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Artificial Intelligence in Adaptive Radiation Therapy
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IOP Publishing
Artificial Intelligence in Adaptive Radiation Therapy
Yi Wang and X. Sharon Qi
Chapter 3
Artificial intelligence in clinical decision making
Xinyu Zhang, Jiang Zhang, Xinzhi Teng, Yuanpeng Zhang and Jing Cai
With the fast development of artificial intelligence (AI) technologies, their application in clinical decision making has been broadly explored. This chapter provides an
overview and fundamental understanding of AI in clinical decision making. It starts
with a brief introduction of clinical decision making and medical AI, followed by the
development of medical AI and its algorithms designed to handle various clinical data.
Specifically, radiomics, a common method to develop models from medical images,
and the algorithms to integrate diverse clinical data types and improve interpretability
of medical AI are introduced. Then, the applications of AI in various clinical scenarios
are listed, encompassing diagnosis and disease phenotyping, personalized treatment,
as well as treatment outcome and prognosis prediction. In the end, the existing
challenges and potential future directions for further developments of medical AI are
discussed. Overall, this chapter provides insights into the current state, major
applications, and future prospects of AI in clinical decision making.
3.1 Introduction
3.1.1 Introduction of clinical decision making and AI
Clinical decision making refers to the process in which the healthcare providers
make decisions when they are diagnosing and treating patients, aiming to provide
accurate diagnosis and optimal treatment to maximize benefits for patients. It
happens in the entire process of disease management involving several aspects:
Information acquisition: Enquire into the disease history of the patient, conduct
physical and laboratory examinations, and gather other related information
to understand the patient’s condition.
Problem identification and diagnosis: Identify potential health problems by
analysing the obtained information and make a diagnosis.
Treatment planning: Recommend appropriate treatment schemes according to
diagnosis, diseaseprogress, the healthcare provider’sexperienceand knowledge,
and other conditions (economic, patient’s preference, etc).
doi:10.1088/978-0-7503-6119-4ch3 3-1 ª IOP Publishing Ltd 2025. All rights,
including for text and data mining (TDM), artificial intelligence (AI) training, and similar technologies, are reserved.
Artificial Intelligence in Adaptive Radiation Therapy
Follow-up: Monitor disease progress and treatment effect and make adjustments
if needed.
Traditional clinical decision making is an exhaustive, comparative, and selective
process. After considering the basic conditions of patients, healthcare providers will:
‘(1) list all possible actions, (2) list all possible outcomes, (3) predict the probability
of each outcome from each action, and then (4) select the best action based on
outcome likelihood and outcome utility’ [1]. In practice, this process mostly relies on
the subjective experience and professional knowledge of healthcare providers,
leading to various and suboptimal decisions. Furthermore, owing to the development of modern technology, there is an explosively increasing volume and types of
medical information, encompassing high-resolution images, continuous physiological records, genome sequencing data, and so on. Effective interpretation of such
medical big data surpasses the capability of humans alone but can be achieved by
artificial intelligence (AI) using advanced machine learning or deep learning
algorithms, thereby making more personalized and accurate clinical decisions.
In recent decades, the application of AI in medicine has been explored broadly.
The topics vary from diagnostic support systems [2] and risk prediction models [3]to
personalized medicine [4] and outcome prediction [5]. They aim to harness the
potential of AI to facilitate accurate, effective, and personalized clinical decision
making using various AI techniques. In health data analytics, AI has inherent
advantages in the efficient processing of data with large amounts and variety, and in
objective decision making. Previous research also highlighted multiple benefits of AI
applications in healthcare, including accelerating the decision-making process [6, 7],
enhancing clinical decision-making capacity [8, 9], improving patient outcomes [10,
11], and so on. In all, AI has shown great potential in the medical field. As
recognized by healthcare providers, AI could serve as a powerful tool in clinical
practice and dramatically change the overall workflow of clinical decision making in
the future.
