IA4AI in Health Interdisciplinary Approaches for Artificial Intelligence in Health

I EDITION | APPLICATION | ON SITE

Deadline for Registration: February 28th, 2026

Period: April 13th – 17th, 2026

Learning objectives 

Artificial Intelligence (AI) has moved beyond the experimental phase and, thanks to its abilities to analyze and understand data, to plan, and to facilitate communication among professionals from different backgrounds, is establishing itself as a support tool in diagnostics, risk assessment and therapeutic decisions, both for individual and population health. However, AI is not yet synonymous with absolute reliability: available systems have limitations and must always be supervised and managed critically by operators with skills not only scientific but also technical, regulatory and organizational.

The integration of AI into a complex system such as healthcare therefore requires knowledge of the technological tools available, of the management of procedural flows in clinical, administrative and economic areas, as well as of existing regulations and potential ethical repercussions. Only in this way can AI’s speculative power be guided by clinicians’ judgment, with the goal of improving services, reducing time and costs, and ensuring greater efficiency.

For this reason, the introduction of Artificial Intelligence in healthcare requires an interdisciplinary approach, capable of integrating different viewpoints and steering these technologies toward safe, effective and sustainable use.

The Seasonal School proposes to examine the essential aspects for introducing AI into healthcare from five different perspectives: technological aspects will be examined, as well as systems currently in use, and the workflows and procedures for process traceability; engineering aspects of data, models and algorithms for designing AI applications that can be effectively and efficiently integrated into analyses of texts, images and biological signals will be reviewed; methods for representing structured knowledge bases built from procedural information and clinical databases will also be presented, with the aim of providing decision‑making support in healthcare; from an administrative perspective, the current and emerging legislative landscape will be discussed, and the requirements, implementation aspects and legal implications that the use of assisted diagnostics must meet for a correct and ethically sound use of these systems will be examined; finally, from a managerial point of view, the management aspects for the sustainability of these systems will be introduced, including elements of integration with existing administrative procedures to potentially make the management of screening campaigns, waiting lists, or interventional/surgical procedure rooms more effective, while minimizing operational costs. Finally, fundamentals for applying AI as support for research and teaching will be provided.

Teaching methodologies

The course offers a cross-cutting in-depth exploration of new AI technologies in the medical/clinical/healthcare field through a multidisciplinary approach that examines frontier aspects between technical development, customization to practice contexts, reasoned and reasonable use, and technical, technological, ethical, economic and regulatory limits. In particular, the course aims to stimulate and develop in students through theoretical lectures and immersive sessions:

• Knowledge: algorithms, methods for knowledge structuring, AI tools and libraries, clinical applications, regulatory context.
• Skills: critical analysis, multidisciplinary integration, management of AI projects.
• Transversal skills:
  • Technological (application areas, algorithms used)
  • Public health (sustainability, social impact)
  • Managerial (project & innovation management)
  • Ethical (responsibility and constraints in AI)
  • Legal (privacy, medical liability)
  • Research (scientific writing and data analysis)
  • Educational (ability to transfer knowledge).

Morning we schedule theoretical lessons, in the afternoon alternate teaching. The following teaching methodologies will be adopted during the classrooms: 

1. Frontal Lessons
2. Case Study Analysis
3. Interactive Session
4. Practical Exercises
5. Witnesses
6. Immersive Lab tour

Target participants 

The course is aimed at students in STEM, Economics, or Management with applications in the healthcare environment. PhD candidates from Italian or foreign universities enrolled in a PhD program may apply. A maximum of 20 students will be admitted to the course, selected, in order of importance, by date of application submission, merit based on scientific results achieved, relevance to the topics, gender balance, and younger age. If additional places become available, the following may also apply:

• PhD graduates who obtained their degree no more than 12 months prior to the application date
• Graduates (Master’s, single-cycle Master’s, Specialist degree or Second Level Master) who obtained their qualification no more than 6 months prior to the application date
• Students enrolled in the final year of a Master’s degree, single-cycle degree, or Second Level Master, with a grade point average of at least 27/30 (Italian system), grade “B” in the Anglo-Saxon system, or equivalent (at the sole discretion of the Evaluation Committee)

Up to 5 School trainees who meet at least one of the above requirements may be admitted to the course as additional participants, without any financial support or benefits.

Coordinator and key teaching staff

Four professors will coordinate the different aspects of technology development and management, medical and legal issues.

• Carlo Alberto Avizzano
• Paolo Ferragina
• Alberto Giannoni
• Milena Vaineri

The modules’ teaching will be also participated by Sant'Anna professors, external professors/professionals, among these:

• Michele Emdin
• Claudio Passino
• Maurizio Mangione
• Alberto Aimo
• Angelo Capodici
• Luca Valcarenghi
• Piero Castoldi
• Mariella Gagliardi
• Andrea Bertolini
• Alberto Pirni
• Silvestro Micera
• Calogero Oddo
• Eleonora Russo
• Mariangela Filosa
• Sabina De Rosis
• Stefano Dalmiani
• Alice Borghini
• Francesco Schiavone
• Andrea Vandelli
• Manuela Furlan
• Jasna Pocek
• Andrea Alessandrelli