Italian Institute of Technology

AlforCOVID: Predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study.

Paolo Soda, Natascha Claudia D’Amico, Jacopo Tessadori et al.

2021 · 91 Zit.

Distinguishing Functional from Non-functional Pituitary Macroadenomas with a Machine Learning Analysis

Carlo Ricciardi, Renato Cuocolo, Giuseppe Cesarelli et al.

2019 · 43 Zit.

AbdomenAtlas: A large-scale, detailed-annotated, & multi-center dataset for efficient transfer learning and open algorithmic benchmarking

Wenxuan Li, Chongyu Qu, Xiaoxi Chen et al.

2024 · 34 Zit.

NephCNN: A deep-learning framework for vessel segmentation in nephrectomy laparoscopic videos

Alessandro Casella, Sara Moccia, Chiara Carlini et al.

2021 · 12 Zit.

Opening scientific knowledge to debunk myths and lies in human nutrition

Marco Capocasa, Davide Venier

2023 · 6 Zit.

How microrobots should be translated: A clinical and value‐centered readiness framework

Hakan Ceylan, Edoardo Sinibaldi, Sanjay Misra et al.

2026 · 1 Zit.

Artificial intelligence for liver diseases: The urgency of collaboration

Manuela Cesaretti, Alessandro Izzo, Orestes Mavrothalassitis et al.

2024 · 1 Zit.

Using<scp>AI</scp>to Steer Brain Regeneration: The Enhanced Regenerative Medicine Paradigm

Gabriella Panuccio, Narayan Puthanmadam Subramaniyam, Ángel Canal-Alonso et al.

2024 · 0 Zit.

Robotic Innovations in Support of the Healthcare Workers Against COVID-19 - DIH-HERO Perspective

Kosta Jovanović, Andrea Schwier, Eloise Matheson et al.

2022 · 0 Zit.

A User-Centred Framework for Explainable Artificial Intelligence in Human-Robot Interaction

Marco Matarese, Francesco Rea, Alessandra Sciutti

2021 · 0 Zit.

Correction to: On the reproducibility of expert-operated and robotic ultrasound acquisitions

Risto Kojcev, Ashkan Khakzar, Bernhard Fuerst et al.

2019 · 0 Zit.