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Artificial Intelligence in Cancer Care Artificial Intelligence in Cancer Care: An Environmental Scan Production of this report has been made possible through a financial contribution from Health Canada, through the Canadian Partnership Against Cancer. Canadian Partnership Against Cancer (2019). Artificial Intelligence in Cancer Care: An Environmental Scan. Toronto, ON: Canadian Partnership Against Cancer. ISBN 978-1-988000-42-8 This report was prepared by Research Power Inc. July 2019 Table of Contents Executive Summary ....................................................................................... 1 Introduction .............................................................................................. 5 Methodology.............................................................................................. 6 Research Question...................................................................................... 6 Literature Review ....................................................................................... 6 Key Informant Interviews................................................................................ 8 Analysis................................................................................................ 8 AI Innovation Examples ................................................................................. 9 Considerations ......................................................................................... 9 Findings .................................................................................................. 10 Organization of the Findings............................................................................. 10 Overview............................................................................................... 10 What is Artificial Intelligence?......................................................................... 10 Support for AI Development in Canada................................................................ 12 Applying AI in Health Care ............................................................................ 12 Artificial Intelligence in Cancer Care...................................................................... 14 Prediction and Early Detection of Disease ............................................................. 15 Diagnostics.......................................................................................... 15 Treatment Planning and Decision-making ............................................................. 19 Managing Symptoms and Complications .............................................................. 22 Evaluating Quality of Care ............................................................................ 22 Process Eficiencies and Support for Health Care Providers ............................................. 23 Improving Patient Experience. 23 Considerations for Implementation...................................................................... 24 The Ethics of AI ...................................................................................... 24 Access to Data, Patient Privacy and Data Security ...................................................... 26 Equity............................................................................................... 27 Transparency, Replication, Validation, and Testing ..................................................... 28 Impact on the Health Workforce ...................................................................... 29 Costs and Cost-efectiveness ......................................................................... 30 Moving AI from Research to Practice .................................................................. 30 Conclusion and Next Steps................................................................................. 31 Appendix A: Key Informants ................................................................................ 32 Appendix B: Interview Guide ............................................................................... 33 Appendix C: AI Innovations ................................................................................. 35 Appendix D: Literature Review Tables ....................................................................... 45 References ................................................................................................ 56 ACRONYMS Acronym Term AI Artificial intelligence AML Acute myeloid leukemia ANN Artificial neural network CADe Computer-aided detection CADx Computer-aided diagnosis CNN Convolutional neural network DCNN Deep convolutional neural network DL Deep learning EHR Electronic health record KNN K-nearest neighbour LLM Logic learning machine ML Machine learning NHS National health service NLP Natural language processing NN Neural network NSCLC Non-small cell lung cancer PNN Probabilistic neural network RF Random forest SBRT Stereotactic body radiation therapy SREs Skeletal-related events SRS Stereotactic radiosurgery SVM Support vector machine VTE Venous thromboembolism Executive Summary INTRODUCTION scan. This report presents findings from a review of the grey and academic literature and interviews with five Health care in Canada faces geographic barriers to Canadian key informants. access such as large distances between health care delivery centres, population concentrated in the southern part of the country, and a federated health care system. As the number of Canadians diagnosed FINDINGS: AI IN CANCER CARE with cancer increases, new models of care delivery that Technology is increasingly being used across the health improve access and eficiency and reduce costs are care system to improve the quality and eficiency of needed. The Canadian Partnership Against Cancer care. AI is one of these important technologies and has (the Partnership) is the steward of the recently refreshed the potential to change the way cancer care is delivered. 2019-2029 Canadian Strategy for Cancer Control (the In recent years, there has been an explosion of research Strategy), which articulates that technology needs exploring the use of AI in health care, and the AI market to play a more prominent role in accelerating the in health care is expected to grow exponentially going improvement of cancer outcomes for Canadians. forward. Overall in the health sector, some of the largest New technological approaches such as artificial areas of expansion for intelligence (AI) have the potential to ofer solutions AI technologies are expected to be: to these geographic challenges, as well as other challenges in the system. • Process optimization (particularly for back-end processes like procurement and scheduling, While there is no standard definition of AI, it can be as well as clinical flow processes) understood as computer programs and technologies • Preclinical research (e.g., drug discovery) that use processes that resemble human intelligence (e.g., reasoning, learning and adaptation, sensory • Clinical decision support, especially in the understanding, interaction).1 AI includes machine areas of diagnosis and prognostication learning (ML) and deep learning (DL), where machines • Using AI to interact directly with patients provide the analytic power to analyze large amounts of through patient-facing applications data and can make predictions or decisions based on the patterns the machine identifies in the data. In order • Using AI to analyze population-level data to learn more about how AI is being used in cancer to identify health trends and changes care in support of potential future work in this area, (e.g., monitoring disease spread) the Partnership has undertaken this environmental Artificial Intelligence in Cancer Care: An Environmental Scan – Canadian Partnership Against Cancer 1 While system-wide adoption of AI technologies appears • Supporting cancer treatment planning to be somewhat limited, there are many examples of and decision-making by: AI being used by individual health care organizations. − Assembling and reviewing patient clinical The areas of radiology, pathology, and dermatology data, published literature, and/or other seem to be the furthest advanced in implementing medical evidence (e.g., past treatment AI technologies. For example, in a small survey of plans) to inform individualized treatment. people working in radiology in the US, over half − Supporting the delivery of precision reported they were already using AI technology or medicine (i.e., a personalized approach planning to use it within the next two years. Data to cancer treatment and follow up care) collection and data processing tasks are the most through the ability to predict disease susceptible to being transformed with technology, progression, survival, and treatment including AI, to improve automation and eficiency. response, and adapt patient care based on these factors. In the area of precision Within cancer care specifically, the key areas in which medicine AI is also being used to support AI is being used or explored include:i drug development. • Analyzing data in support of detecting cancer • Better identifying and proactively earlier and/or identifying those at higher managing symptoms and complications risk of cancer. Some of these approaches use cancer patients may experience methods that are less invasive than traditional (e.g., depression/anxiety, hospital readmission imaging and biopsies (e.g., extracting and afer surgery, clinical deterioration). analyzing data from blood such as genetic • Making the care process more eficient markers or basic blood work results like
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