@article{
 title = {Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans},
 type = {article},
 year = {2021},
 pages = {199-217},
 volume = {3},
 id = {1e7f9170-24be-3892-9c28-30a22304c004},
 created = {2024-01-13T06:15:54.683Z},
 file_attached = {true},
 profile_id = {d5b9f595-4104-33d2-b640-e84dee1b7ded},
 last_modified = {2025-02-21T12:00:32.714Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 folder_uuids = {ded4b384-1a9c-491d-88dd-c5e6a88d479d},
 private_publication = {false},
 abstract = {Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we consider all published papers and preprints, for the period from 1 January 2020 to 3 October 2020, which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. All manuscripts uploaded to bioRxiv, medRxiv and arXiv along with all entries in EMBASE and MEDLINE in this timeframe are considered. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 62 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher-quality model development and well-documented manuscripts.},
 bibtype = {article},
 author = {Roberts, Michael and Driggs, Derek and Thorpe, Matthew and Gilbey, Julian and Yeung, Michael and Ursprung, Stephan and Aviles-Rivero, Angelica I. and Etmann, Christian and McCague, Cathal and Beer, Lucian and Weir-McCall, Jonathan R. and Teng, Zhongzhao and Gkrania-Klotsas, Effrossyni and Ruggiero, Alessandro and Korhonen, Anna and Jefferson, Emily and Ako, Emmanuel and Langs, Georg and Gozaliasl, Ghassem and Yang, Guang and Prosch, Helmut and Preller, Jacobus and Stanczuk, Jan and Tang, Jing and Hofmanninger, Johannes and Babar, Judith and Sánchez, Lorena Escudero and Thillai, Muhunthan and Gonzalez, Paula Martin and Teare, Philip and Zhu, Xiaoxiang and Patel, Mishal and Cafolla, Conor and Azadbakht, Hojjat and Jacob, Joseph and Lowe, Josh and Zhang, Kang and Bradley, Kyle and Wassin, Marcel and Holzer, Markus and Ji, Kangyu and Ortet, Maria Delgado and Ai, Tao and Walton, Nicholas and Lio, Pietro and Stranks, Samuel and Shadbahr, Tolou and Lin, Weizhe and Zha, Yunfei and Niu, Zhangming and Rudd, James H.F. and Sala, Evis and Schönlieb, Carola Bibiane},
 doi = {10.1038/s42256-021-00307-0},
 journal = {Nature Machine Intelligence},
 number = {3}
}

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To address this, we give many recommendations which, if followed, will solve these issues and lead to higher-quality model development and well-documented manuscripts.","bibtype":"article","author":"Roberts, Michael and Driggs, Derek and Thorpe, Matthew and Gilbey, Julian and Yeung, Michael and Ursprung, Stephan and Aviles-Rivero, Angelica I. and Etmann, Christian and McCague, Cathal and Beer, Lucian and Weir-McCall, Jonathan R. and Teng, Zhongzhao and Gkrania-Klotsas, Effrossyni and Ruggiero, Alessandro and Korhonen, Anna and Jefferson, Emily and Ako, Emmanuel and Langs, Georg and Gozaliasl, Ghassem and Yang, Guang and Prosch, Helmut and Preller, Jacobus and Stanczuk, Jan and Tang, Jing and Hofmanninger, Johannes and Babar, Judith and Sánchez, Lorena Escudero and Thillai, Muhunthan and Gonzalez, Paula Martin and Teare, Philip and Zhu, Xiaoxiang and Patel, Mishal and Cafolla, Conor and Azadbakht, Hojjat and Jacob, Joseph and Lowe, Josh and Zhang, Kang and Bradley, Kyle and Wassin, Marcel and Holzer, Markus and Ji, Kangyu and Ortet, Maria Delgado and Ai, Tao and Walton, Nicholas and Lio, Pietro and Stranks, Samuel and Shadbahr, Tolou and Lin, Weizhe and Zha, Yunfei and Niu, Zhangming and Rudd, James H.F. and Sala, Evis and Schönlieb, Carola Bibiane","doi":"10.1038/s42256-021-00307-0","journal":"Nature Machine Intelligence","number":"3","bibtex":"@article{\n title = {Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans},\n type = {article},\n year = {2021},\n pages = {199-217},\n volume = {3},\n id = {1e7f9170-24be-3892-9c28-30a22304c004},\n created = {2024-01-13T06:15:54.683Z},\n file_attached = {true},\n profile_id = {d5b9f595-4104-33d2-b640-e84dee1b7ded},\n last_modified = {2025-02-21T12:00:32.714Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {ded4b384-1a9c-491d-88dd-c5e6a88d479d},\n private_publication = {false},\n abstract = {Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. 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