3.1.2 The role of AI in clinical decision making
3.1.2.1 Diagnostic decision support
Diagnosis is the first critical decision healthcare providers make during the process
of disease management. In 1998, the first commercial computer-aided diagnosis
(CAD) system was approved by the United States Food and Drug Administration
(FDA) for mammography. Subsequently, more commercial CAD systems for other
medical images, such as computer tomography (CT) and magnetic resonance
imaging (MRI), received FDA approval. To date, CAD is the most widely used
application of AI in real-world clinical settings, particularly in the departments
relying highly on images (radiology, pathology, gastroscopy, etc). Unlike computer
diagnosis that aims at replacing humans, CAD provides recommendations or
potential diagnoses to healthcare providers who make the final diagnosis. The
usefulness of CAD in disease detection and diagnosis has been confirmed by
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previous research including for lung nodules [ 12, 13], calcifications [14, 15], intracranial aneurysms [16], fractures [17, 18], and so on. In addition, CAD can largely
accelerate the diagnosis process from minutes to seconds, which is critical for some
acute events, such as stroke and hemorrhage [6].
3.1.2.2 Patient profiling and precise medicine
Patient profiling involves gathering and analysing a variety of information on
patients to discover the characteristics that are relevant to disease conditions and
treatment response. Patient profiles include all the clinical, pathological, biological,
and other information, allowing AI to extract insights associated with disease
severity and classification. Many diseases, such as cancer, are heterogeneous and
contain subtypes yet to be discovered. In this regard, AI has been used in genomic
profiling and multi-omics integration for the discovery of new subtypes, contributing
to a deeper understanding of disease mechanisms and characteristics [19].
Furthermore, the capability of AI-based patient profiling in facilitating precise
medicine was also evaluated [20]. In addition, AI also plays an important role in new
drug development based on precise patient profiling [21].
3.1.2.3 Treatment assessment and prognosis prediction
The ultimate goal of clinical decision making is to improve the treatment outcome of
patients. With AI assessing treatment response and predicting prognosis, healthcare
providers are able to choose the appropriate therapeutics for patients and prevent
them from unnecessary toxicity. One current method to evaluate treatment response
is the response evaluation criteria in solid tumors (RECIST) based on tumor size
change. However, some studies noticed that tumors may change in density or
vascularization without obvious change in size [22]. For such changes that are less
perceptible to human eyes, radiomics has shown great potential by detecting the
texture changes of tumors on medical images for the prediction of treatment toxicity
[23], survival [24], metastasis [25], and recurrence [26].
3.2 AI algorithms for clinical decision making
3.2.1 Workflow of AI development in clinical decision making
The AI algorithms for clinical decision making are mostly developed by the datadriven approach where the associations between a particular clinical endpoint and
patient demographics are established in a quantitative manner. Personalized clinical
decisions can be made based on accurate predictions of individual clinical outcomes.
Several steps are generally involved during the development of AI algorithms,
including data acquisition, feature engineering, and model construction and
evaluation.
3.2.1.1 Data acquisition
The acquisition of data for the development of medical AI typically involves various
components. Routinely produced clinical data can be retrospectively exported from
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the electronic medical record system (EMRS) as texts or tables. Imaging and
structured data are mostly exported from imaging consoles or radiotherapy treatment planning systems following the digital imaging and communications in
medicine (DICOM) standard. Some non-routine data, such as region-of-interest
(ROI), that are not used for treatment plan evaluation (e.g. peritumoral region [27]),
can be either manually drawn or automatically generated. Deep-learning-based
auto-segmentation can also be used to accelerate the ROI generation, with optional
manual adjustments.
3.2.1.2 Feature engineering
Feature engineering plays a crucial role in the workflow of AI, encompassing feature
extraction, removal of non-repeatable features, and selection of relevant and
independent features.
Feature extraction involves extracting reliable and meaningful quantitative
features from multi-level data. In the context of medical images, features are
extracted to capture intensity and texture characteristics, either by predefined
mathematical formulas within a defined volume of interest or deep learning
algorithms [28]. Additionally, geometric features can be utilized to quantify the
relative positions and shape of tumors in relation to surrounding organs [10]. For
radiation dose maps, quantitative features could include dose–volume-histogram
(DVH) features and dosiomics features, which encompass radiomic-based features,
and momentum-based features [29].
The removal of non-repeatable features eliminates features that cannot be
reproduced under the same settings. Assessment of feature repeatability often
involves utilizing test–retest cohorts or introducing perturbations to generate
pseudo-test–retest cohorts [30]. By removing non-repeatable features, the generalizability of the model is enhanced, ensuring more reliable and consistent performance [31].
