B5224 - Artificial Intelligence for Medicine M

Learning outcomes

At the end of the course, the student has: -knowledge regarding the need for AI in Medicine; -understanding of weaknesses of AI techniques, methods to overcome the challenges of AI in Medicine, and ways in which AI may improve healthcare; -essential knowledge to develop and implement reliable AI solutions in clinical research (using, e.g., supervised and unsupervised machine and deep learning techniques, explainable AI methods, and generative adversarial techniques); -practical skills to develop state-of-the-art AI tools with real-world medical data (e.g., clinical and imaging data); -the ability to critically read, discuss and evaluate methods and results of studies using AI in Medicine; -an overview of state-of-the-art AI-powered tools in Medicine.

Course contents

· Introduction to AI in Health and Medicine: progress, challenges, and opportunities;

· Introduction to Clinical Data: medical data collection and management, ethics and informed consent, quality control, data harmonization, data augmentation, synthetic datasets, dataset size vs. confidence intervals;

· Fundamentals of AI for Medicine: weaknesses of AI techniques and their consequences in Medicine, evaluation of AI tools in Medicine, explainable AI, AI reproducibility, beyond supervised learning, federated and swarm learning, transparent reporting, the ethics of AI in healthcare and Medicine, clinical integration;

· Hands-on Projects with Clinical Data: projects to provide technical experience in AI for Medicine;

· Real-world AI-powered tools of AI in Medicine: an overview of state-of-the-art AI solutions in Medicine and Healthcare.

Readings/Bibliography

Notes provided by the Professor.

M. Lutz, "Learning Python", O'Reilly, 2013

I. Goodfellow, Y. Bengio, A. Courville, "Deep Learning", MIT Press, 2016 (https://www.deeplearningbook.org/ )

Teaching methods

The course comprehends both ex-cathedra lessons and practical exercises on the personal computer. The aim of the lessons is to provide the students with a theoretical knowledge about artificial intelligence for Medicine, and to make them aware about the advantages and limitations of each available technique. The practical exercises aim at training the students on the resolution of real biomedical problems, and at showing the potential benefits but also the shortcomings and difficulties introduced by artificial intelligence techniques and software packages.

Given the type of activity and teaching methods adopted, the attendance of this training activity requires the prior participation of all students in modules 1 and 2 of training on safety in the workplace [https://elearning-sicurezza.unibo.it/] in e-learning mode.

Assessment methods

The learning assessment will be performed through a final examination consisting of an oral test focused on both the theoretical concepts presented during the lessons and the software tools used in the laboratory. The exam ascertains the theoretical-practical skills and competences of the student, correctness of language, and clearness of concepts and exposition.

Teaching tools

Document camera, videoprojector.

Notes provided by the Professor.

Personal computer laboratory.

Software environment for performing practical exercises in the computer science laboratory.

Office hours

See the website of Stefano Diciotti