Papers

2025

• From oracular to judicial: enhancing clinical decision making through contrasting explanations and a novel interaction protocol
  F. Cabitza, L. Famiglini, C. Fregosi, S. Pe, E. Parimbelli, G.A. La Maida, E. Gallazzi
  Proceedings of the 30th International Conference on Intelligent User Interfaces (IUI)
• Conformal Prediction for ECG Interpretation: A Study on Human-AI Collaboration in Clinical Decision Support
  D. Folgado, L. Famiglini, A. Campagner, H. Dores, M. Barandas, H. Gamboa, F. Cabitza
  International Conference on Artificial Intelligence in Medicine (AIME), 140–149

2024

• Evaluation of uncertainty quantification methods in multi-label classification: A case study with automatic diagnosis of electrocardiogram
  M. Barandas, L. Famiglini, A. Campagner, D. Folgado, R. Simão, F. Cabitza, H. Gamboa
  Information Fusion 101, 101978
• Evidence-based XAI: An empirical approach to design more effective and explainable decision support systems
  L. Famiglini, A. Campagner, M. Barandas, G.A. La Maida, E. Gallazzi, F. Cabitza
  Computers in Biology and Medicine 170, 108042
• Never tell me the odds: Investigating pro-hoc explanations in medical decision making
  F. Cabitza, C. Natali, L. Famiglini, A. Campagner, V. Caccavella, E. Gallazzi
  Artificial Intelligence in Medicine 150, 102819
• Alternative strategies to generate Class Activation Maps supporting AI-based advice in vertebral fracture detection in X-ray images
  S. Pe, L. Famiglini, E. Gallazzi, C. Bortolotto, L. Carone, A. Cisarri, A. Salina et al.
  Methods of Information in Medicine 63(03/04), 122–136
• Dissimilar similarities: Comparing human and statistical similarity evaluation in medical AI
  F. Cabitza, L. Famiglini, A. Campagner, L.M. Sconfienza, S. Fusco et al.
  International Conference on Modeling Decisions for Artificial Intelligence (MDAI)

2023

• Explainability meets uncertainty quantification: Insights from feature-based model fusion on multimodal time series
  D. Folgado, M. Barandas, L. Famiglini, R. Santos, F. Cabitza, H. Gamboa
  Information Fusion 100, 101955
• Let me think! Investigating the effect of explanations feeding doubts about the AI advice
  F. Cabitza, A. Campagner, L. Famiglini, C. Natali, V. Caccavella, E. Gallazzi
  International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE)
• Towards a rigorous calibration assessment framework: advancements in metrics, methods, and use
  L. Famiglini, A. Campagner, F. Cabitza
  ECAI 2023 — Frontiers in Artificial Intelligence and Applications 372, 645–652
• Everything is varied: The surprising impact of instantial variation on ML reliability
  A. Campagner, L. Famiglini, A. Carobene, F. Cabitza
  Applied Soft Computing 146, 110644
• Color shadows 2: Assessing the impact of XAI on diagnostic decision-making
  C. Natali, L. Famiglini, A. Campagner, G.A. La Maida, E. Gallazzi, F. Cabitza
  World Conference on Explainable Artificial Intelligence, 618–629
• Biomarkers for mixed dementia: a hard bone to bite? Preliminary analyses and promising results for a debated topic
  A. Campagner, L. Famiglini, B. Arosio, P. Rossi, G. Annoni, F. Cabitza
  AIxAS@AI*IA, 136–143

2022

• How is test laboratory data used and characterised by machine learning models? A systematic review of diagnostic and prognostic models developed for COVID-19 patients
  A. Carobene, F. Milella, L. Famiglini, F. Cabitza
  Clinical Chemistry and Laboratory Medicine 60(12), 1887–1901
• A robust and parsimonious machine learning method to predict ICU admission of COVID-19 patients
  L. Famiglini, A. Campagner, A. Carobene, F. Cabitza
  Medical & Biological Engineering & Computing
• Color shadows (part I): Exploratory usability evaluation of activation maps in radiological machine learning
  F. Cabitza, A. Campagner, L. Famiglini, E. Gallazzi, G.A. La Maida
  International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE)
• Application of machine learning to improve appropriateness of treatment in an orthopaedic setting of personalized medicine
  F. Milella, L. Famiglini, G. Banfi, F. Cabitza
  Journal of Personalized Medicine 12(10), 1706
• Global interpretable calibration index, a new metric to estimate machine learning models' calibration
  F. Cabitza, A. Campagner, L. Famiglini
  International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE)
• Re-calibrating machine learning models using confidence interval bounds
  A. Campagner, L. Famiglini, F. Cabitza
  International Conference on Modeling Decisions for Artificial Intelligence (MDAI)
• Detecting the effect size of weather conditions on Patient-Reported Outcome Measures (PROMs)
  F. Milella, A. Seveso, L. Famiglini, G. Banfi, F. Cabitza
  Journal of Personalized Medicine 12(11), 1811
• Machine learning and laboratory values in the diagnosis, prognosis and vaccination strategy of COVID-19
  A. Carobene, L. Famiglini, E. Sabetta, A. Naclerio, G. Banfi
  Artificial Intelligence in COVID-19, 121–156 (book chapter)
• Coloured shadows: understanding the value of visual aided diagnosis through AI-generated saliency maps
  E. Gallazzi, L. Famiglini, G.A. La Maida, P.D. Giorgi, B. Misaggi, F. Cabitza
  Orthopaedic Proceedings 104 (SUPP_14), 20–20
• A parsimonious machine learning approach to detect inappropriate treatments in spine surgery on the basis of patient-reported outcomes
  L. Famiglini, F. Milella, P. Berjano, F. Cabitza
  IADIS International Conference ICT, Society and Human Beings 2022
• Through the looking glass: Understanding the value of visual aided diagnosis through AI-generated saliency maps
  E. Gallazzi, B. Misaggi, L. Famiglini, G.A. La Maida, P.D. Giorgi, F. Cabitza
  Brain and Spine 2, 100967
• A Confidence Interval-Based Method for Classifier Re-Calibration
  A. Campagner, L. Famiglini, F. Cabitza
  Studies in Health Technology and Informatics 294, 127–128

2021

• Surveilling COVID-19 emotional contagion on Twitter by sentiment analysis
  C. Crocamo, M. Viviani, L. Famiglini, F. Bartoli, G. Pasi, G. Carrà
  European Psychiatry 64(1), e17
• Prediction of ICU admission for COVID-19 patients: a machine learning approach based on complete blood count data
  L. Famiglini, G. Bini, A. Carobene, A. Campagner, F. Cabitza
  IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS)
• On the generalization of figurative language detection: the case of irony and sarcasm
  L. Famiglini, E. Fersini, P. Rosso
  International Conference on Applications of Natural Language to Information Systems (NLDB)