Advancements, Challenges and Future Prospects of Reinforcement
Learning in Healthcare
Meiyi Feng
a
School of Business, Macau University of Science and Technology, Macau Special Administrative Region, China
Keywords: Reinforcement Learning, Healthcare Application, Machine Learning.
Abstract: Reinforcement Learning (RL) has become a groundbreaking approach in machine learning, significantly
impacting healthcare by providing solutions to intricate decision-making challenges. This comprehensive
review examines the current state of RL in healthcare, focusing on dynamic treatment protocols, automated
diagnostic systems, resource allocation, as well as privacy and security issues. RL's ability to adapt and
optimize treatment plans dynamically, enhance diagnostic accuracy, and manage healthcare resources
efficiently underscores its potential to revolutionize clinical practices. However, the implementation of RL in
healthcare is fraught with challenges, including the need for extensive, high-quality datasets, difficulties in
interpreting complex models, and significant data privacy concerns. To mitigate these challenges, recent
innovations have been introduced. Transitional Variational Autoencoders (tVAEs) are used to generate
realistic patient data, enhancing the simulation capabilities of RL models. Federated learning frameworks
have been developed to ensure data privacy by enabling collaborative model training without sharing raw
data. Additionally, transfer learning and domain adaptation techniques improve the generalization of RL
models across diverse healthcare settings. This review provides a thorough analysis of these advancements
and their implications for healthcare, offering a detailed understanding of RL's current applications and
limitations. Future research directions are proposed to address existing challenges, aiming to ensure the robust,
transparent, and ethical integration of RL technologies into clinical settings, thereby maximizing their
potential to improve healthcare outcomes.
1 INTRODUCTION
Reinforcement Learning (RL) is an approach within
the field of machine learning based on reward
mechanisms, which has gained widespread attention
and application in the healthcare sector in recent
years. RL interacts with the environment to
continuously adjust strategies to achieve the goal of
maximizing long-term rewards. Its unique features
make it particularly suitable for addressing complex
decision-making problems in healthcare, such as
optimizing disease diagnosis and treatment plans (Yu,
Liu, Nemati & Yin, 2021; Coronato et al., 2020). In
healthcare, RL applications range from dynamic
treatment regimens to automated medical diagnosis.
For example, in chronic disease management and
intensive care, RL can help optimize treatment plans
by dynamically adjusting drug dosages and treatment
strategies to improve therapeutic outcomes (Yu, Liu,
a
https://orcid.org/0009-0003-7693-933X
Nemati & Yin, 2021; Coronato et al., 2020).
Additionally, RL is used in medical image analysis
and disease prediction, such as early detection of lung
cancer and vessel centerline tracking (Yu, Liu,
Nemati & Yin, 2021; Yang et al. 2024).
Despite the promising prospects of RL in
healthcare, several challenges and gaps remain in
current research. First, training RL models often
relies on large amounts of high-quality data, which
are costly and ethically challenging to obtain in the
healthcare domain (Yu, Liu, Nemati & Yin, 2021).
Second, existing RL models exhibit limitations in
handling multi-class imbalanced data, which may
lead to poor predictive performance for minority
classes in practical applications (Yang et al. 2024).
Moreover, the application of RL in healthcare faces
issues with model generalization. Many current
models perform well on specific datasets but have not
been validated across different patient populations
Feng, M.
Advancements, Challenges and Future Prospects of Reinforcement Learning in Healthcare.
DOI: 10.5220/0013205700004568
In Proceedings of the 1st International Conference on E-commerce and Arti๏ฌcial Intelligence (ECAI 2024), pages 39-44
ISBN: 978-989-758-726-9
Copyright ยฉ 2025 by Paper published under CC license (CC BY-NC-ND 4.0)
39
and medical settings (Yu, Liu, Nemati & Yin, 2021).
Additionally, data privacy and security are crucial
research directions, especially when using distributed
learning methods like federated learning (Otoum et
al. 2021).
To address these research gaps, recent studies
have proposed several improvements. For instance,
Transitional Variational Autoencoders (tVAEs) are
used to generate more realistic patient trajectories,
enhancing the model's ability to simulate patient data.
