Research Article
Optimization of Treatment Plans using Deep Reinforcement Learning with the Human-in-the-loop
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 186 - Issue 4 |
| Published: Jan 2024 |
| Authors: M.A. El-Dosuky |
10.5120/ijca2024923389
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M.A. El-Dosuky . Optimization of Treatment Plans using Deep Reinforcement Learning with the Human-in-the-loop. International Journal of Computer Applications. 186, 4 (Jan 2024), 1-5. DOI=10.5120/ijca2024923389
@article{ 10.5120/ijca2024923389,
author = { M.A. El-Dosuky },
title = { Optimization of Treatment Plans using Deep Reinforcement Learning with the Human-in-the-loop },
journal = { International Journal of Computer Applications },
year = { 2024 },
volume = { 186 },
number = { 4 },
pages = { 1-5 },
doi = { 10.5120/ijca2024923389 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2024
%A M.A. El-Dosuky
%T Optimization of Treatment Plans using Deep Reinforcement Learning with the Human-in-the-loop%T
%J International Journal of Computer Applications
%V 186
%N 4
%P 1-5
%R 10.5120/ijca2024923389
%I Foundation of Computer Science (FCS), NY, USA
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).
References
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