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Table_2_Real-world post-deployment performance of a novel m..:
Lucy Thomas
;
Chris Hyde
;
Dan Mullarkey
...
doi:10.3389/fmed.2023.1264846.s003. , 2023
Link:
https://doi.org/10.3389/fmed.2023.1264846.s003
RT Journal T1
Table_2_Real-world post-deployment performance of a novel machine learning-based digital health technology for skin lesion assessment and suggestions for post-market surveillance.docx
UL https://suche.suub.uni-bremen.de/peid=base-ftfrontimediafig:oai:figshare.com:article_24467104&Exemplar=1&LAN=DE A1 Lucy Thomas A1 Chris Hyde A1 Dan Mullarkey A1 Jack Greenhalgh A1 Dilraj Kalsi A1 Justin Ko YR 2023 K1 Dermatology K1 Emergency Medicine K1 Gastroenterology and Hepatology K1 Geriatrics and Gerontology K1 Intensive Care K1 Medical Genetics (excl. Cancer Genetics) K1 Nephrology and Urology K1 Nuclear Medicine K1 Orthopaedics K1 Otorhinolaryngology K1 Pathology (excl. Oral Pathology) K1 Radiology and Organ Imaging K1 Foetal Development and Medicine K1 Obstetrics and Gynaecology K1 Family Care K1 Primary Health Care K1 Medical and Health Sciences not elsewhere classified K1 artificial intelligence K1 skin cancer K1 AI for skin cancer K1 AI as a medical device K1 DERM K1 deep ensemble for the recognition of malignancy K1 Skin Analytics JF doi:10.3389/fmed.2023.1264846.s003 LK http://dx.doi.org/https://doi.org/10.3389/fmed.2023.1264846.s003 DO https://doi.org/10.3389/fmed.2023.1264846.s003 SF ELIB - SuUB Bremen
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