Furthermore, RL frameworks combined with
federated learning are explored to strengthen data
protection and confidentiality measures in healthcare
IoT systems. This method allows model training
without sharing raw data, thereby protecting patient
privacy (Otoum et al. 2021).
This paper endeavours to offer an exhaustive
analysis of the main research findings and recent
advancements in the application of RL in healthcare.
This paper explores a wide range of RL applications,
including dynamic treatment regimes, automated
medical diagnosis, and healthcare resource
management. The review includes a detailed
examination of existing models, their limitations, and
the innovative solutions proposed to address these
challenges. Additionally, this review is structured to
explore the different facets and impacts of RL in
healthcare, With the aspiration of offering an all-
encompassing overview pertaining to the current
framework and prospective developments of RL
research in this significant sector.
2 METHODS
2.1 Introduction to Reinforcement
Learning
Reinforcement learning, representing a sophisticated
branch within the broader domain of machine
learning, emphasizes enabling an agent to learn
optimal decision-making through interactions with its
environment. The fundamental principle involves the
agent performing actions in different states with the
aim of optimizing aggregate rewards over an
extended period. Through the acquisition of feedback
in the guise of rewards or penalties corresponding to
its actions, the agent is steered towards formulating
an optimal policy.
Reinforcement Learning challenges are
commonly framed within the construct of Markov
Decision Processes (MDPs), which are defined by the
following key tuple ๐‘†, ๐ด, ๐‘ƒ, ๐‘…, ๐›พ (Amparore et al.,
2013):
- ๐‘ : A set of states
- ๐‘Ž: A set of actions
- ๐‘ƒ : A transition likelihood matrix ๐‘ƒ(๐‘ โ€ฒ|๐‘ , ๐‘Ž) ,
defining the likelihood of shifting to states from state
๐‘  after executing an action ๐‘Ž.
- ๐‘… : A reward function ๐‘…(๐‘ , ๐‘Ž) , providing the
instantaneous reward after action ๐‘Ž in state ๐‘ .
- ๐›พ : A discount factor gamma in [0, 1], which
quantifies the significance of future rewards.
The agent's paramount goal is to devise a
strategy ๐œ‹(๐‘ )
that maximizes the anticipated
aggregated reward, commonly known as the return.
This return is calculated as the sum of discounted
rewards accrued over time, reflecting both immediate
and future benefits:
๐บ
๎ฏง
= ๐‘…
๎ฏง๎ฌพ๎ฌต
+ ๐›พ๐‘…
๎ฏง๎ฌพ๎ฌถ
+ ๐›พ
๎ฌถ
๐‘…
๎ฏง๎ฌพ๎ฌท
+ โ‹ฏ =
โˆ‘
๐›พ
๎ฏž
๐‘…
๎ฏง๎ฌพ๎ฏž๎ฌพ๎ฌต
๎ฎถ
๎ฏž๎ญ€๎ฌด
(1)
A commonly employed algorithm in RL is Q-
learning, which updates the value of state-action pairs
(Q-values) using the Bellman equation:
๐‘„(๐‘ , ๐‘Ž) โ†๐‘„(๐‘ , ๐‘Ž)+๐›ผ[๐‘… + ๐›พ๐‘š๐‘Ž๐‘ฅ
๎ฏ”๏‡ฑ
๐‘„(๐‘ โ€ฒ, ๐‘Žโ€ฒ) โˆ’
๐‘„(๐‘ , ๐‘Ž)] (2)
where ๐›ผ is the learning rate.
Through continuous interaction with its
environment, the agent progressively learns to select
actions that maximize long-term cumulative rewards.
This ability renders RL especially effective for
handling complex decision-making tasks across
various fields, such as healthcare.
2.2 Dynamic Treatment Regimes
2.2.1 Enhancing DRL with Transitional
Variational Autoencoders
In their study, Baucum and Khojandi introduce
Transitional Variational Autoencoders (tVAEs) to
improve Deep Reinforcement Learning (DRL)
applications in healthcare. Temporal Variational
Autoencoders (tVAEs) are sophisticated generative
neural network models designed to create an explicit
correlation between the configurations of clinical
parameters across sequential temporal intervals,
utilizing retrospective patient data. These models
facilitate the accurate reconstruction of temporal
patterns in clinical datasets by capturing the
underlying distributional dynamics over time. One
significant benefit of the tVAE model is its minimal
reliance on distributional assumptions while
maintaining consistent training and testing
architectures. By utilizing tVAEs, researchers can
create more realistic patient trajectories, facilitating
the development of effective treatment policies
(Baucum et al., 2020).
