EDUCATIONAL REPOSITORY
Welcome to CADRA's educational repository.
With the rapid growth of artificial intelligence (AI) usage in healthcare and radiation medicine, navigating available resources can be challenging. CADRA offers a curated collection of essential materials, from books and academic papers to self-paced and immersive courses, designed to help you understand AI concepts, explore its applications, and develop expertise in the field.
Please note: some resources are exclusive to members of partner organizations.
Click on any of the buttons below to get started.
Is there an educational resource you’d like to see added? Let us know! hello@cadra-acadr.ca
Books
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Explore the theory and practical applications of artificial intelligence (AI) and machine learning in healthcare. This book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare and big data challenges.
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AI is radically transforming business. Are you ready?
Look around you. Artificial intelligence is no longer just a futuristic notion. It's here right now--in software that senses what we need, supply chains that "think" in real time, and robots that respond to changes in their environment. Twenty-first-century pioneer companies are already using AI to innovate and grow fast. The bottom line is this: Businesses that understand how to harness AI can surge ahead. Those that neglect it will fall behind. Which side are you on?
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The hidden costs of artificial intelligence—from natural resources and labor to privacy, equality, and freedom
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Melanie Mitchell separates science fact from science fiction in this sweeping examination of the current state of AI and how it is remaking our world
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Medicine has become inhuman, to disastrous effect. The doctor-patient relationship--the heart of medicine--is broken: doctors are too distracted and overwhelmed to truly connect with their patients, and medical errors and misdiagnoses abound. In Deep Medicine, leading physician Eric Topol reveals how artificial intelligence can help. AI has the potential to transform everything doctors do, from notetaking and medical scans to diagnosis and treatment, greatly cutting down the cost of medicine and reducing human mortality. By freeing physicians from the tasks that interfere with human connection, AI will create space for the real healing that takes place between a doctor who can listen and a patient who needs to be heard.
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AI is poised to transform every aspect of healthcare, including the way we manage personal health, from customer experience and clinical care to healthcare cost reductions. This practical book is one of the first to describe present and future use cases where AI can help solve pernicious healthcare problems.
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Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare.
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This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.
Articles
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This brief review summarizes the major applications of artificial intelligence (AI), in particular deep learning approaches, in molecular imaging and radiation therapy research. To this end, the applications of artificial intelligence in five generic fields of molecular imaging and radiation therapy, including PET instrumentation design, PET image reconstruction quantification and segmentation, image denoising (low-dose imaging), radiation dosimetry and computer-aided diagnosis, and outcome prediction are discussed. This review sets out to cover briefly the fundamental concepts of AI and deep learning followed by a presentation of seminal achievements and the challenges facing their adoption in clinical setting.
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Artificial intelligence (AI) has the potential to fundamentally alter the way medicine is practised. AI platforms excel in recognizing complex patterns in medical data and provide a quantitative, rather than purely qualitative, assessment of clinical conditions. Accordingly, AI could have particularly transformative applications in radiation oncology given the multifaceted and highly technical nature of this field of medicine with a heavy reliance on digital data processing and computer software. Indeed, AI has the potential to improve the accuracy, precision, efficiency and overall quality of radiation therapy for patients with cancer. In this Perspective, we first provide a general description of AI methods, followed by a high-level overview of the radiation therapy workflow with discussion of the implications that AI is likely to have on each step of this process. Finally, we describe the challenges associated with the clinical development and implementation of AI platforms in radiation oncology and provide our perspective on how these platforms might change the roles of radiotherapy medical professionals.
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Artificial intelligence (AI) is emerging as a technology with the power to transform established industries, and with applications from automated manufacturing to advertising and facial recognition to fully autonomous transportation. Advances in each of these domains have led some to call AI the “fourth” industrial revolution [1]. In healthcare, AI is emerging as both a productive and disruptive force across many disciplines. This is perhaps most evident in Diagnostic Radiology and Pathology, specialties largely built around the processing and complex interpretation of medical images, where the role of AI is increasingly seen as both a boon and a threat. In Radiation Oncology as well, AI seems poised to reshape the specialty in significant ways, though the impact of AI has been relatively limited at present, and may rightly seem more distant to many, given the predominantly interpersonal and complex interventional nature of the specialty. In this overview, we will explore the current state and anticipated future impact of AI on Radiation Oncology, in detail, focusing on key topics from multiple stakeholder perspectives, as well as the role our specialty may play in helping to shape the future of AI within the larger spectrum of medicine.
