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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.<\/jats:p>","DOI":"10.1007\/s11547-023-01634-5","type":"journal-article","created":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T11:01:57Z","timestamp":1683543717000},"page":"755-764","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["Explainable AI in radiology: a white paper of the Italian Society of Medical and Interventional Radiology"],"prefix":"10.1007","volume":"128","author":[{"given":"Emanuele","family":"Neri","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5152-497X","authenticated-orcid":false,"given":"Gayane","family":"Aghakhanyan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marta","family":"Zerunian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicoletta","family":"Gandolfo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roberto","family":"Grassi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vittorio","family":"Miele","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea","family":"Giovagnoni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea","family":"Laghi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"SIRM expert group on Artificial Intelligence","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,5,8]]},"reference":[{"key":"1634_CR1","doi-asserted-by":"publisher","first-page":"955","DOI":"10.1016\/j.rcl.2021.06.006","volume":"59","author":"E Ranschaert","year":"2021","unstructured":"Ranschaert E, Topff L, Pianykh O (2021) Optimization of radiology workflow with artificial intelligence. 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