ESMO-Webinar-series-Applications-and-Promises-of-AI-in-Oncology-1000x250

Live on

23 Jan 2024 at 18:30 CET

Chairs’ welcome

It is with great pleasure that we invite you to participate in the first ESMO Real World Data Webinar on the topic of Applications and promises of Artificial Intelligence (AI) in oncology – what oncologists need to know.

AI methodologies have been applied to medical research for years and they have recently made an impactful entrance in oncology. AI broadly speaking consists of a set of techniques allowing computers to emulate human intelligence, employing algorithms created for the analyses and the design of either predictions or conclusions based on big datasets.

The latter is especially important for cancer research considering the critical mass of data available for analysis and that standard analysis methods failing to exploit it to its fullest potential. This is particularly the case for multiomics data, with their high variation in nature, format, or storage.

The main training objective of this webinar will be to ease the clinical and research community into the mindset of AI methodologies themselves– from a general overview of the most frequently used machine learning (ML) / deep learning (DL) methods and Explainable AI to a deep dive in novel data platforms and repository structures integrating these approaches and their design. This will allow clinicians to identify the value of AI models for their trials and studies, making the volume of patient- and tumour-related data valuable and more fully exploitable; as well as biologists to increase the playing field in tumour biology to discover new biomarkers and mechanisms.

The webinar will conclude with a live discussion of questions submitted by the audience whilst the webinar is taking place.

We are delighted to announce the distinguished speakers who will join us in this webinar, and we encourage and invite you to register and join us all in this new ESMO activity.

Dr. Arsela Prelaj
National Cancer Institute (INT), Milan, Italy
Politecnico di Milano University, Milan, Italy

And

Prof. Jakob Kather
Technical University (TU) Dresden, Dresden, Germany
University Hospital, Dresden, Germany

Speakers

Programme

Time	Title	Speaker
5 min	Welcome and Introduction	Arsela Prelaj, Jakob Kather
15 min	Diagnosis and Prediction - Introducing AI Concepts of Machine Learning in Radiomics	Raquel Perez Lopez
15 min	How AI is Transforming the Field of Digital Pathology for Diagnoses and Prognostication	Alexander Pearson
15 min	Bigger is Better? Multiomics and Multimodal Data Integration for Prognostication and Prediction	Mireia Crispin
10 min	LIVE Discussion and Q&A	All

Live viewers of the webinar will be awarded a certificate of attendance and 1 ESMO-MORA category 1 point

Learning objectives:

Gain a general understanding of AI methodologies applied in medical research, particularly in the field of oncology.
Explore the impact of AI in oncology, emphasising its ability to emulate human intelligence and analyse large datasets for predictions and conclusions.
Recognise the significance of applying AI to cancer research due to the vast amount of data available, which conventional analysis methods may not fully utilise.
Focus on the challenges posed by multiomics data, including variations in nature, format, and storage, and the need for specialised approaches to exploit its potential.
Acquire knowledge of frequently used machine learning (ML) and deep learning (DL) methods, as well as Explainable AI concepts.
Enable clinicians to identify the value of AI models in trials and studies, making patient- and tumour-related data more exploitable.
Empower biologists to broaden their scope in tumour biology, facilitating the discovery of new biomarkers and mechanisms through the integration of AI methodologies.

Speakers

Programme

Learning objectives:

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