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Artificial intelligence (AI) has exceptional potential to positively impact the field of radiation oncology. However, large curated datasets - often involving imaging data and corresponding annotations - are required to develop radiation oncology AI models. Importantly, the recent establishment of Findable, Accessible, Interoperable, Reusable (FAIR) principles for scientific data management have enabled an increasing number of radiation oncology related datasets to be disseminated through data repositories, thereby acting as a rich source of data for AI model building. This manuscript reviews the current and future state of radiation oncology data dissemination, with a particular emphasis on published imaging datasets, AI data challenges, and associated infrastructure. Moreover, we provide historical context of FAIR data dissemination protocols, difficulties in the current distribution of radiation oncology data, and recommendations regarding data dissemination for eventual utilization in AI models. Through FAIR principles and standardized approaches to data dissemination, radiation oncology AI research has nothing to lose and everything to gain.
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Artifical Intelligence (AI) was reviewed with a focus on its potential applicability to radiation oncology. The improvement of process efficiencies and the prevention of errors were found to be the most significant contributions of AI to radiation oncology. It was found that the prevention of errors is most effective when data transfer processes were automated and operational decisions were based on logical or learned evaluations by the system. It was concluded that AI could greatly improve the efficiency and accuracy of radiation oncology operations.
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Artificial intelligence (AI) seems to be bridging the gap between the acquisition of data and its meaningful interpretation. These approaches, have shown outstanding capabilities, outperforming most classification and regression methods to date and the ability to automatically learn the most suitable data representation for the task at hand and present it for better correlation. This article tries to sensitize the practising radiation oncologists to understand where the potential role of AI lies and what further can be achieved with it.
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Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks (DNNs), many AI-based methods have been proposed to address challenges in different aspects of radiotherapy. Commercial vendors have started to release AI-based tools that can be readily integrated to the established clinical workflow. To show the recent progress in AI-aided radiotherapy, we have reviewed AI-based studies in five major aspects of radiotherapy, including image reconstruction, image registration, image segmentation, image synthesis, and automatic treatment planning. In each section, we summarized and categorized the recently published methods, followed by a discussion of the challenges, concerns, and future development. Given the rapid development of AI-aided radiotherapy, the efficiency and effectiveness of radiotherapy in the future could be substantially improved through intelligent automation of various aspects of radiotherapy.
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Artificial intelligence (AI) has already been implemented widely in the medical field in the recent years. This paper first reviews the background of AI and radiotherapy. Then it explores the basic concepts of different AI algorithms and machine learning methods, such as neural networks, that are available to us today and how they are being implemented in radiotherapy and diagnostic processes, such as medical imaging, treatment planning, patient simulation, quality assurance and radiation dose delivery. It also explores the ongoing research on AI methods that are to be implemented in radiotherapy in the future. The review shows very promising progress and future for AI to be widely used in various areas of radiotherapy. However, basing on various concerns such as availability and security of using big data, and further work on polishing and testing AI algorithms, it is found that we may not ready to use AI primarily in radiotherapy at the moment.
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Recent advances in computing capability allowed the development of sophisticated predictive models to assess complex relationships within observational data, described as Artificial Intelligence. Medicine is one of the several fields of application and Radiation oncology could benefit from these approaches, particularly in patients’ medical records, imaging, baseline pathology, planning or instrumental data. Artificial Intelligence systems could simplify many steps of the complex workflow of radiotherapy such as segmentation, planning or delivery. However, Artificial Intelligence could be considered as a “black box” in which human operator may only understand input and output predictions and its application to the clinical practice remains a challenge. The low transparency of the overall system is questionable from manifold points of view (ethical included). Given the complexity of this issue, we collected the basic definitions to help the clinician to understand current literature, and overviewed experiences regarding implementation of AI within radiotherapy clinical workflow, aiming to describe this field from the clinician perspective.
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Outside of randomized clinical trials, it is difficult to develop clinically relevant evidence-based recommendations for radiation therapy (RT) practice guidelines owing to lack of comprehensive real-world data. To address this knowledge gap, we formed the Learning from Analysis of Multicenter Big Data Aggregation consortium to cooperatively implement RT data standardization, develop software solutions for data analysis, and recommend clinical practice change based on real-world data analyzed. The first phase of this “Big Data” study aimed at characterizing variability in clinical practice patterns of dosimetric data for organs at risk (OARs) that would undermine subsequent use of large-scale, electronically aggregated data to characterize associations with outcomes. Evidence from this study was used as the basis for practical recommendations to improve data quality.
