Objective
AI-empowered Personalized Medicine promises to find tailored, targeted, nearly “hand-made” cures for patients. Cancer treatment desperately needs boosters to find tailored, targeted cures for patients and Personalized Medicine can play a crucial role. Tailored targeted therapies in cancer treatment are already a reality but the current practice of targeted therapies in cancer treatment has been derived with traditional methods of data analysis. AI-empowered Personalized Medicine may help to bring targeted therapies to the next level. However, no matter how precise it is, no matter how many lives it can save in principle, and no matter if it can utilize the entire medical knowledge. If clinicians do not understand its suggestions and decisions, AI-empowered Personalized Medicine will not be a game changer, clinicians will not use it to make everyday decisions and, thus, it is doomed to fail. Hence, the real challenge is building AI-empowered Personalized Medicine systems that can be accepted by clinicians and clinical researchers. In KATY, we grasp the above challenge and we propose an AI-empowered Personalized Medicine system that can bring medical “AI-empowered knowledge” to the tips of the fingers of clinicians and clinical researchers. The AI-empowered knowledge is a human interpretable knowledge that clinicians and clinical researchers can: understand, trust and effectively use in their everyday working routine. KATY is then a AI-empowered Personalized Medicine system built around two main components: A Distributed Knowledge Graph and A pool of eXplainable Artificial Intelligence predictors. As a stress test and due to the lack of personalized clinical responses, KATY will be experimented in a low prevalence and complex cancer: Clear cell renal cell carcinoma (ccRCC).
Field of science
- /natural sciences/computer and information sciences/data science/data analysis
- /natural sciences/computer and information sciences/artificial intelligence
- /medical and health sciences/clinical medicine/cancer
Programme(s)
Call for proposal
H2020-SC1-FA-DTS-2020-1
See other projects for this call
Funding Scheme
RIA - Research and Innovation actionCoordinator
00133 Roma
Italy
Participants (19)
75015 Paris 15
1749 016 Lisbon
EH8 9YL Edinburgh
00138 Roma
80309 Gdansk
KY16 9AJ St Andrews
1010 Wien
08290 Cerdanyola Del Valles (Barcelona)
66386 St Ingbert
08035 Barcelona
20133 Milan
81 451 Gdynia
50009 Zaragoza
37136 Verona
38700 La Tronche
22100 Lund
151 23 Marousi Attikis
10561 Athina
03056 Kyiv