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.
Project Goals
The development of knowledge and competences on integration of AI in Cancer Care Continuum: from diagnosis to clinical decision-making. 
The project was 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 carried out a multi-step project consisting of: 1) a series of Educational Webinars and seven Article Reports – one after each webinar - has been produced and published in the Cancerworld magazine; 2) a Virtual Symposium on AI in cancer care.
All the sessions (free of charge and CME accredited) was accessible to the audience by registering on SPCC's OncoCorner e-learning platform. Materials are available on-demand on OncoCorner.
Sharing Progress in Cancer Care wishes to express its appreciation to AstraZeneca, Novartis and Roche for their indipendent support.
24.10.2022 17:30 CEST
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Filipovic Aleksandra, Kalogeropoulos Dimitris