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Artificial Intelligence in Adaptive Radiation Therapy
CBCT or when propagating contours from one volumetric image to a new
volumetric image. Registrations can be computationally demanding, requiring
much time to perform, and lead to delays in the adaptive proton workflow. AI
can significantly reduce the time required for performing the registrations [24].
Chapter 9 looks at the use of AI for deformable and rigid registration as it pertains
to the general development of AI deformable registration tools. Specifically to
proton therapy, some groups have begun to develop tools to evaluate the robustness
of the deformable registration, performed with or without AI assistance [25–29], or
used expert analysis to evaluate the contours propagated with the deformable
registration [30].
18.2.3 Contour propagation
While AI tools have been developed and validated for initial contouring of patient
anatomy and treatment targets (chapter 9), the same tools can be used for daily
adaptive therapy. Since other AI contouring tools are designed to generate new
contours without a patient specific prior, a recent study focused on adaptive proton
therapy workflows incorporated the planning CT contours and generated patient
specific models for the daily adaptive workflow [31, 32]. More work is needed to
develop more robust, rapid, and reliable daily contouring tools for daily adaptive
proton therapy, including rapid quality assurance reviews of the contours [25].
18.2.4 Dose calculations
The third computationally demanding step in the adaptive proton therapy workflow
is the dose calculation. While the pencil beam and, more recently, the Monte Carlo,
dose calculations can be performed in less than 1 min [33], there are opportunities to
use AI as a secondary dose check, refine the pencil beam dose calculation in the local
scatter inaccuracies, or perform a full dose model [1, 34–39]. There is significant
overlap between the dose calculation and plan optimization, given that each
scenario of an analytic plan optimization requires at least an estimate of the dose
distribution. The use of AI for proton dose calculations is at an early stage but
multiple groups are developing AI tools to increase the speed of the dose calculations
and potentially improve the final plan through more optimal plan generation.
18.2.5 Plan optimization
Plan optimization for proton therapy can be a computationally demanding process,
with some current commercial optimization algorithms requiring minutes to hours for
the creation of multiple potential plans used for the calculation of Pareto optimal
solutions. While there is extensive work ongoing to develop AI tools for rapid plan
optimization, few studies have looked specifically at the area of online proton
plan optimization/reoptimization with AI algorithms [40]. The issue of rapid plan
optimization for adaptive proton therapy has been addressed with other analytic tools
(plan libraries) or approximations (limiting spots or using prior beam arrangements)
[41]. The same AI tools being developed for general plan optimization in chapter 10 can
be explored for online adaptive proton therapy.
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Artificial Intelligence in Adaptive Radiation Therapy
18.2.6 Other developments
There remain other issues in adaptive proton therapy for which AI will provide
additional improvements. Currently, the use of CBCT has been extensively studied
and validated for static anatomy. Motion artifacts are particularly challenging for
CBCT imaging. As noted in chapter 8, AI can be a powerful tool to correct the
motion artifacts in CBCT imaging [42, 43]. Such developments in the imaging of
moving anatomy will be important for proton therapy workflows.
Second, current imaging for proton therapy typically focused on the use of CT or
CBCT which are not able to provide the same soft tissue or biological information of
MRI or PET. Following the work described in chapter 8, the use of AI to provide
synthetic MR or biological information in the context of CT based workflows can
potentially provide critical information to aid the adaptation or triaging of proton
therapy workflows [44, 45].
18.3 Implementation of adaptive proton therapy
As with any adaptive radiotherapy workflow, the daily changes in the patient
contours and, when required, the treatment plan must be reviewed with appropriate
quality assurance. The need to verify any updated contours was discussed above and
remains an area of research, in particular considering AI tools to increase the speed
and accuracy of the contour reviews. In addition, there is need to perform secondary
verification of the updated plan, as noted above with respect to secondary dose
calculations.
