Artificial Intelligence in Cancer Care Educational Project
Medicine is a practice that uses different sciences.
When managing serious, possibly life-threatening, diseases, we are constantly faced with two essential aspects of decision-making that appeal to what is important to treat the disease, on the one hand, and what is valuable to the person being treated, on the other. If artificial intelligence is applied to this valuable aspect, the amount of important information that can be gained for treating a disease today exceeds our capacity for immediate and integrative analysis.
The integration of big data into clinical cancer research provides an unprecedented opportunity to integrate information and complex research outputs, which in turn requires powerful computational resources. Through its formidable analytical power, artificial intelligence holds the promise of transforming the way we study, diagnose and treat cancer.
The use of machine-learning in preclinical and translational cancer research has increased rapidly in recent years, bringing exciting progress in digital pathology and diagnostics and enriching foundational and drug-discovery research. From unfolding the intricacies of multi-omics and cellular phenotypes or extracting clinically relevant patterns to using behavioral data collected from wearable devices, machine learning is revolutionizing the arena of cancer research.
Artificial intelligence has given rise to great expectations for improving cancer diagnosis, prognosis and therapy but has also highlighted some of its inherent outstanding challenges, such as potential implicit biases in training datasets, data heterogeneity and the scarcity of external validation cohorts.
The development of knowledge and competences on integration of AI in Cancer Care Continuum: from diagnosis to clinical decision-making.
The project will be supported by multimedia materials containing simulations or examples of AI applications in cancer care.
Project Learning Objectives
- To identify opportunities and challenges that may arise from the application of AI in Cancer Care Continuum
- To share best practices in the application of AI in cancer diagnosis and treatment
- To educate HCPs on the latest innovations of the application of AI to understand how it works on the clinical side
- To raise cancer patients' awareness of AI in cancer care
- To know implications of the new frontiers of AI in precision oncology
Project Target Group
HCPs - Cancer Patients Organisations – Oncologists - Researchers - Medical Companies - Cancer Institutes - Universities - Healthcare Public Authorities
Project Approach and Outcomes
SPCC will carry out a multi-step project consisting of: 1) a series of Educational Webinars; 2) a Virtual Symposium on AI in cancer care.
All the sessions (free of charge and CME accredited) will be accessible to the audience by registering on SPCC's OncoCorner e-learning platform. The materials will remain available on-demand on OncoCorner.
The first step of this project will consist of seven Educational Webinars, including multimedia examples and a panel discussion for each webinar on controversial topics, challenges and best practices of AI in cancer care.
Webinars contents will be based on published data and focus on literature or experience examples. Regulatory issues (e.g., privacy, transparency) will be presented, where are relevant for specific topics or applications.
Seven Article Reports – one after each webinar - will be produced and published on the Cancerworld magazine.
WEBINAR 1: AI in Cancer Care: an Overview
What is AI in Cancer: Basics and Settings of Application
The State-of-the Art of AI in Cancer Care: Opportunity, Bias, Barriers and Gaps
Value in AI in Cancer Care: a Systematic Patient-Oriented Innovation
What’s New in AI in Cancer Care: Progresses and Regulatory Perspective
WEBINAR 2: AI Applications in Diagnosis
Image Analysis in Digital Pathology: from Clinical to Computational Pathology
AI in Precision Testing
AI for Imaging
Machine-Assisted Medical Decision Making
WEBINAR 3: AI in Managing Clinical Data and Trials
The Value of AI in Oncology: from Research to Clinical Trials
Big Data, RWD and RWE: an AI Perspective beyond Traditional Clinical Trials
AI Applications in Real-life Clinical Practice
Patient Involvement and Empowerment in AI Clinical Dimensions
WEBINAR 4: AI in Genomics and Reporting for Clinical Practice
New Software Platforms to Improve Multidisciplinary
Tumour Board Workflows
Reporting Genomics Data
Integrating Genomic Data and Clinical Data in Practice
WEBINAR 5: AI in Robotic Surgery
Developing Robotic Surgery in Europe
AI Visualization Systems for Next Generation
Robotics and Distance Surgery
Virtual Reality and AI
WEBINAR 6: The Patient’s Perspective in AI
WEBINAR 7: Artificial Intelligence for Specific Tumours
Risk Stratification and Clinical Pathological Features in Breast and Colon Cancers and Melanoma
The Symposium will consist of a virtual round table with take-home messages presentations to be discussed between faculty and participants. It will summarize the main topics of the webinars (one for each area) with a special focus on multidisciplinary interaction.
An Article Report will be produced and published on the Cancerworld magazine.
Dates, faculty, programme and further information will be available soon.