.Sensors (Basel, Switzerland) · 2024Article
- Integrating Machine Learning with Multi-Omics Technologies in Geroscience: Towards Personalized Medicine.Journal of personalized medicine · 2024Review
- Generalization Enhancement of Visual Reinforcement Learning through Internal States.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Reinforcement learning (RL) has emerged as a dynamic and transformative paradigm in artificial intelligence, offering the promise of intelligent decision-making in complex and dynamic environments. This unique feature enables RL to address sequential decision-making problems with simultaneous sampling, evaluation, and feedback. As a result, RL techniques have become suitable candidates for developing powerful solutions in various domains. In this study, we present a comprehensive and systematic review of RL algorithms and applications. This review commences with an exploration of the foundations of RL and proceeds to examine each algorithm in detail, concluding with a comparative analysis of RL algorithms based on several criteria. This review then extends to two key applications of RL: robotics and healthcare. In robotics manipulation, RL enhances precision and adaptability in tasks such as object grasping and autonomous learning. In healthcare, this review turns its focus to the realm of cell growth problems, clarifying how RL has provided a data-driven approach for optimizing the growth of cell cultures and the development of therapeutic solutions. This review offers a comprehensive overview, shedding light on the evolving landscape of RL and its potential in two diverse yet interconnected fields.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.