Aim of this course is to provide the theoretical basis aimed at fostering an interdisciplinary and integrative interaction between clinicians, AI algorithm designers, medical-application-specific “chip scientists”. The interaction between AI and cognitive neuroscience/neuropsychology will be discussed, with a focus on how information derived from neurophysiological data can guide AI to manage different final effectors through brain computer interfaces (BCI). Furthermore, the use of AI to advance the knowledge on brain functioning with the application of machine learning on brain signals will be discussed.
Course Prerequisites
The course requires the student to have notions that are preparatory to the topics covered in the teaching course.
Teaching Methods
The course is organized in frontal lectures, through PowerPoint presentations, complemented with video sessions and interactive sessions, with practical examples of human diseases. This will be integrated with case studies and simulation approaches. In addition, the training activities may be integrated by in-depth seminars.
Assessment Methods
Oral examination. The final evaluation is based on the level of understanding of the topics presented
Texts
Lesson content; Kandel, Schwarz, Principles of neuroscience;
Contents
The changing landscape of medical education, with specific focus on Neurology; AI-assisted Detection & Diagnosis- Neuroimaging; AI-assisted Neurorehabilitation Disease Management - Health Analytics Neurology and Aging AI-assisted neurotherapeutics- drug discovery in neurological disorders Brain computer interfaces (non invasive, semi-invasive, invasive, closed loop, open loop); Adaptive Interfaces Using Neural Measures to Predict Real-World Outcomes; Social/Affective neuroscience for human-machine interaction; Usability of BCIs in cognitive neuroscience; BCI in cognitive and neurological rehabilitation
