Learning objectives

Human Pathology
The course is aimed at teaching knowledge and skills regarding human pathology with a systematic approach divided by pathology topics (inflammatory, degenerative and neoplastic pathology) and divided by districts and systems (pathology of the gastrointestinal tract, urogenital, pathology of the endocrine system, central nervous system, soft tissue and skin, head and neck area, respiratory system, breast pathology, and haemolymphatic system pathology). The integration of the study of pathologies with the knowledge of immunohistochemical and molecular methods with diagnostic, prognostic and predictive value and of bioinformatics concepts applied to the genetics of tumors is envisaged. The training course also includes the teaching of modern digital technologies that characterize "Digital Pathology" with concepts of image analysis and application of artificial intelligence systems applied to histopathology. Furthermore, in-depth analysis of laboratory management software (LIS) is provided with particular attention to the concepts of traceability and sharing.
Machine Learning Module
The module aims to provide the fundamentals of machine learning, particularly the prediction techniques most used in the diagnostic field. The concepts of clustering, regression, and supervised classification will be introduced, showing, also with laboratory activities, the leading classical techniques and those based on neural networks.

Examination Methods

The exam consists of an oral test aimed at verifying knowledge of the course contents. Machine Learning Module Verification of learning will be carried out through: -written test with questions and exercises, -evaluation of laboratory activities.

Evaluation criteria

The oral test assesses the level of knowledge of the theoretical contents, the ability to use language in describing pathologies and their specific diagnostic, prognostic and predictive factors. The exam also tests the student's logical/deductive skills and his knowledge of the application of new "Digital Pathology" technologies supported by artificial intelligence systems in the context of specific laboratory management software that guarantee traceability and sharing concepts. Machine Learning Module At the end of the course the student must be able to: -understand the fundamentals of the main machine learning algorithms used in the medical field, -evaluate the performance of different AI-based methodologies and choose the most appropriate approach for a specific problem, -analyze the design choices of an application that uses AI. The final grade is given by the positive evaluation for a score equal to or greater than 18/30 in each module.