Gomes, I., Martelo, M., Vilela, R. D., Jimenez, M., Trinidad, E. M., Gómez-Miragaya, J., González-Suárez, E., Martins, É., Torres, S. S., Corredeira, P., Miranda, J. L., [...]
Oliveira, P. d., Vala, I. S., Faísca, P., Guimaraes, J. C., Pina, F., Poli, E., Diegues, I., Osório, H., Matthiesen, R., Serre, K., & Constantino Rosa [...]
On May 15th and 16th, 2025, the iSTARS project hosted its second international meeting — the iSTARS Health AI Summit – at Lisbon Medical School, bringing together a diverse community of health professionals, researchers, students, data scientists, and patient advocates to explore how data and artificial intelligence are shaping the future of medicine and healthcare.
Riso, B., Santos, M. J. D. S., Ávila, P. Portuguese and the biobanks: Profiles and attitudes of the Portuguese population towards biobanks for health research in [...]
Thursday, 15th of May and Friday, 16th of May 2025 Grand Auditorium João Lobo Antunes, Egas Moniz Building, Lisbon Medical School (FMUL)
Riso, B. Os Biobancos na investigação biomédica: desafios para a saúde na sociedade contemporânea [Biobanks in biomedical research: challenges for health in contemporary society] Portugal: Lisbon [...]
PredictMet; April 2025 – January 2026
iSTARS research platform for health data science; April 2024 – September 2024
iSTARS team has been awarded funding under the FCT (Portuguese Foundation for Science and Technology) “Artificial Intelligence, Data Science and Cybersecurity of relevance to Public Administration” call to develop an advanced clinical decision-making support tool to predict disease progression in oligometastatic breast cancer patients.
Lauwereyns, J., Bajramovic, J., Bert, B., Camenzind, S., De Kock, J., Elezović, A., Erden, S., Gonzalez-Uarquin, F., Ulman ,Y.I., Hoffmann, O.I., Kitsara, M., Kostomitsopoulos, N., Neuhaus, [...]
Tel. +351 217 999 422
Address. Avenida Professor Egas Moniz, 1649-028 Lisboa — Portugal
Email. istars@medicina.ulisboa.pt
The project iSTARS received funding from the European Union’s Horizon 2020 Research and Innovation programme under grant agreement No 952377
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