arXiv:2606. 01028v1 Announce Type: new Abstract: Medical treatment recommendation poses several challenges to reinforcement learning (RL): patient physiology evolves in continuous time, measurements and interventions are performed at irregular intervals, and treatment effects vary substantially across individuals.
By Yuepeng Wang, Ken Kawano, Yongqi Zhou, Yoshihiko Fujisawa, Richard Weiss, Akifumi Wachi, Katsuki Fujisawa, Ying Chen, Mehrshad Sadria, Xin Liu, Kyoung-Sook Kim, Xiao Hu, Sebastien Gros, Xun Shen
arXiv:2512.08029v4 Announce Type: replace
Abstract: Clinical decision-making in oncology requires forecasting how disease evolves under treatment, yet most AI systems remain static predictors that ca...
By Tianxingjian Ding, Yuanhao Zou, Chen Chen, Mubarak Shah, Yu Tian
The paper presents a study on adaptive chemotherapy control using deep reinforcement learning (DRL) to address tumor heterogeneity and drug resistance. Closed‑loop DRL dosing policies—continuous (TD3) and discrete (DQN)—are trained on a high‑dimensional heterogeneous tumor model and benchmarked against a Pontryagin's Maximum Principle (PMP) open‑loop solution. Across a 100‑patient virtual cohort with ±10% parameter perturbations, TD3 achieves higher average tumor reduction, while DQN offers tighter inter‑patient dosing consistency, highlighting an efficacy‑consistency trade‑off. The work assumes full observation of tumor subpopulations, noting that clinical translation will require handling sparse, noisy measurements.
By Bereket Sitotaw Kidane, Md Samiul Haque Motayed, Shuo Wang
The paper introduces MedGym, a benchmark environment for dynamic medical treatment recommendation that models patient evolution in continuous time using Physics-Informed Neural Networks. It addresses gaps in existing reinforcement learning (RL) approaches by allowing evaluation of RL methods under irregular measurement intervals, personalized treatment responses, and safety considerations. MedGym enables direct comparison between discrete-time and continuous-time RL methods and supports clinically relevant metrics such as personalization and trajectory-level safety.
By Yuepeng Wang, Ken Kawano, Yoshihiko Fujisawa, Yongqi Zhou, Akifumi Wachi, Mehrshad Sadria, Lei Zhou, Richard Weiss, Katsuki Fujisawa, Ying Chen, Xin Liu, Kyoung-Sook Kim, Xiao Hu, Sebastien Gros, Xun Shen
arXiv:2606. 17405v1 Announce Type: new Abstract: Clinical decision support AI systems (CDSASs) must adapt to evolving patient conditions in real-time while adhering to strict safety constraints.
By Xinyu Qin, Anil K. Sood, Ruiheng Yu, Sara Corvigno, Elaine Stur, Lu Wang
arXiv:2606. 16721v1 Announce Type: new Abstract: Medical diagnosis and treatment are dynamic processes in which patient states evolve over time and clinical interventions alter future outcomes.
By Ke Liu, Mengxuan Li, Yanyi Bao, Tianyun Zhang, Chong Chu, Jiajun Bu, Haishuai Wang