Contributo in atti di convegno, 2023, ENG

AI trustworthiness in prostate cancer imaging: a look at algorithmic and system transparency

Colantonio S.; Berti A.; Buongiorno R.; Del Corso G.; Pachetti E.; Pascali M.A.; Kalantzopoulos C.; Kalokyri V.; Kondylakis H.; Tachos N.; Fotiadis D.; Giannini V.; Mazzetti S.; Regge D.; Papanikolaou N.; Marias K.; Tsiknakis M.

CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; Foundation for Research and Technology Hellas, Ioannina, Greece; Foundation for Research and Technology Hellas, Institute of Computer Science, Heraklion, Greece; Foundation for Research and Technology Hellas, Institute of Computer Science, Heraklion, Greece; Foundation for Research and Technology Hellas, Ioannina, Greece; Foundation for Research and Technology Hellas and University of Ioannina, Ioannina, Greece; Department of Surgical Sciences, University of Turin, Turin and Department of Radiology, Candiolo Cancer Institute, Candiolo, Italy; Department of Surgical Sciences, University of Turin, Turin and Department of Radiology, Candiolo Cancer Institute, Candiolo, Italy; Department of Surgical Sciences, University of Turin, Turin and Department of Radiology, Candiolo Cancer Institute, Candiolo, Italy; Champalimaud Foundation, Computational Clinical Imaging Group, Lisboa, Portugal; Foundation for Research and Technology Hellas, Institute of Computer Science, Heraklion, Greece; Foundation for Research and Technology Hellas, Institute of Computer Science, Heraklion, Greece

A responsible approach to artificial intelligence and machine learning technologies, grounded in sound scientific foundations, technical robustness, rigorous testing and validation, risk-based continuous monitoring and alignment with human values is imperative to guarantee their favourable impact and prevent any adverse effects they may have on individuals and communities. An essential aspect of responsible development is transparency, which constitutes a fundamental principle of the European approach towards artificial intelligence. Transparency can be achieved at different levels, such as data origin and use, system development, operation and usage. In this paper, we present the techniques implemented and delivered in the EU H2020 ProCAncer-I project to meet the transparency requirements at the different levels required.

IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, Malta, 7-9/12/2023

Keywords

Trustworthy AI, Traceability, Medical Imaging, Prostate Cancer

CNR authors

Buongiorno Rossana, Pachetti Eva, Berti Andrea, Del Corso Giulio, Colantonio Sara, Pascali Maria Antonietta

CNR institutes

ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"