The student should be able to apply the knowledge and skills acquired during the course to understanding AI applications to medical activities, with a specific focus on neuroscience.
Course Prerequisites
The course requires the student to have basic and introductory notions that are preparatory to the topics covered in the teaching course.
Teaching Methods
The course is organized in frontal lectures, carried out through PowerPoint presentations, compemented with video sessions and interactive sessions, which will help students to see practical examples of human diseases. This activity will also include integration with case studies and simulation approaches. In addition, the training activities will be integrated by in-depth seminars.
Assessment Methods
The final evaluation is based on the degree of depth and understanding of the topics presented and on the ability to integrate the knowledge acquired during the course. A final project will be developed and will culminate in a written report and in-class group presentation. Students will work in small groups to complete the project. A general goal of such project is to demonstrate ability in analyzing a clinical dataset provided by the instructors, critically discussing the findings. Each group should apply methods learnt, to address a research question, interpreting and evaluating data, including a discussion of relevant literature.
Texts
Kandel, Schwarz, Principles of neuroscience Lesson content;
Contents
interface cerebral activity-machines for the study of neuronal excitability, as well as for sleep medicine; Technology-based objective measures, such as wearable sensors, voice analysis, remote-communication wearable-sensor-data analysis. Medical diangostic tools to implement diangosis and optimize treaments
