"Health and Life Science" Area
Introduction
The National Program of PhD in Artificial Intelligence (AI) is divided into five doctoral courses held at 5 different universities federated with each other and with the National Research Council for the coordination of this program at a national level, which involves a total of 5 universities and research institutions. The five PhD programs have a common foundation addressing the fundamentals and development of AI and each has an area of specialization in a strategic sector of AI development and application.
The Campus Bio-Medico University is the leader for the AI sector for Health and Life Sciences, which consists of the participating and associated entities listed below.
In this field, the application of AI and, in particular, the integration of AI, IoT & Biorobotics prefigures scenarios of rapid evolution towards precision medicine, an increasingly predictive, personalized, participatory and preventive medicine. The specific training path of this vertical component involves the conception, study, design, development and application of innovative methods, tools and systems that can be used for both biological and bio-research basic engineering, necessary for understanding the origin of pathologies and the preliminary verification of innovative solutions on computational models, organ-on-chip, both for experimental and clinical medical research, to maximize the impact of this research on human health, well-being, safety and quality of life, including in longevity. A path that starts from the generation of significant data on the state of health and relevant environmental conditions, passes to this data's processing with AI and data science techniques for knowledge extraction and decision support and then arrives at the synthesis, implementation and monitoring of the tools and actions necessary for diagnostic, therapeutic and assistance purposes to improve the health and safety of the person in health, social and work contexts, using digital tools and cyber-physical systems.
Given the particular nature of contexts involving health, the doctorate will also address the problems related to the acceptability of AI technical solutions by healthcare professionals and patients and their effective inclusion in healthcare processes, including to avoid excessive workloads and improve defensive medicine approaches, in support of the quality, sustainability and accessibility of health services.
Participating bodies - the list of bodies is shown, in order by the number of grants made available in the announcement and in alphabetical order
| UCBM |  |
| University of Catania |  |
| University of Pavia |  |
| SISSA International School of Advanced Studies |  |
| Teleconsys SpA |  |
| University of Bari |  |
| Università del Piemonte Orientale |  |
| University of Campania "L. Vanvitelli" |  |
| Turin University |  |
| BPCOMedia Srl |  |
| CNR |  |
| ENAV SpA | |
| Eustema SpA |  |
| Human Technopole |  |
| National Institute of Nuclear Physics (INFN) |  |
| Luiss Guido Carli |  |
| Chieti-Pescara "G. D'Annunzio" University |  |
| University of the Study of Molise |  |
| University of Rome "Tor Vergata" |  |
| University of Messina |  |
Objectives of the Course
The objective of the three-year Italian national PhD program in Artificial Intelligence (AI) for Health and Life Sciences is to foster the postgraduate training of researchers, innovators and professionals with specializations in cutting-edge artificial intelligence methodologies and in application sectors with high social impact. The PhD program ensures an integrated and "complex" vision of the ecosystem of AI technologies and solutions, capable of addressing challenges related to health and life sciences with a systemic and multidisciplinary approach.
The “Health and Life Sciences” specialization area focuses on the study of scientific questions relating to health and life sciences through artificial intelligence and data science methods, powered by big data analytics skills and enhanced with inter-disciplinary hybridization with biological, biomedical and molecular sciences, as well as with advanced imaging and, more generally, signal acquisition and processing. The combination of model-driven and data-driven data mining approachesing, of machine learning and deep learninging the ability to observe, measure, model and predict complex events is progressively increasing, making available tools for assisted diagnosis, decision support and predictions of the clinical course of a pathology. This scientific line is intertwined with IoT and biorobotics, prefiguring scenarios of rapid evolution towards precision medicine, an increasingly predictive, personalized, participatory and preventive medicine.
The specific training path of this area of specialization involves the conception, study, planning, development and application of innovative methods, tools and systems that can be used both for basic biological and bio-engineering research, necessary for understanding of the origin of pathologies and the preliminary verification of innovative solutions on computational, organ-on-chip models, both for experimental, translational and clinical medical research, in order to maximize the impact of this research on health, well-being, safety and quality of human life, even in longevity. A path that starts from the generation of significant data on the state of health and relevant environmental conditions, passes to their processing with AI & data science techniques for knowledge extraction and decision support and arrives at the synthesis, implementation and the monitoring of the tools and actions necessary for diagnostic, therapeutic and assistance purposes to improve the health and safety of the person in health, social and work contexts, using digital tools and cyber-physical systems.