There have been recent reports of quality assurance methods for adaptive proton
therapy [3, 46, 47]. The current publications have not explicitly included AI tools in
the online quality assurance checks but the need for rapid, robust, and reliable QA
presents a strong case for incorporating AI tools in the implantation of adaptive
proton therapy workflows. Additionally, there are multiple groups deploying
adaptive proton therapy workflows including PSI in Switzerland and a collaboration
between IBA, Raystation, UC Leuven, and UMCG (ProtonART). The collaborative model allows for the deployment of commercially developed tools in
Raystation combined with vendor support from IBA and clinical experts.
18.4 Summary
Proton therapy might provide clinically superior dose distributions for some treatment locations as a result of the Bragg peak depth dose distribution which allows for
sparing of distal tissues. Due to the sensitivity of the Bragg peak location in the
patient, the precision of the dose distribution requires knowledge of the integral
tissue composition, density, and magnitude. Daily set-up uncertainties, motion due
to bowels or breathing, and weight or tumor changes can all contribute to variations
in the proton Bragg peak locations resulting in dose delivery differences in the target
as well as other organs in close proximity to the target. To maximize the advantages
of the proton dose distribution, adaptive workflows have been proposed to measure
the changes of the integral tissues and adjust the position of the Bragg peak to stop
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Artificial Intelligence in Adaptive Radiation Therapy
at the intended position more precisely. The current adaptive proton therapy
workflows can require significant time and computational resources to image,
measure, adjust, and recalculate the optimal dose distribution for the current patient
anatomy. Artificial intelligence can provide dramatic reductions in time and
improved image quality to detect the anatomic variations.
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IOP Publishing
Artificial Intelligence in Adaptive Radiation Therapy
Yi Wang and X. Sharon Qi
Chapter 19
Artificial intelligence in clinical trials
Sang Ho Lee, Huaizhi Geng and Ying Xiao
Artificial intelligence (AI) is transforming clinical trials, making them more efficient,
flexible, and focused on patient needs. Clinical trials have always been essential to
medical progress, providing the strong evidence needed to ensure that new treatments, drugs, and medical devices are safe and effective. However, traditional trials
can be costly, time-consuming, and sometimes difficult to organize, especially when
it comes to finding and enrolling the right participants. AI offers new ways to
address these challenges by making trials more adaptive and data-driven, allowing
researchers to adjust plans based on patient-specific information and ongoing
results. This chapter discusses the importance of clinical trials in healthcare, explains
key trial methods, and explores how AI is changing the way trials are designed and
run. From improving participant selection to using digital twin (DT) technology for
personalized trial plans, AI is making trials more accurate and responsive. The
chapter also covers important ethical and regulatory issues to consider when
applying AI in clinical research. With these advances, AI has the potential to
improve the speed, quality, and impact of clinical trials, leading to faster and more
reliable medical discoveries.
19.1 Designing clinical trials with AI
19.1.1 The essential role of clinical trials
Clinical trials are research studies conducted to evaluate new medical treatments,
drugs, or devices. Clinical trials are pivotal in advancing healthcare technology,
serving as the foundation for evaluating new medical innovations. These trials are
instrumental in assessing the safety, efficacy, and overall benefit of emerging medical
products, including drugs, devices, and health system interventions [1–3]. They offer
a critical platform for understanding the mechanisms, therapeutic effects, and
potential adverse impacts of new technologies, thus playing a key role in their
development.
doi:10.1088/978-0-7503-6119-4ch19 19-1 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
Particularly in the field o f medicine, clinical trials facilitate the testing
and validation of novel technologies such as biomedical imaging, digital data
management, and online software solutions [4–6]. These trials integrate patient
engagement, data science, and technological advancements to forge innovative
care pathways [7]. The incorporation of electronic health records, mobile applications, and wearable devices is revolutionizing clinical trials, making them more
efficient and pragmatic [8].
Furthermore, clinical trials are crucial for generating evidence that shapes clinical
practices and drug development [9]. They enable the comparison of new advancements to conventional treatments using rigorous scientific methods [10], thereby
establishing their value and efficacy. The evolving nature of clinical trials, influenced
by regulatory and technological advancements, continually refines their designs and
capabilities [11].
Clinical trials contribute to the optimization of current clinical procedures and
ensure that novel treatments meet the safety and efficacy standards required for
FDA approval. Clinical trials are essential in developing and implementing new
technologies in healthcare, providing critical evidence-based data to support their
application in clinical practice.