Feature selection focuses on identifying independent features that significantly
contribute to the desired outcome for subsequent model construction. Accurate
selection of features plays a vital role in enhancing model performance by
incorporating only the most informative and discriminative features with a minimum risk of overfitting.
3.2.1.3 Model construction and evaluation
Constructing models involves selecting appropriate algorithms based on the selected
features and the specific clinical task. Common machine learning algorithms include
support vector machines, logistic regression, k-nearest neighbors, decision trees,
random forests, and extreme gradient boosting. Area under the receiver operating
characteristic curve (AUC), accuracy, F1 score, sensitivity, specificity, precision,
positive predictive value (PPV), and negative predictive value (NPV) are commonly
used to evaluate the performance of the model. Generalizability and imbalanced
data are two factors significantly impacting model performance. Generalizability
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Figure 3.1. The process of model construction and evaluation.
Figure 3.2. Common workflow of radiomics.
refers to how well a model trained on certain clinical data performs on unseen data.
Techniques such as small-sample learning, manifold learning, and transfer learning
can improve the generalizability of models. On the other hand, imbalanced datasets
can be addressed through methods such as resampling or ensemble algorithms [32].
Figure 3.1 briefly illustrates the model construction and evaluation process.
3.2.2 Radiomics
Similar to general AI development, a typical workflow of radiomics involves data
acquisition, data preprocessing, feature extraction, and model development. The
process of data acquisition has been demonstrated in the previous section. Data
preprocessing involves enhancing data quality by harmonization and removing
irrelevant information, which can minimize bias and improve sensitivity. A large
number of quantitative handcrafted or deep learning features can then be extracted
from medical images. Finally, machine learning or deep learning models can be
constructed from the extracted radiomics features. To reduce the risk of overfitting
and enhance the model stability, several procedures can be adopted to reduce the
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number of features based on variance, collinearity, and relevance to clinical before
model development. Figure 3.2 shows the common workflow of radiomics.
Radiomics feature mapping is a high-dimensional representation that visualizes
radiomics feature distribution at different locations of the i mage. It can facilitate
the explanations of the selected radiomics features and the final developed models
by identifying the regions that contribute significantly to the positive and negative
predictions. They can be further utilized for more intuitive clinical decision
making. For example, the size of the highlighted tumor subregion based on the
mapping of the radiomics feature discovered from the global value retains the
predictive value of treatment efficacy for adjuvant chemotherapy on patients with
locoregionally advanced nasopharyngeal carcinoma (NPC) [33]. It can also be
directly applied to image mapping tasks, such as lung functional map generation
from static CT images [34].
3.2.3 Data integration by AI
The individual clinical data of a patient is composed of various aspects of clinical
information including the following categories. (i) Electronic medical record (EMR)
data: patients’ basic information, medical history, diagnostic records, treatment
plans, etc. (ii) Medical imaging data: such as x-ray images, CT scans, MRI scans,
etc. (iii) Laboratory test data: laboratory test results of blood, urine, tissue samples,
etc. (iv) Vital signs data: physiological parameters of the patient such as heart rate,
body temperature, blood pressure, etc. Data integration aims to analyse data from
various sources, enabling more comprehensive personalized medical recommendations for clinical decision making. AI, through automating data extraction and
transformation as well as various advanced techniques, can enhance the process of
data integration.
Multi-omics involves the comprehensive analysis of two or more individual omics
disciplines, such as genomics, transcriptomics, proteomics, metabolomics, and
radiomics. It combines biological information from various levels and scales to
obtain more accurate prediction. On the other hand, multi-view in machine learning
refers to the fusion of data represented by multiple different feature sets [35]. For
example, each type of electroencephalogram signal features extracted using different
methods, such as wavelet packet decomposition (WPD), short-time Fourier transform (STFT), kernel principal component analysis (kernel PCA), can be considered
as a view. In comparison to the previous two methods, multi-modal machine
learning has a broader scope as it encompasses a wider range of data types. In the
clinical context, each source or form of information can be referred to as a modality
[36]. It can be diverse data describing the same patient (such as patient records,
medical images, lab reports, etc), or it can be imaging data generated by different
imaging devices (such as x-ray, CT, MRI, etc). Multi-modal machine learning aims
to build models that can handle and correlate multi-modal data, thereby leveraging
the complementary nature of different modalities. While the three concepts are not
exactly the same, they share similarities in terms of integrating diverse information.