Given the particular nature of contexts involving health, the doctorate will also address the problems related to the acceptability of AI technical solutions by healthcare professionals and patients and their effective inclusion in healthcare processes, also to avoid excessive workloads and defensive medicine approaches, in support of the quality, sustainability and accessibility of health services.
Training activity (XXXIX cycle)
Students must attend at least 140 hours of courses (over three years; it is suggested in the initial phase of the course is done within the first two years).
In particular, every student enrolled in the PhD course on AI for Health and Life Science is required to:
- attend the doctoral school organized by the National AI Doctorate which addresses specific issues for the health and life sciences sector. The school's commitment is 30 hours.
- attend and produce documentation of passing the final test of specific courses for a total of at least a further 110 hours of lessons. These courses must be chosen from those made available by the doctoral program of the kealth and life sciences sector, or by the other four national doctoral programs.
Training activities organized for doctoral students by other bodies (e.g. summer school) and by other National AI Doctorates contribute to the 110 hours mentioned above, subject to authorization which is obtained by sending a request to the Working Group for the Evaluation of the Training Activities", appointed by the Teaching Board. Attendance of courses provided as part of the bachelor's and master's degree programs is not permitted, unless authorized by the WGETA beacuse it is motivated by specific training needs.
In determining the activities related to the 110 hours, a maximum of 20 hours can be dedicated to activities on soft skills, research management, European and international research systems, entrepreneurship, intellectual property, etc. organized by the university or by the research bodies of the National Doctorate.
>> Specific training activities in the Health and Life Sciences sector
>> Specific training activities for the other sectors of the National Doctorate
Infrastructures for research and services available to doctoral students
The National Research Council (CNR) contributes to the scientific coordination and funding of the National PhD in Artificial Intelligence (PhD-AI.it), and participates in all five PhD-AI.it doctorates, and its multi-disciplinary vocation.
The European research infrastructure SoBigData.eu, a pillar of the ecosystem of Italian and international big data and AI research laboratories, is coordinated by the CNR, through the Institute of Information Science and Technology of the CNR Area of Pisa .
Eight further CNR Institutes, in various sectors engineering, biomedical sciences and cognitive sciences have joined the doctorate in the health and life sciences area, also with members of the teaching body.
Each university, through the departments directly involved, makes infrastructures available for carrying out specific research projects, such as computational facilities, imaging facilities, etc.
>> Members of the PhD Board
"Health and Life Science" Area
Introduction
The National Program of PhD in Artificial Intelligence (AI) is divided into five doctoral courses held at 5 different universities federated with each other and with the National Research Council for the coordination of this program at a national level, which involves a total of 5 universities and research institutions. The five PhD programs have a common foundation addressing the fundamentals and development of AI and each has an area of specialization in a strategic sector of AI development and application.
The Campus Bio-Medico University is the leader for the AI sector for Health and Life Sciences, which consists of the participating and associated entities listed below.
In this field, the application of AI and, in particular, the integration of AI, IoT & Biorobotics prefigures scenarios of rapid evolution towards precision medicine, an increasingly predictive, personalized, participatory and preventive medicine. The specific training path of this vertical component involves the conception, study, design, development and application of innovative methods, tools and systems that can be used for both biological and bio-research basic engineering, necessary for understanding the origin of pathologies and the preliminary verification of innovative solutions on computational models, organ-on-chip, both for experimental and clinical medical research, to maximize the impact of this research on human health, well-being, safety and quality of life, including in longevity. A path that starts from the generation of significant data on the state of health and relevant environmental conditions, passes to this data's processing with AI and data science techniques for knowledge extraction and decision support and then arrives at the synthesis, implementation and monitoring of the tools and actions necessary for diagnostic, therapeutic and assistance purposes to improve the health and safety of the person in health, social and work contexts, using digital tools and cyber-physical systems.
