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This document discusses the current AI scientific evidence in thoracic imaging, its potential clinical utility, implementation and costs, training requirements and validation, its\u2019 effect on the training of new radiologists,\u00a0post-implementation issues, and medico-legal and ethical issues. All these issues have to be addressed and overcome, for AI to become implemented clinically in thoracic radiology.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      \u2022\n                      <jats:italic>Assessing the datasets used for training and validation of the AI system is essential.<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      \u2022\n                      <jats:italic>A departmental strategy and business plan which includes continuing quality assurance of AI system and a sustainable financial plan is important for successful implementation.<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      \u2022\n                      <jats:italic>Awareness of the negative effect on training of new radiologists is vital.<\/jats:italic>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-023-09409-2","type":"journal-article","created":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T00:07:54Z","timestamp":1675296474000},"page":"5077-5086","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Implementation of artificial intelligence in thoracic imaging\u2014a what, how, and why guide from the European Society of Thoracic Imaging (ESTI)"],"prefix":"10.1007","volume":"33","author":[{"given":"Fergus","family":"Gleeson","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marie-Pierre","family":"Revel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J\u00fcrgen","family":"Biederer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna Rita","family":"Larici","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katharina","family":"Martini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Frauenfelder","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicholas","family":"Screaton","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Helmut","family":"Prosch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Annemiek","family":"Snoeckx","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicola","family":"Sverzellati","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benoit","family":"Ghaye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3612-3446","authenticated-orcid":false,"given":"Anagha P.","family":"Parkar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,2,2]]},"reference":[{"key":"9409_CR1","doi-asserted-by":"publisher","first-page":"2113","DOI":"10.1148\/rg.2017170077","volume":"37","author":"G Chartrand","year":"2017","unstructured":"Chartrand G, Cheng PM, Vorontsov E et al (2017) Deep learning: a primer for radiologists. 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