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Artificial Intelligence (AI) is currently being introduced into different domains, including medicine. Specifically in radiation oncology, machine learning models allow automation and optimization of the workflow. A lack of knowledge and interpretation of these AI models can hold back wide-spread and full deployment into clinical practice. To facilitate the integration of AI models in the radiotherapy workflow, generally applicable recommendations on implementation and quality assurance (QA) of AI models are presented. For commonly used applications in radiotherapy such as auto-segmentation, automated treatment planning and synthetic computed tomography (sCT) the basic concepts are discussed in depth. Emphasis is put on the commissioning, implementation and case-specific and routine QA of AI models needed for a methodical introduction in clinical practice.
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This study will evaluate radiation medicine professionals’ perceptions of clinical and professional risks and benefits, and the evolving roles and responsibilities with artificial intelligence (AI).
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In recent decades, artificial intelligence (AI) tools have been applied in many medical fields, opening the possibility of finding novel solutions for managing very complex and multifactorial problems, such as those commonly encountered in radiotherapy (RT). We conducted a PubMed and Scopus search to identify the AI application field in RT limited to the last four years. In total, 1824 original papers were identified, and 921 were analyzed by considering the phase of the RT workflow according to the applied AI approaches. AI permits the processing of large quantities of information, data, and images stored in RT oncology information systems, a process that is not manageable for individuals or groups. AI allows the iterative application of complex tasks in large datasets (e.g., delineating normal tissues or finding optimal planning solutions) and might support the entire community working in the various sectors of RT, as summarized in this overview. AI-based tools are now on the roadmap for RT and have been applied to the entire workflow, mainly for segmentation, the generation of synthetic images, and outcome prediction. Several concerns were raised, including the need for harmonization while overcoming ethical, legal, and skill barriers.
Conferences/Presentations
CAMRT
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Articulate how AI may impact the field of radiation therapy
Appreciate the ways in which professions of radiation therapy, medical physics, and radiation oncology may approach such change similarly or differently
Recognize where collaboration may be important to define a responsible path forward with AI
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Discuss the impact that artificial intelligence (AI) will have on the MRT professions
Debate how the MRT profession needs to adapt and prepare for a future AI enhanced health care system
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Discuss the basics of artificial intelligence (AI), machine learning (ML) and deep learning (DL)
Describe requirements for performing these methods
Summarize how these approaches are improving nuclear medicine and medical imaging in general
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Topic 1: AI: The Invisible Assistant - Presenter: Dr. Jaron Chong, Assistant Professor at the Department of Radiology, McGill University
Topic 2: Edison Analytics Artificial Intelligence Platform - Presenter: Daniel Zikovitz, Principal Solutions Architect and Susan May, GM Healthcare
To provide an overview of how analytics, AI and radiology workflow drive better safety, quality and efficiencies in imaging
Topic 3: Next Generation Radiation Therapy, MR Linacs and Artificial Intelligence - Presenter: Tom Chadwick, Elekta Healthcare Accounts Director
1. Provide an overview of emerging technologies in Radiation Therapy
2. Present an update on MR based linac technologies
3. Discuss the use if Artificial Intelligence in Radiation Therapy
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Discuss basic concepts of machine learning and its applications to medical imaging
Describe the applications of artificial intelligence to cardiac implantable devices identification on chest radiographs
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Artificial intelligence can potentially automate many parts of image analysis, from identifying anatomic structures to detecting pathology. This may fundamentally alter the roles of medical imaging professionals. In this talk we will review examples of capabilities of current AI systems, including the work of my own team on ultrasound image analysis, and assess implications for stakeholders including patients, technologists and radiologists.
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Consider how professions in medical Imaging and radiation therapy are positioning themselves with respect to AI, including MRTs, physicians, and physicists
Map out potential future roles for MRTs within the landscape of AI
Propose elements of a formal position for MRTs on the responsible implementation of AI in medical Imaging and radiation therapy practice
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Discuss the low penetration of artificial intelligence in medicine compared to other industries
Understand the changing roles and responsibilities of healthcare professionals in the era of AI
Webinars
CAMRT
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Discuss ethical use of data to build artificial intelligence (AI).
Explore societal benefits of AI vs privacy.
Question if AI can learn without compromising patient safety.
Discuss research and development considerations.
Review considerations for AI development and deployment.
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Learn about CADRA’s mission and vision, and explain how our work aligns with the Pan-Canadian Cancer Data Strategy
Courses/Certificates
Canada
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The Michener Institute of Education at UHN
Due to the current technological revolution, more people expect new technologies to replace older ones to make processes more efficient and to reduce errors. Artificial Intelligence’s (AI) primary aim in a health-related environment is to provide clinical decision and diagnostic support by analyzing relationships between treatment options and patient outcomes. AI has also been developed for patient monitoring and care, drug development and disease prevention. This online certificate program will introduce students to the discipline of AI and how it is applied in the healthcare environment. Students will acquire data science and analytic skills, learn how to implement AI solutions and participate in creating an AI solution.