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2.2.2 Closed-Loop Healthcare Processing
with DRL and Human Body
Simulators
Dai et al. propose a closed-loop healthcare processing
method using DRL combined with Deep Neural
Networks (DNNs) to build human body simulators.
These simulators can dynamically accept
interventions and produce observations. By
integrating conceptual embedding techniques with
DRL, the study investigates effective healthcare
strategies. The system specifically consists of a
virtual human physiological simulator seamlessly
integrated with an advanced Deep Reinforcement
Learning (DRL)-driven therapeutic intervention
module. The treatment module diagnoses latent
health states from high-dimensional radiographic
observations and selects therapeutic actions to restore
the simulated physiological model to an optimal
healthy condition, creating
a highly adaptive,
autonomously self-regulating healthcare informatics
architecture (Dai et al., 2022).
2.2.3 Personalized Healthcare
Coronato et al. provide a comprehensive review of
RL's role in healthcare, emphasizing its potential to
support personalized treatments in precision
medicine. The document explores several RL
applications, such as Dynamic Treatment Regimes
(DTRs) for chronic illnesses like HIV, oncology,
hypertension, and anemia. Utilizing the Sequential
Multiple Assignment Randomized Trial (SMART)
approach, RL aids in developing DTRs by employing
sequential decision-making processes that consider
patient responses at each stage of treatment. This
approach aims to personalize drug dosages and
treatment schedules to maximize therapeutic
outcomes and minimize adverse effects (Coronato et
al., 2020).
2.2.4 Dual-Agent RL Model for Optimizing
Dynamic Treatment Regimes
Blumrosen et al. propose a Dual-Agent
Reinforcement Learning (DaRL) model utilizing
medical and natural agents to optimize healthcare
interventions. The medical agent employs external
data and sensors to adjust treatments, while the
natural agent's rewards are based on internal
biological states like neural circuits and dopamine
levels. This integration enhances treatment accuracy
and efficiency, dynamically adjusting drug dosages
and reducing incorrect interventions by continuously
monitoring patient health (Blumrosen et al., 2019).
2.3 Automated Medical Diagnosis
2.3.1 Automated Diagnosis with Machine
Learning Models
Structured medical data, such as physiological
signals, images, and lab tests, benefit from RL
methods for tasks like feature extraction and image
segmentation. Key techniques include Q-learning for
prostate segmentation in ultrasound images, Trust
Region Policy Optimization (TRPO) for surgical
gesture segmentation, and Deep Q-Network (DQN)
for automatic landmark detection in MRI and CT
images, achieving robustness and accuracy.
Unstructured medical data, such as clinical notes and
reports, use RL techniques for diagnosis inference.
Key methods include DQN to improve diagnosis
accuracy using external evidence, adaptive online
learning combining supervised learning for risk
assessment and RL for decision making, and
symptom checking systems using DQN to enhance
efficiency and accuracy in diagnosis (Yu, Liu, Nemati
& Yin, 2021).
2.3.2 Deep Reinforcement Learning for IoT-
Enabled Smart Healthcare Systems
Jagannath et al. present a novel IoT-enabled smart
healthcare system that utilizes DRL to automate
medical diagnosis and decision-making. The system
architecture is composed of four tiers: patient data
acquisition, edge processing, data conveyance, and
cloud computation. Patient data is gathered through
various sensors in a Body Area Network (BAN) and
sent to data centers using IoT protocols. DRL
algorithms, like Deep Q-Network (DQN), are utilized
to conduct intricate data analysis and formulate
sophisticated diagnostic and therapeutic
determinations. The system was evaluated with
synthetic data from BAN sensors, showing high
accuracy in estimating hidden health states and
making decisions that closely align with those of a
physician. This method not only improves diagnostic
accuracy but also provides an efficient telemedicine
solution (Jagannath et al., 2022).
