How can we design more efficient, equitable, and sustainable healthcare systems in a rapidly changing world?

This challenge-based course invites students to tackle real-world problems at the intersection of health economics, policy, and innovation. Working in international and interdisciplinary teams, participants will explore how resources are allocated in healthcare, how technologies are evaluated, and how policies can improve access, quality, and sustainability of care.

Through a hybrid learning format, students will engage with asynchronous lectures, interactive and mentoring sessions with academic experts and external stakeholders. The challenge involves collaboration with healthcare organizations and policymakers.

Participants will develop key skills in critical and innovative thinking, teamwork, and problem-solving, while gaining foundational hands-on experience in analyzing complex health systems and proposing actionable solutions. Depending on the focus, topics may include digital health, health technology assessment, resource allocation, or inequalities in access to care. The course will conclude in a final pitch, where teams present their solutions to a panel of experts and stakeholders.

This challenge is ideal for students interested in health, economics, public policy, and innovation, who want to make a tangible impact and work collaboratively in an international environment.

Member universities may require additional information from applicants. Please read the application information section carefully and include any extra required information in this section.

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University of Trento

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Health Economics in Action: From Idea to Impact

Health Economic Challenge

Medical physicists have a central role in modern healthcare systems, ensuring the reliability and quality of technologies, as well as the proper use of radiation to guarantee patient safety. Quality assurance of imaging devices, radiation safety, optimization of clinical protocols, and validation of medical technologies are traditionally their major responsibilities.

Recently, the rapid development of artificial intelligence (AI) and its progressive integration in healthcare has gained considerable attention due to its seemingly remarkable performance in supporting radiological workflows and medical diagnosis. Applications of interest in medical imaging include automated organ segmentation, disease classification, clinical outcome prediction, and synthetic image generation. Commercial AI solutions have been developed to improve efficiency, reproducibility, and diagnostic support.

Despite the growing interest, several studies have highlighted suboptimal performance in real-world healthcare and performance degradation over time.

This critical aspect emerges from a persistent mismatch between development and deployment conditions: commercial AI systems are often trained on polished datasets, whereas real-world clinical data are highly fragmented and heterogeneous. As a result, the current lack of standardized procedures prevents quantitative validation of the performance, robustness, and clinical reliability of these systems before deployment.

A key question remains unanswered: how can we objectively evaluate the performance of AI systems in modern healthcare?

Within this challenge, students will work in interdisciplinary teams to suggest a strategy for evaluating AI solutions through rigorous and feasible procedures ahead of clinical practice.

Associate partner

ASUIT - Azienda sanitaria universitaria integrata del Trentino

Associate partner

ASUIT - Azienda sanitaria universitaria integrata del Trentino