ReviewCancers2021
Reinforcement Learning for Precision Oncology.
Review in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses that pooled it.
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
33 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Reinforcement Learning Algorithms and Applications in Healthcare and Robotics: A Comprehensive and Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Review
- Advances and Future Directions in Antibody-Drug Conjugates: From Paradigm Shifts to Data-Driven Design.Cancers · 2026Review
- Cutting-edge advances in endocrine therapy for breast cancer (Review).Oncology letters · 2026Review
- Adjuvant nivolumab vs pembrolizumab in stage IIB/IIC melanoma: a reinforcement learning-based simulation study.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Next-generation hybrid bioanalytical platforms and clinical integration of TDM technologies for precision monitoring to optimize last-resort antibiotic therapy.Frontiers in pharmacology · 2026Review
- Artificial intelligence for risk assessment and outcome prediction in malignant haematology.British journal of haematology · 2026Review
- The spatial revolution in immuno-oncology: artificial intelligence decoding NK cell niches to predict therapeutic response.Frontiers in immunology · 2026Review
- Towards AI-based precision rehabilitation via contextual model-based reinforcement learning.Journal of neuroengineering and rehabilitation · 2025Article
- Using Artificial Intelligence to Develop Clinical Decision Support Systems-The Evolving Road of Personalized Oncologic Therapy.Diagnostics (Basel, Switzerland) · 2025Article
- Towards Post-Genomic Oncology: Embracing Cancer Complexity via Artificial Intelligence, Multi-Targeted Therapeutics, Drug Repurposing, and Innovative Study Designs.International journal of molecular sciences · 2025Review
- Reinforcement Learning and Its Clinical Applications Within Healthcare: A Systematic Review of Precision Medicine and Dynamic Treatment Regimes.Healthcare (Basel, Switzerland) · 2025Review
- Precision Dosing in Presence of Multiobjective Therapies by Integrating Reinforcement Learning and PK-PD Models: Application to Givinostat Treatment of Polycythemia Vera.CPT: pharmacometrics & systems pharmacology · 2025Article
- Reinforcement Learning in Personalized Medicine: A Comprehensive Review of Treatment Optimization Strategies.Cureus · 2025Review
- Personalized decision making for coronary artery disease treatment using offline reinforcement learning.NPJ digital medicine · 2025Article
- AI in optimized cancer treatment: laying the groundwork for interdisciplinary progress.Oxford open immunology · 2025Article
- Artificial Intelligence in Breast Cancer Diagnosis and Surgical Decision-Making: An Updated and Comprehensive Overview of Precision and Personalization in Current Evidence.Cancer management and research · 2025Review
- The data scientist as a mainstay of the tumor board: global implications and opportunities for the global south.Frontiers in digital health · 2025Article
- Artificial Intelligence in Head and Neck Cancer: Innovations, Applications, and Future Directions.Current oncology (Toronto, Ont.) · 2024Review
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
Abstract
Precision oncology is grounded in the increasing understanding of genetic and molecular mechanisms that underly malignant disease and offer different treatment pathways for the individual patient. The growing complexity of medical data has led to the implementation of machine learning techniques that are vastly applied for risk assessment and outcome prediction using either supervised or unsupervised learning. Still largely overlooked is reinforcement learning (RL) that addresses sequential tasks by exploring the underlying dynamics of an environment and shaping it by taking actions in order to maximize cumulative rewards over time, thereby achieving optimal long-term outcomes. Recent breakthroughs in RL demonstrated remarkable results in gameplay and autonomous driving, often achieving human-like or even superhuman performance. While this type of machine learning holds the potential to become a helpful decision support tool, it comes with a set of distinctive challenges that need to be addressed to ensure applicability, validity and safety. In this review, we highlight recent advances of RL focusing on studies in oncology and point out current challenges and pitfalls that need to be accounted for in future studies in order to successfully develop RL-based decision support systems for precision oncology.
Indexed as
Identifiers
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.