Whether multi-omics, multi-view, or multi-modal integration is used, it goes beyond
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simply stitching together different types of data, aiming to overcome the limitations
of individual data sources and modalities.
3.2.4 Interpretability of AI models
Model interpretability is the ability to explain and understand the internal
mechanisms and predictions of an AI model. The absence of interpretability of AI
model predictions hinders the understanding of its underlying mechanisms, thereby
the practical applications of medical AI. There are two major approaches to achieve
interpretability. The first approach involves reducing the intrinsic complexity of
machine learning models to enhance interpretability. Intrinsic interpretable machine
learning models can display the relationship between model inputs and outputs, such
as the explainable boosting machine (EBM) model. The second approach is post-hoc
interpretability, which involves conducting interpretability analysis after model
construction. Methods such as SHapley Additive exPlanations (SHAP) and local
interpretable model-agnostic explanations (LIME) fall into this category. The scope
of interpretability includes both global interpretability and local interpretability.
Global interpretability provides an overall view of the model’s features, weights,
parameters, or structure. Local interpretability explains the prediction results of
individual instances [37].
Instead of the interpretability of AI models, clinical practitioners often place more
emphasis on the interpretability of features, particularly on how features are related
to clinical objectives. The interpretability of individual features can provide meaningful explanations for the predicted results. Certain features of AI models may have
biological significance themselves. For example, the interpretability of radiomics
features can be enhanced by exploring their associations with tumor heterogeneity.
Feature importance is a common method to interpret features by revealing the
weights and magnitudes of features that are globally or locally interpretable in
complex models [38]. A widely adopted and intuitive approach is correlation
analysis, which assesses the significance of features by calculating the correlation
coefficients between the features and the target. In addition, post-hoc interpretability
methods such as SHAP can also analyse the importance of features by calculating
their contributions to the model predictions, as shown in figure 3.3.
A fuzzy rule describes a fuzzy logical relationship. It consists of fuzzy conditions
and a fuzzy conclusion and is typically structured in an ‘IF–THEN’ form. The fuzzy
conditions describe the state of the input variables, while the fuzzy conclusion
describes the state of the output variable. Fuzzy rules are interpretable because they
use natural language terms (e.g. ‘high’, ‘medium’, ‘low’) and have intuitive logical
reasoning relationships. The fuzzy model conducts fuzzy reasoning based on fuzzy
rules, and then transforms fuzzy output into specific operations. The classical
Takagi–Sugeuo–Kang (TSK) fuzzy model determines the parameters of the conclusion part of the fuzzy rule by parameter estimation [39]. The TSK fuzzy model is
widely used in the field of AI due to its good nonlinear approximation ability and
strong interpretability.
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Figure 3.3. Example of SHAP plots based on the breast cancer dataset (an open-source dataset in Python’s
scikit-learn library). (a) Waterfall plot for local interpretation . Th e x-axis represents SHAP values and the
y-axis repres ents feature names and their corresponding values in an individual sample. (b) Summary plot
for global interpretation. The x-axis represents SHAP values and the y-axis repres ents feature names.
The importance of features decreases sequentially from top to bottom.
3.3 Application of AI in clinical decision making
3.3.1 Diagnosis and disease phenotyping
Early disease detection aims to promptly identify diseases, enabling timely intervention and management. AI, particularly in conjunction with medical imaging, has
demonstrated substantial potential in disease detection. In recent years, COVID-19
has had a profound impact on global public health, for which medical imaging is
frequently employed to detect suspected COVID-19 cases. Numerous AI models
based on chest x-ray images have been developed for automated diagnosis and
enhanced accuracy in lung disease classification. For instance, an interpretable TSK
fuzzy system has been developed to leverage radiomics features extracted from chest
x-ray images to detect COVID-19, which achieved a high level of classification
accuracy while preserving interpretability [40]. Additionally, a two-step feature
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