2.3.3 Dueling Double Deep Q-Network for
Multi-Class Imbalance in Healthcare
Applications
Yang, EI-Bouri et al. introduce a dueling double deep
Q-network (D-DDQN) to tackle the issue of multi-
class imbalance in reinforcement learning,
particularly for healthcare applications. The Q-value
Advancements, Challenges and Future Prospects of Reinforcement Learning in Healthcare
41
function is optimized through policy iteration. The
dueling architecture separates value and advantage
estimation, enabling better state-action
representation. Double deep Q-learning mitigates
overestimation by using separate networks for action
selection and value estimation. This approach is
particularly beneficial in healthcare for optimizing
resource allocation and improving diagnostic
accuracy in class-imbalanced medical datasets (Yang
et al., 2024).
2.4 Health Resources Allocation and
Scheduling and Health
Management
2.4.1 Enhanced Intelligent Clustering-
Oriented Routing Framework for 5G-
Integrated Smart Healthcare Solutions
Ahad et al. introduce an sophisticated Clustering-
based Routing Protocol (CRP-GR) that integrates
game theory with reinforcement learning to improve
resource allocation within a 5G-driven smart
healthcare paradigms. This protocol is designed to
optimize energy usage and extend network lifespan
by selecting energy-efficient cluster heads and
determining the optimal multipath routes for data
transmission. By using reinforcement learning, the
protocol adapts to the heterogeneous and dynamic
nature of smart healthcare networks, improving the
quality of service (QoS) and ensuring efficient use of
resources (Ahad et al., 2021).
2.4.2 EPRAM: Enhancing Smart Healthcare
with Fog Computing
Talaat et al. introduce the Effective Prediction and
Resource Allocation Method (EPRAM) within fog
computing architectures for advanced cognitive
healthcare applications enhances real-time resource
management and prediction using deep reinforcement
learning and probabilistic neural networks. EPRAM
achieves low latency, high resource utilization, and
effective load balancing, outperforming traditional
algorithms (Talaat et al., 2022).
2.5 Security and Privacy
2.5.1 Federated Reinforcement Learning-
Augmented Intrusion Detection
Framework for IoT-Enabled
Healthcare Ecosystems
Otoum et al. discuss the implementation of an
Intrusion Detection System (FRL-IDS) which is
grounded in Federated Reinforcement Learning
designed to enhance security and privacy in IoT-
enabled healthcare systems. The proposed model
leverages federated learning to maintain data privacy
by ensuring that data remains localized while only
sharing model parameters. This approach helps in
detecting and mitigating cyber intrusions without
compromising patient data. The system processes
data from multiple healthcare entities and updates the
global detection model collaboratively, ensuring
robust and secure data communication across the
network (Otoum et al., 2021).
2.5.2 Reinforcement Learning-Based
Trajectory Design for WPT-Enabled
UAV Healthcare Delivery
Merabet et al. explore the deployment of
reinforcement learning methodologies to enhance the
security and efficiency of healthcare delivery systems
utilizing Unmanned Aerial Vehicles (UAVs). The
proposed system employs Wireless Power Transfer
(WPT) technology, enabling UAVs t
to replenish
their power reserves mid-mission, thereby extending
their operational range. The RL algorithm is used to
design optimal UAV trajectories that minimize travel
time and energy consumption while ensuring secure
data transmission during deliveries. By implementing
RL, the system can dynamically adapt to changing
environmental conditions and potential security
threats, providing a reliable and secure healthcare
delivery solution (Merabet et al., 2022).
3 DISCUSSIONS
3.1 Limitations and Challenges
3.1.1 Interpretability
RL models, particularly deep reinforcement learning,
are often termed "opaque systems" owing to the
intricate and enigmatic nature of their decision-
making processes, which are not easily interpretable
by humans. In healthcare, interpretability is crucial as
medical professionals need to trust and understand the
reasoning behind a model's recommendations. The
lack of transparency can impede the adoption of RL
in clinical settings, as practitioners may be hesitant to
trust systems they cannot fully understand. This
challenge necessitates the development of methods
that can elucidate the internal workings of RL models,
making them more accessible and trustworthy.