19.1.2 Trial protocols and methodologies
Clinical trial design methodologies encompass a range of statistical and practical
considerations. Chow [12] and Onken [13] both emphasized the importance of
randomization, blinding, and sample size determination in ensuring the validity and
reliability of trial results. Sverdlov [ 14] and Hee [15] further expanded on these
principles, with Sverdlov focusing on optimal designs for different stages of drug
development and Hee discussing the application of Bayesian decision theory in small
trials and pilot studies. Collectively, these methodologies aimed to enhance the
efficiency and quality of clinical trials.
Clinical trial design methodologies include various approaches such as singlearm, placebo-controlled, crossover, factorial, noninferiority, and diagnostic device
validation designs [16]. Bayesian clinical trial design methodology is used for
evaluating the effect of an investigational product on both recurrent event and
terminating event processes [17]. Another method involves enrolling patient candidates based on the predicted progression of a condition and analysing subsets of
clinical trial data to generate measures of effi cacy [ 18]. Optimal designs are used for
different stages of clinical drug development, including phase I dose–toxicity studies,
phase I/II studies, phase II dose–response studies, phase III randomized controlled
multi-arm multi-objective clinical trials, and population pharmacokinetics –pharmacodynamics experiments [19]. Randomized and controlled clinical trials are considered the gold standard, but the design should be tailored to the specific research
question and objectives [20].
A good clinical trial protocol should include a clear research question, a detailed
methodology, and a study schedule and costing [21,
22]. It should also address
potential bias through blinding and random allocation of subjects [13].
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Artificial Intelligence in Adaptive Radiation Therapy
Furthermore, it must adhere to ethical principles, such as respect for persons,
beneficence, and justice, as well as good clinical practice guidelines [23].
A good clinical trial protocol should include key elements such as a rationale for
the study, a clear method description, measures to ensure subject safety, information
about research funders and organizational details, and a plan for monitoring the
trial [24]. Additionally, protocols should align with routine clinical techniques and
standard clinical processes to increase the likelihood of successful execution [25].
They should also incorporate principles of good clinical practice (GCP) to ensure
rigor, reproducibility, and transparency in scientific research [26]. Other important
elements include predefined analysis plans, standardization of procedures across
sites, assurance of staff competence, transparent data coding and entry, regular
quality assurance, and open publication of data [27]. Furthermore, guideline
protocols should be prepared and published to clarify the purpose and scope of
the guideline, facilitate the development process, ensure integrity and quality, and
avoid duplication [28]. Overall, a good clinical trial protocol should be comprehensive, transparent, and aligned with established standards and guidelines.
19.1.3 AI-driven clinical trial design and execution
AI has the potential to significantly impact clinical trial design and execution. It can
be used to reshape key steps of trial design, such as patient cohort selection and
monitoring, leading to increased success rates [29]. AI can also accelerate clinical
testing by automating tasks and optimizing patient selection [30]. The opportunities
are significant. AI can create efficiencies in various aspects of clinical trials, such as
reducing sample sizes, improving enrollment, and conducting faster and more
optimized adaptive trials [3]. AI technologies such as deep learning, neural networks,
and natural language processing have been applied in disease diagnosis, personalized treatment, drug discovery, and forecasting epidemics or pandemics [31].
AI-driven platforms can efficiently identify potential trial patients by applying
clinical trial criteria to real-world data, reducing patient recruitment timelines by
months [32]. However, AI presents both challenges and opportunities in clinical trial
design and execution. The challenges include ethical concerns, data availability, and
lack of regulatory guidance, which hinder the acceptance of AI tools in drug
development [33]. The implementation of AI in clinical practice is still at an early
stage, and more research is needed to assess its benefits and challenges [34].
However, as regulators provide more guidance, its scope of use is expected to
broaden rapidly [35].
