AI in Oncology

Link al trailer del corso: https://www.edvance.it/
Target: studentesse e studenti di CdS magistrali
Lingua: eng
Il corso appartiene ad una serie?: No
Breve Descrizione:

The MOOC “AI in Oncology: from Precision Medicine to Future Clinical Trial Design” explores how artificial intelligence is reshaping modern oncology. Starting from the foundations of precision medicine, the course guides participants through the main areas where AI is making an impact: from translational research to biomarker discovery, from innovative clinical trial design to the use of large language models in clinical practice. Bridging clinical and computational perspectives, this course provides clinicians and researchers with the conceptual and practical tools to critically understand, assess, and apply AI technologies in contemporary oncology.
Informazioni Base:

The course is organized into 5 modules that guide learners through a progressive journey across the main applications of artificial intelligence in oncology: from the foundations of precision medicine to the design of future clinical trials.

  1. Module 1 – Precision oncology: definitions. Introduction to the core principles of precision medicine and the role of data in personalizing cancer care.

  2. Module 2 – AI for translational research in oncology. How artificial intelligence supports translational research, from multi-omics data analysis to novel therapeutic target discovery.

  3. Module 3 – Exploring AI-based biomarkers. An in-depth look at AI-derived biomarkers: radiomics, pathomics, genomics and their intersections.

  4. Module 4 – How AI can shape clinical trial design. The new frontiers of clinical research: patient matching, virtual trials, and predictive modeling to optimize trial outcomes.

  5. Module 5 – Large language models in oncology. The emerging role of large language models, from decision support in Molecular Tumor Boards to ethical and regulatory perspectives.

Each module includes video lectures, written materials and self-assessment quizzes. 
Risultati Attesi:

Intended Learning Outcomes

By the end of the course, students will be able to:

  1. Remember and understand

  • Define the fundamental concepts of precision medicine and artificial intelligence in the context of oncology.

  • Describe the main types of biomedical data used in AI-driven oncology (clinical, imaging, omics, and textual data).

  • Explain how AI models differ from traditional statistical approaches in data analysis and prediction.

Apply

  • Apply AI-based reasoning to interpret examples of translational oncology research, such as multi-omics integration and biomarker discovery.

  • Illustrate the workflow of an AI-assisted decision-support system in oncology, from data input to clinical output.

Analyze

  • Distinguish between different categories of AI biomarkers (radiomics, pathomics, genomics, digital phenotyping) and assess their potential clinical use.

  • Examine the structure and design of AI-enhanced clinical trials, identifying the role of algorithms in patient matching and outcome prediction.

Evaluate

  • Critically appraise the advantages, limitations, and ethical challenges of AI applications in oncology.

  • Assess the robustness and generalizability of published AI models, with attention to validation strategies and bias control.

Create

  • Propose integrative strategies for combining clinical, imaging, and molecular data through AI methods to support precision oncology.

  • Design an outline for a research or clinical project that leverages AI to improve patient stratification or therapeutic development.
Strategia di valutazione:

At the end of each module the student will be able to try a self-evaluation test, and to try the proposed additional exercises with solutions.
Attività Base:

The modules are designed to provide students with an overview of the state of the art in the use of artificial intelligence in oncology. Particular attention is given to the role of AI in translational research and clinical trials. The lessons will be delivered in video format, and all teaching materials will be made available. Simple exercises will be offered throughout the course to help students consolidate the main concepts explained.
Prerequisiti:

Basic knowledge on cancer biology, clinical trials design and AI. 
Livello EQF: EQF Level 5
ISCED-F: 0619 Information and Communication Technologies (ICTs) not elsewhere classified
Categoria: Transdisciplinarità
SDGs: GOOD HEALTH AND WELLBEING
Docenti:

Alessandra Merlini, Department of Oncology
Carico Lavoro Totale (in ore/settimana): 6
Numero settimane del corso: 1
Contatti:

Alessandra Merlini

alessandra.merlini@unito.it

For technical issues:

edvancedeh@unito.it