Graduates in Artificial Intelligence for Biomedicine and Healthcare will work closely with a team of doctors, research scientists, or public health managers to design, develop, and apply techniques based on artificial intelligence to exploit and interpret data for clinical prediction and research. They will be ideal team leaders, able to coordinate work between several professional figures with diversified skills, as they will be competent on both the analytical and the biomedical area.

The skills associated with their professional profile are:

➔ the ability to identify, customize and critically evaluate the analytical tools already available for their application in the hospital, academic, and industrial biomedical and public health fields,

➔ the ability to develop customized tools for medical, scientific, health, and management issues,

➔ the ability to use, interpret, and contextualize information from large amount of data of biomedical, scientific, health, and management relevance,

➔ the ability to integrate different knowledge and skills, with leadership, independent learning and critical judgement, communication, and interpersonal skills.

Career opportunities:

• Graduates will be able to work as digital innovators in biomedicine in private or public research laboratories, within hospitals, clinical research laboratories, or public health companies.
• They may also exploit they managerial skills within organizations in the public or private health sector for the administration of:
• therapeutic and diagnostic procedures
• health and epidemiological data that use omics data or big data
• technological innovation processes centered on data in the biomedical field.

Finally, they will be able to undertake an entrepreneurial career, for example in the development of informatic tools for diagnosis and prognosis or for the management of large-scale health data.

The LM-91 Artificial Intelligence for Biomedicine and Healthcare program will train a professional figure who possesses:

• a significant knowledge of the basic aspects of biomedicine, from the biological aspects (biochemical, molecular, cellular, histological and genetics), up to the clinical aspects (internal medicine, radiology, surgery),
• a solid knowledge and ability to apply the basic techniques underlying artificial intelligence, such as mathematical and computational methods, machine learning, deep learning, data management and representation, and mathematical modeling,
• a broad and advanced knowledge of artificial intelligence tools, techniques, and data science related to biomedicine, especially focusing on:
  • robotic surgery,
  • biological and diagnostic imaging,
  • pharmaco-omics,
  • epidemiology,
• expertise on ethical, legal, managerial, and organizational aspects associated with the production, processing, and interpretation of biomedical data,
• capability of integrating different knowledge and skills in the multidisciplinary biomedical and informatic fields.

The different areas of the training course are:

1) BIOMEDICAL AREA

In this area the courses will strengthen basic knowledge of biochemistry, cellular and molecular biology, tissue pathophysiology, genetics, internal medicine, and oncology.

2) COMPUTER SCIENCE AREA

In this area the courses aim at deepening multiple modeling, computational, and decision-making tools in the biomedical field, based on data science techniques and artificial intelligence. Specifically, fundamental approaches to the modeling of complex biological systems, machine learning, deep learning, neural networks, and computer vision for improving performance in diagnostics and prognostics will be treated.

3) MANAGERIAL AND JURIDICAL AREA

This area will address issues related to ethical, legal, managerial, and organizational aspects associated with the production, storage, processing, interpretation, and management of biomedical data through artificial intelligence-based techniques.

4) MULTIDISCIPLINARY AREA

This area collects students' knowledge and skills to focus on the most relevant aspects involving biomedicine and artificial intelligence, such as robotic surgery, biological and diagnostic imaging, omics and omics data processing, and finally epidemiology and public health through modeling approaches.

The focus of the lessons of the first year will be on biomedical and computer science areas, while the interdisciplinary and laboratory activities, managerial and legal areas, together with internship-thesis projects, will take place during the second year.

The Dublin descriptors for LM-91 Artificial Intelligence for Biomedicine and Helthcare, outcomes fulfilled by all the graduates, can be described according to two main principles: knowledge and understandings, and ability to apply knowledge and understandings.

Knowledge and understanding:

The master's graduate will have acquired knowledge and understanding of notions and languages typical of the biomedical, computer science, statistical, ethical, legal, managerial, and organizational fields, and in particular:

• the biochemical and molecular bases of biological macromolecules, the main metabolic processes, and organ-specific aspects of human biochemistry and molecular biology in the context of health sciences,
• the structure and functioning of the cell, its integration into a tissue, and their physiological and pathological aspects,
• the fundamental genetic mechanisms underlying the functioning of the human body and the onset of mono and multifactorial diseases,
• basic medicine (anatomy, pathological anatomy, physiology and pathophysiology, medical genetics), clinical medicine (internal medicine, medical oncology) and surgery (general and specialist surgery such as urology),
• the main mathematical and computational modeling approaches applicable in biological and clinical fields,
• the main methods of acquisition and use of images in the biological and medical fields, and of the main traditional analysis techniques, based on machine learning or deep learning,
• the biomedical and modeling aspects necessary for the description of population dynamics in an epidemiological or genetic view,
• modern omics technologies and related data analysis procedures,
• recent technological developments in medicine and surgery with reference to approaches that take advantage of data-driven or artificial intelligence-based algorithms,
• tools and techniques of artificial intelligence and information systems of strategic importance in biomedicine and healthcare,
• the ethical, legal, managerial, and organizational aspects associated with the production, storage, processing, interpretation, and management of biomedical data.

Ability to apply knowledge and understanding

The master's graduate will be able to apply the acquired knowledge and understanding to different areas:

• the choice of analytical methods appropriates to the nature and structure of biomedical data from different experimental techniques or from epidemiological or clinical sources, contextualized on different spatial scales, up to omics data,
• the analysis, interpretation and use of different types of biomedical data, as well as their integration in scientific, clinical, and industrial research and development processes,
• the choice and application of modeling approaches that make possible to integrate in disease prediction or to identify key components for the definition of personalized therapies,
• the application of artificial intelligence techniques, machine learning, and more generally computer science technologies (Information and Communication Technologies, ICT), to the discovery, development, and use of methods, processes, tools, and products of relevance in the biomedical field,
• the application of ethical, legal, and managerial principles, in the management of the various stages of multidisciplinary scientific and productive processes in the integrated fields of biomedicine and information technology.

The acquisition of these skills will be favored and stimulated throughout the Master Course through laboratory and practical activities, critical reflection on proposed topics, individual or group research and deepening study, and internships. These didactic activities will be designed in such a way as to give the student the opportunity to apply, through individual and group work, the acquired concepts and knowledge to unknown situations.