19.1.4 Incorporation of digital twins (DTs) in clinical trials
DT technology represents a transformative approach to enhancing clinical trials,
offering a revolutionary means of understanding and predicting the outcomes of
medical interventions. At its core, this technology involves creating a virtual replica
of each patient, meticulously crafted using advanced AI methods. This DT
integrates a comprehensive array of data, encompassing the patient’s real-world
medical history, physiological and molecular characteristics, and baseline health
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Artificial Intelligence in Adaptive Radiation Therapy
information [36, 37]. Such an intricate synthesis allows for a deeper, more
personalized analysis of clinical trials.
The application of DTs extends across various medical disciplines, with
notable impacts in fields such as oncology and cardiology. In oncology, for instance,
DTs facilitate the simulation of diverse dosing regimens. This enables researchers to
delve into the nuances of dose–response relationships and uncover key determinants
that influence a patient’s response to treatment. Such detailed insights are invaluable
in tailoring more effective, individualized treatment strategies.
Cardiac in silico clinical trials is an area where DTs are making significant strides.
By generating personalized cardiac models based on individual clinical data, these
trials allow for the meticulous assessment of various therapies. This not only
enhances the understanding of treatment efficacy but also paves the way for more
customized therapeutic approaches [38].
A critical aspect of DT technology in clinical trials is the incorporation of
blockchain technology. By embedding these advanced cryptographic systems, the
integrity of trial data is significantly bolstered, ensuring its authenticity and
reliability. This integration also plays a crucial role in safeguarding participant
safety, a paramount concern in any clinical trial [37].
Matched pair analysis emerges as a powerful tool in this context. Researchers can
compare the outcomes of a patient undergoing a clinical trial with those predicted
for their DT. This comparison allows for a more nuanced evaluation of the
treatment’s effectiveness, providing a clearer picture of its benefits and potential
risks [39].
Moreover, the fusion of AI with in silico trials—simulated trials conducted
digitally—is set to revolutionize clinical trial design. AI’s capability to expand case
group sizes, automate and optimize trial designs, and even predict success rates,
heralds a new era of efficiency and effectiveness in clinical research. The result is a
more streamlined, accurate, and predictive trial process, potentially accelerating the
development of new treatments and therapies [40].
19.2 Implementation of AI in ongoing clinical trials
19.2.1 Integration with existing clinical trial frameworks
The key components of a clinical trial infrastructure (figure 9.1) include the
performance site, which can be academic medical centers, multi-specialty groups,
or clinical trial companies, and the staff involved such as investigators, coordinators,
raters/neuropsychologists, and managers. Other important elements are access to
study participants for enrollment, appropriate training of staff, regulatory oversight
through investigational review boards, and ancillary services. Different types of
studies, such as therapeutic, longitudinal observational, and imaging, have varying
requirements for staff and infrastructure. Efficient trial development and participant
accrual can be facilitated by parallel processing of trial approval steps, a physicianled research team, and regular meetings to foster research accountability.
Centralizing resources and expertise, providing training for clinical research staff,
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Figure 19.1. Clinical trial infrastructure overview, illustrating core components: performance sites, staff roles,
key elements, study types, and management and operations, each detailing essential elements for trial support.
developing common data elements, and evaluating effectiveness are recommended
strategies to strengthen clinical trial infrastructure [24].
Additional key components of clinical trial infrastructure include strong hospital
administrative support, clinical research staff, site-specific tumor boards, patient
care navigators, and integration of translational research infrastructure and capabilities, which is crucial in cancer trials. Financial and organizational management,
new trial feasibility assessment, standardization of procedures, compliance and
safety monitoring, pharmacy support, patient recruitment, effective marketing,
institutional support, building diverse teams, clinician engagement, and continuing
professional education are also essential for the success of clinical trials. Information
technology infrastructure and human coordinating processes facilitate sponsor/CRO
collaboration on international trials. Developing this infrastructure functions as a
quality improvement intervention, particularly in low- and middle-income countries,
and increasing efficiency in trial development is crucial [41].
The fundamental principles of trial design are also key components, summarized
from the previous section, including a priori formulation of a specific research
question, precise description of the study population, and limitation of potential
bias. Randomization and blinding of investigators and participants are critical
techniques to reduce bias, and the structure of modern trials, designed to protect
patient safety while generating safety and efficacy data, is shaped by regulations and
international standards [23].
19-5