ReviewKidney diseases (Basel, Switzerland)2019
Deep Reinforcement Learning in Medicine.
Review in Kidney diseases (Basel, Switzerland), 2019. 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.
- Patient-Specific Deep Reinforcement Learning for Proton Beam Delivery Under Inter-Phase Variations.International journal of particle therapy · 2026Article
- Reinforcement Learning and Its Clinical Applications Within Healthcare: A Systematic Review of Precision Medicine and Dynamic Treatment Regimes.Healthcare (Basel, Switzerland) · 2025Review
- Application of Artificial Intelligence in the Headache Field.Current pain and headache reports · 2024Review
- Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review.Diagnostics (Basel, Switzerland) · 2023Review
- ChatGPT, GPT-4, and Other Large Language Models: The Next Revolution for Clinical Microbiology?Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2023Article
- The artificial intelligence and machine learning in lung cancer immunotherapy.Journal of hematology & oncology · 2023Review
- A Bayesian Network Approach to Explainable Reinforcement Learning with Distal Information.Sensors (Basel, Switzerland) · 2023Article
- Translating Data Science Results into Precision Oncology Decisions: A Mini Review.Journal of clinical medicine · 2023Review
- Deep learning facilitates multi-data type analysis and predictive biomarker discovery in cancer precision medicine.Computational and structural biotechnology journal · 2023Review
- A Review of Radiomics and Artificial Intelligence and Their Application in Veterinary Diagnostic Imaging.Veterinary sciences · 2022Review
- Understanding basic principles of Artificial Intelligence: a practical guide for intensivists.Acta bio-medica : Atenei Parmensis · 2022Article
- Artificial intelligence in pancreatic cancer.Theranostics · 2022Review
- Music Score Recognition Method Based on Deep Learning.Computational intelligence and neuroscience · 2022Article
- Reinforcement Learning for Precision Oncology.Cancers · 2021Review
- 3D cephalometric landmark detection by multiple stage deep reinforcement learning.Scientific reports · 2021Article
- AI and Big Data in Healthcare: Towards a More Comprehensive Research Framework for Multimorbidity.Journal of clinical medicine · 2021Review
- Human locomotion with reinforcement learning using bioinspired reward reshaping strategies.Medical & biological engineering & computing · 2021Article
- Machine learning in the prediction of cancer therapy.Computational and structural biotechnology journal · 2021Review
- Artificial Intelligence and Dialysis.Kidney diseases (Basel, Switzerland) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Reinforcement learning has achieved tremendous success in recent years, notably in complex games such as Atari, Go, and chess. In large part, this success has been made possible by powerful function approximation methods in the form of deep neural networks. The objective of this paper is to introduce the basic concepts of reinforcement learning, explain how reinforcement learning can be effectively combined with deep learning, and explore how deep reinforcement learning could be useful in a medical context.
Indexed as
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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.