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University of Toronto
Despite promises that Artificial Intelligence (AI) will transform health care, the development and adoption of AI in health care has lagged behind other industries. Some of the causes for this lag include restrictions on the use of health care data, resistance from the clinical community, the gap between hype and reality of AI, ethical concerns, regulation of health technologies, and difficulties bridging the cultures of healthcare and engineering. Yet despite spectacular failures such as Watson Health, AI is slowly beginning to appear in health care settings, most often in the context of research, but increasingly in the form of FDA and Health Canada-approved products. The aim of this course is to build a critical understanding of end-to-end lifecycle of AI in health care, from working with raw health care data, to integration of AI with clinical workflow, through to regulatory approval. This course will be of particular interest to translational AI researchers looking to apply their work to health care, as well as health care practitioners and informaticians seeking to understand how to leverage AI in their industry.
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CAMH: The Centre for Addiction and Mental Health
Almost two million Canadians say their need for mental health services has only been met partially or not at all 1. To close this gap, mental health clinicians, administrators and policy-makers need effective options that complement traditional, face-to-face mental health supports.
An integral part of this initiative involves using technology to deliver mental health care.
The promise is not without risks. Canada needs to build capacity in digital mental health and AI, which includes high-quality training for the range of interdisciplinary health and social service professionals who deliver mental health services.
The digital and AI mental health certificate supports continuing professional development through a multimedia suite of topic-focused video, e-learning and resource collections. We are partnering with clinicians, researchers, patients and families to help health care professionals understand, access and use technology to deliver care to Canadians.
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McMaster University and the National Institute of Health Information
Participants completing this course will gain a comprehensive understanding of the three main areas of Artificial Intelligence: Artificial Narrow Intelligence (ANI), Artificial General intelligence (AGI), and Artificial Super Intelligence (ASI), within the context of health care and the data management supply chain systems found in health care. We will explore related fields in this course, including the use of robotic technologies, clinical decision support, electronic medical records, and more.
The course cover various ethical issues raised from the use of AI in the future, and other important considerations such as: regulations, data privacy, cybersecurity and data protection, and AI in contrast to public health policy aims. We will also apply the application of new AI technologies to stakeholders’ views of value, including clinicians, patients, vendors, payers, and even the investment community and capital markets. Throughout the course, we will review real-world examples, and other thought leaders’ views and articles. Most of the weekly sessions will include a dedicated discussion of the “Applications” and potential “Implications” for the use of AI in health care covered in that session’s topics.
Upon completion you will be awarded a NIHI - McMaster CE microcredential in Artificial Intelligence in Health Care: Perspectives on Data and it Uses. A microcredential is issued in a digital format, provides details of acquired competencies and is shareable and transportable.
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University of Manitoba
Learn how Artificial Intelligence and Machine Learning can solve the most important challenges in your business or organization and fuel your career. Explore the possibilities and how to apply them with this university credential offered entirely online, no coding required.
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British Columbia Institute of Technology (BCIT)
This Digital Health Advanced Certificate is aimed at post-diploma and baccalaureate healthcare students who are interested in expanding their knowledge, skills, and competencies toward the application of digital health/health informatics in the healthcare environment.
International
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Stanford University
Artificial intelligence (AI) has transformed industries around the world, and has the potential to radically alter the field of healthcare. Imagine being able to analyze data on patient visits to the clinic, medications prescribed, lab tests, and procedures performed, as well as data outside the health system -- such as social media, purchases made using credit cards, census records, Internet search activity logs that contain valuable health information, and you’ll get a sense of how AI could transform patient care and diagnoses. In this specialization, we'll discuss the current and future applications of AI in healthcare with the goal of learning to bring AI technologies into the clinic safely and ethically. This specialization is designed for both healthcare providers and computer science professionals, offering insights to facilitate collaboration between the disciplines.
Identify problems healthcare providers face that machine learning can solve
Analyze how AI affects patient care safety, quality, and research
Relate AI to the science, practice, and business of medicine
Apply the building blocks of AI to help you innovate and understand emerging technologies
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Harvard University
The Harvard Chan School Executive and Continuing Education Business Applications for AI in Health Care Certificate of Specialization is for all health care stakeholders, from the doctor’s office to the business suite. Those who will benefit from this certificate include CEOs, CIOs, Chief Medical Informatics Officers, Chief Medical Officers, Clinicians, Data Scientists, Engineers, Finance Professionals, Implementation Specialists, Product and Project Managers, and Investors.
