Optimization of Treatment Plans using Deep Reinforcement Learning with the Human-in-the-loop
| dc.contributor.author | Muhammad Ahmed Al-Desouki | |
| dc.date.accessioned | 2025-06-19T10:20:38Z | |
| dc.date.issued | 2024-01 | |
| dc.description.abstract | Human-Centered Artificial Intelligence (HCAI) is a philosophy that focuses on designing AI systems that prioritize human wellbeing and user experiences. Medical technologies driven by AI are developing quickly to provide useful solutions for clinical practice. Treatment plan optimization is a process that aims to improve the effectiveness and efficiency of a treatment plan for a specific medical condition. Combining Deep Reinforcement Learning (DRL) with human-in-the-loop (HITL) can optimize treatment plans by combining the expertise of human clinicians with deep reinforcement learning algorithms. This paper provides two approaches for treatment plan optimization with Proximal Policy Optimization (PPO) and Deep Q Learning (DQN). | |
| dc.identifier.uri | https://research.arabeast.edu.sa/handle/123456789/258 | |
| dc.language.iso | en | |
| dc.publisher | International Journal of Computer Applications (IJCA) | |
| dc.title | Optimization of Treatment Plans using Deep Reinforcement Learning with the Human-in-the-loop | |
| dc.type | Article |