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3.1.2 Applicability
The application of RL in healthcare is still in its
infancy, and its practical use is limited by several
factors. Firstly, RL requires extensive amounts of
data to train models effectively, which can be a
significant barrier in medical domains where data is
often scarce and sensitive. Secondly, healthcare
environments are highly complex and dynamic,
making it difficult to design RL algorithms that can
adapt to such variability. Finally, the integration of
RL systems into existing healthcare workflows
presents another layer of complexity, as these systems
must be seamlessly incorporated into routine
practices without disrupting care delivery.
3.1.3 Privacy
Patient data privacy is a paramount concern in
healthcare. RL models require substantial amounts of
data, which raises significant privacy issues. Ensuring
that patient information is protected while using it to
train RL models is a complex task. Methods such as
data anonymization and secure data sharing protocols
are necessary but may not be sufficient to address all
privacy concerns. Additionally, the potential for data
breaches and the misuse of sensitive information are
ongoing risks that need to be continually managed
and mitigated.
3.2 Future Prospects
3.2.1 The Solutions for Interpretability
To address the interpretability challenge, integrating
expert systems with RL can provide a framework
where the decision-making process is more
transparent. Expert systems utilize heuristic-based
methodologies to emulate the cognitive judgment
capabilities of a domain-specific specialist. By
combining this with RL, it is possible to create
systems that not only learn from data but also
incorporate domain-specific knowledge that is easier
for practitioners to understand. Approaches such as
SHapley Additive exPlanations (SHAP) and Local
Interpretable Model-agnostic Explanations (LIME)
present promising methods for interpreting the
outputs of complex models. SHAP assigns an
importance value to each feature for a specific
prediction, thereby elucidating the influence of each
feature on the model's output. LINE approximates the
model locally with a simpler interpretable model,
providing insights into how the original model works
in the vicinity of the instance being predicted. These
methods can help demystify the decisions made by
RL algorithms, thereby increasing their acceptance
and trust among healthcare professionals.
3.2.2 The Solutions for Applicability
Cross-domain knowledge transfer and domain
specific adaptation hold significant potential for
enhancing the applicability of RL in healthcare.
Transfer learning entails using a model pre-trained in
one domain and fine-tuning it for a related but
different domain, thus reducing the data needed for
training in the new domain. This is especially
beneficial in healthcare, where data is frequently
scarce and costly to acquire. Domain adaptation, on
the other hand, helps models generalize across
various settings by aligning the data distributions
between the source and target domains. This ensures
that RL models can perform well even when there are
differences between the training data and the real-
world data they encounter in deployment. By
applying these techniques, RL models can become
more robust and effective in various healthcare
environments, from hospitals in urban centers to rural
clinics with different patient demographics.
3.2.3 The Solutions for Privacy
Federated learning offers a solution to the privacy
challenge by enabling the training of RL models
across multiple institutions without sharing raw data.
This approach allows models to learn from a broader
dataset while maintaining patient confidentiality. In
federated learning, data remains localized at each
institution, and only the model updates (gradients) are
shared and aggregated to improve the global model.
This decentralized approach to training models
mitigates the risks associated with centralized data
storage, such as data breaches and privacy violations.
By aggregating insights from decentralized data
sources, federated learning can help create more
comprehensive and accurate RL models. For
example, hospitals in different regions can
collaborate to train an RL model on patient treatment
outcomes without exposing sensitive patient data.
This collective learning process can result in more
effective and generalized healthcare solutions,
potentially accelerating the adoption of reinforcement
learning in the healthcare sector.
4 CONCLUSIONS
To summarize, this paper has provided a thorough
analysis of RL applications, including dynamic
Advancements, Challenges and Future Prospects of Reinforcement Learning in Healthcare
43
treatment protocols, automated diagnostic systems,
resource allocation, as well as privacy and security
measures. The primary contribution of this study lies
in highlighting RL's ability to enhance healthcare
outcomes. However, RL faces significant limitations,
such as data scarcity, model interpretability, and
privacy concerns. Future research should aim to
integrate expert systems for improved
interpretability, leverage transfer learning to enhance
applicability, and employ federated learning to
address privacy issues. These advancements will be
crucial in fully realizing the potential of RL in
healthcare, ensuring robust, transparent, and ethical
deployment in clinical settings.
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