Explainable AI in radiology: a white paper of the Italian Society of Medical and Interventional Radiology.

Journal: La Radiologia medica
Published Date:

Abstract

The term Explainable Artificial Intelligence (xAI) groups together the scientific body of knowledge developed while searching for methods to explain the inner logic behind the AI algorithm and the model inference based on knowledge-based interpretability. The xAI is now generally recognized as a core area of AI. A variety of xAI methods currently are available to researchers; nonetheless, the comprehensive classification of the xAI methods is still lacking. In addition, there is no consensus among the researchers with regards to what an explanation exactly is and which are salient properties that must be considered to make it understandable for every end-user. The SIRM introduces an xAI-white paper, which is intended to aid Radiologists, medical practitioners, and scientists in the understanding an emerging field of xAI, the black-box problem behind the success of the AI, the xAI methods to unveil the black-box into a glass-box, the role, and responsibilities of the Radiologists for appropriate use of the AI-technology. Due to the rapidly changing and evolution of AI, a definitive conclusion or solution is far away from being defined. However, one of our greatest responsibilities is to keep up with the change in a critical manner. In fact, ignoring and discrediting the advent of AI a priori will not curb its use but could result in its application without awareness. Therefore, learning and increasing our knowledge about this very important technological change will allow us to put AI at our service and at the service of the patients in a conscious way, pushing this paradigm shift as far as it will benefit us.

Authors

  • Emanuele Neri
    Department of Radiological Sciences, University of Pisa, Via Savi 10, 56126 Pisa, Italy.
  • Gayane Aghakhanyan
    Academic Radiology, Department of Translational Research and of New Surgical and Medical Technology, University of Pisa, 56126, Pisa, Italy. gayane.aghakhanyan@med.unipi.it.
  • Marta Zerunian
    Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy.
  • Nicoletta Gandolfo
    Diagnostic Imaging Department, VillaScassi Hospital-ASL 3, Corso Scassi 1, Genoa, Italy.
  • Roberto Grassi
    Department of Radiology, University of Campania "L. Vanvitelli", Naples, Italy.
  • Vittorio Miele
    Department of Radiology, Careggi University Hospital, L.go G.A. Brambilla, 3, 50134, Florence, Italy. vmiele@sirm.org.
  • Andrea Giovagnoni
    Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Via Tronto, 10/A, 60126, Ancona, Italy.
  • Andrea Laghi
    Department of Radiological Sciences, Oncology and Pathology, University La Sapienza, AOU Sant'Andrea, Via di Grottarossa 1035, 00189 Rome, Italy.