Given the particular nature of contexts involving health, the doctorate will also address the problems related to the acceptability of AI technical solutions by healthcare professionals and patients and their effective inclusion in healthcare processes, including to avoid excessive workloads and improve defensive medicine approaches, in support of the quality, sustainability and accessibility of health services.
| BODY | typology |
| CNR | PARTICIPANT |
| Tor Vergata University | PARTICIPANT |
| University of Bari | PARTICIPANT |
| SISSA | PARTICIPANT |
| University of Piemonte Oriental woody eau de toilette. | PARTICIPANT |
| University of Catania | PARTICIPANT |
| University of Turin | PARTICIPANT |
| University of Pavia | PARTICIPANT |
| University of Messina | ASSOCIATED |
| Vanvitelli University | ASSOCIATED |
| University of Tuscia | ASSOCIATED |
| LUISS | ASSOCIATED |
| University of Molise | ASSOCIATED |
| University of Reggio Calabria | ASSOCIATED |
| University of Chieti | ASSOCIATED |
| INFN | ASSOCIATED |
| COT | ASSOCIATED |
| University of Genoa | ASSOCIATED |
| Sant'Anna of Pisa | ASSOCIATED |
| Sant'Anna of Pisa | ASSOCIATED |
| Politecnico di Milano | ASSOCIATED |
Objectives of the Course
The objective of the three-year Italian national PhD program in Artificial Intelligence (AI) for Health and Life Sciences is to foster the postgraduate training of researchers, innovators and professionals with specializations in cutting-edge artificial intelligence methodologies and in application sectors with high social impact. The PhD program ensures an integrated and "complex" vision of the ecosystem of AI technologies and solutions, capable of addressing challenges related to health and life sciences with a systemic and multidisciplinary approach.
The “Health and Life Sciences” specialization area focuses on the study of scientific questions relating to health and life sciences through artificial intelligence and data science methods, powered by big data analytics skills and enhanced with inter-disciplinary hybridization with biological, biomedical and molecular sciences , as well as with advanced imaging and, more generally, signal acquisition and processing. The combination of model-driven and data-driven data mining approaches, of machine learning and deep learning the ability to observe, measure, model and predict complex events is progressively increasing, making available tools for assisted diagnosis, decision support and predictions of the clinical course of a pathology. This scientific line is intertwined with IoT and biorobotics, prefiguring scenarios of rapid evolution towards precision medicine, an increasingly predictive, personalized, participatory and preventive medicine.
Training activity - XXXVIII cycle
Students must attend at least 140 hours of courses (over three years; preferably in the initial phase of the course within the first two years).
In particular, every student enrolled in the PhD course on AI for Health and Life Science is required to:
- attend two doctoral schools organized by the AI National Doctoral Program (www.phd-ai.it). In particular, a school is aimed at all students enrolled in the 5 courses of the Program itself, on topics transversal to the various application sectors; a second school deals with specific topics for the health and life sciences sector. The commitment of each school is equal to 20 hours.
- attend and produce documentation of passing the final exam of the specific courses for a total of at least a further 100 hours of lessons. These courses must be chosen among those made available by the doctoral program of the health and life sciences sector, or by the other 4 national doctoral programs;
Training activities organized for doctoral students by other bodies (eg summer schools) contribute to the 100 hours mentioned above, with prior authorization from the Academic Board formalized by the student's supervisor.
In determining the activities related to the 100 hours, a maximum of 20 hours can be dedicated to activities on soft skills, research management, European and international research systems, entrepreneurship, intellectual property, etc. organized by the university or by the research bodies of the National Doctorate.
>> Specific training activities in the Health and Life Sciences sector
>> Specific training activities of the other sectors of the National Doctorate
Infrastructures for research and services available to doctoral students
The National Research Council (CNR) contributes to the scientific coordination and funding of the National PhD in Artificial Intelligence (PhD-AI.it), and participates in all five PhD-AI.it doctorates, and its multi-disciplinary vocation.
The European research infrastructure SoBigData.eu, a pillar of the ecosystem of Italian and international big data and AI research laboratories, is coordinated by the CNR, through the Institute of Information Science and Technology of the CNR Area of Pisa.
Eight further CNR Institutes, in various sectors engineering, biomedical sciences and cognitive sciences have joined the doctorate in the health and life sciences area, also with members of the teaching body.
Each university, through the departments directly involved, makes infrastructures available for carrying out specific research projects, such as computational facilities, imaging facilities, etc.
>> Members of the PhD Board