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Massachusetts Institute of Technology
This course runs over 6 weeks with an estimated 6-8 hours per week of study time
This course is delivered in our Self-Paced Online format which enables you to participate at your own pace within weekly modules
You will learn through a variety of formats including: interactive videos, practice quizzes, presentations, assignments, and discussion forums
You will have access to a Success Adviser who will help you manage your time, and support you with any administrative or technical queries you might have
You will earn a certificate of course completion from the MIT Sloan School of Management
Programs
Digital Health
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University of Toronto
The Digital Health Technologies (DHT) stream covers similar content by looking at regulation (with a medical device/software emphasis), drug development to provide a framework to understand major steps in medical device/software development, clinical trial design to provide the context for data output that will be analyzed (emphasis on data analysis), and drug action basics (not as technically detailed as the BioPh stream), through courses in Digital Health Technology, Data Science in Health I, Data Science in Health, Part II and Digital Ethnography in Health.
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McMaster University
McMaster’s eHealth MSc program is a unique healthcare graduate program that immerses you in the world of digital health informatics. Offered through a collaboration by three prestigious McMaster Faculties and Schools – the Faculty of Health Sciences, the Faculty of Engineering, and the DeGroote School of Business – the eHealth MSc builds on theoretical foundations in healthcare, information technology and business. An 8-month internship offers firsthand perspective on how these fields intersect in the transformation of modern healthcare delivery and management.
You do not need an information technology degree to enroll in the eHealth MSc. Our interdisciplinary approach to learning lets you identify your area of study and pursue this through elective coursework and research. The program’s flexibility means that your academic experience can be as broad or as focused as you like. Modes of study include full time course-based, full time thesis-based, and part-time options.
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McGIll University
The Digital Health Innovation Masters provides training in applied data science, clinical epidemiology, medical artificial intelligence, innovation and design thinking and informatics. It includes a Masters thesis developing and evaluating new digital technologies under the supervision of professors in various specialties of digital health in the McGill Network. Examples include the development and assessment of digitalized health and social data using specialized software, analysis of high volume streams of clinical and health-related data from clinical systems, wearables and social media, and development of new digital tools.
Health Informatics
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University of Toronto
The faculty members are the foremost thinkers, researchers, and practitioners influencing our health-care system today. Students are exposed to the latest evidence-informed research, thinking, and practice in the Health Informatics program, leading to the Master of Health Informatics (MHI) degree.
This Health Informatics professional program at the University of Toronto provides graduates with expertise in clinical information and communication technologies (ICTs) required to lead organizational and health system change. The MHI degree program prepares health informaticians to bridge the gaps between clinicians and ICT specialists. The learning experience is further enhanced by practicum placements and a curriculum shaped by strong relationships with leaders and top-performing organizations
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University of Waterloo
Designed for professionals with backgrounds in public health and/or health care: professionals who require more knowledge about computer science and health informatics in order to identify, design and manage informatics solutions relevant to health and health systems.
You will learn from faculty who lead research in public health sciences and public health intervention design and evaluation.
Through the experiential learning of a practicum position, you will experience what it is like to use the knowledge, tools, and skills learned in the MHI Program in a real public health setting.
The MHI program is flexible for the working professional and is completed online as a part-time or full-time student.
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Western University
The Master of Health Information Science (MHIS) program is a joint graduate program with the Faculty of Health Sciences. The program allows you to choose between a one-year course-based program, or a two-year thesis-based program. Both program options provide students with fundamental knowledge in health and health care, including: public health, health informatics and digital health, patient and professional information seeking behaviour, information ethics and policy. Learn at the intersection of healthcare and information management, policy, knowledge translation and clinical practice.
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University of Victoria
Students will learn to identify what information and data are needed and make effective healthcare decisions by doctors, nurses, hospital administrators, government planners, and other health care professionals.
Health information science (also known as health informatics) is the study of:
how health data are collected, stored, and communicated;
how those data are processed into health information suitable for administrative and clinical decision making; and
how computer and telecommunications technology can be applied to support these processes.
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Carlton University
Distinctive features of our MSc program in Health Science, Technology and Policy (HSTP) include an emphasis on skill acquisition, using problem-based approaches to understand health issues and sectors, and participation in collaborative interdisciplinary research projects. The program design is facilitated by the cohort model, which allows for collaboration between students from various academic backgrounds to improve their understanding of complex problems in the broad field of health, and provides opportunities for students to enhance their learning experience by working together. The professional relationships formed by students in each cohort, as well as the opportunities to meet and work with experts across health sectors and disciplines, will provide additional value to students as they prepare for their professional careers.