challenge

Background
After undergoing surgery, Besnik is eager to recover quickly but struggles to follow the hospital’s generic dietary recommendations. Meanwhile, Liridona, who manages diabetes, often receives broad nutritional advice that doesn’t fully align with her lab results. At Olive Hospital, doctors and nutritionists rely on diagnostic tests to guide treatment, yet tailoring dietary plans to individual needs remains a challenge. Proper nutrition is vital for managing chronic diseases, supporting post-surgical healing, and preventing complications—but translating medical advice into personalized, actionable meal plans remains difficult.

Problem
Many patients lack clear, practical guidance on what to eat based on their medical conditions and test results. Doctors and nutritionists spend significant time manually creating dietary plans, often without the tools to continuously adapt them to a patient’s changing health status. A smarter, more dynamic approach to medical nutrition is needed—one that personalizes meal plans while remaining flexible and accessible.

The Challenge
How can we bridge the gap between medical data and personalized nutrition? Your task is to develop an innovative solution that helps patients make informed dietary choices based on their health needs. The solution should consider medical test results, evolving health conditions, and individual preferences while offering adaptable and practical guidance. How can technology, AI, or digital health tools make nutrition an integral part of personalized care.

Background
Arjan, a 58-year-old teacher, has no symptoms but worries about his risk of prostate cancer due to his father’s history with the disease. His doctor recommends screening, but Arjan is unsure if he really needs it. Meanwhile, Blerim, a 65-year-old retired mechanic, was diagnosed at a late stage, despite having regular check-ups. Advances in AI and data science offer the potential to change these stories—by identifying high-risk individuals earlier and ensuring timely interventions.

Problem
Despite medical advancements, many men still face late diagnoses due to limited screening accessibility, resource constraints, and gaps in existing risk prediction models. Some models lack accuracy across diverse populations, while others fail to integrate smoothly into clinical workflows, making them less effective in real-world settings. A more inclusive, data-driven solution could help bridge these gaps and improve early detection efforts.

The Challenge
How can AI-driven risk prediction improve prostate cancer detection and prevention? Your task is to develop a robust and inclusive machine-learning model that analyzes patient data—such as genetic profiles, lifestyle habits, and clinical records—to identify individuals at risk. The model should prioritize accuracy, fairness, and usability while ensuring patient privacy. Can your solution seamlessly integrate into clinical settings, helping doctors make better-informed decisions? Design a tool that brings early detection within reach for more people, improving health outcomes one step at a time.

30. Nov. 17

Challenge A

Challenge A

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Challenge B

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Challenge C

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Challenge D

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Challenge E

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Challenge F

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Challenge H