Búsqueda | Biblioteca Virtual en Salud

1.

Machine learning in the prediction of medical inpatient length of stay.

Bacchi, Stephen; Tan, Yiran; Oakden-Rayner, Luke; Jannes, Jim; Kleinig, Timothy; Koblar, Simon.

Intern Med J ; 52(2): 176-185, 2022 Feb.

Artículo en Inglés | MEDLINE | ID: mdl-33094899

2.

Medical student knowledge and critical appraisal of machine learning: a multicentre international cross-sectional study.

Blacketer, Charlotte; Parnis, Roger; B Franke, Kyle; Wagner, Morganne; Wang, David; Tan, Yiran; Oakden-Rayner, Luke; Gallagher, Steve; Perry, Seth W; Licinio, Julio; Symonds, Ian; Thomas, Josephine; Duggan, Paul; Bacchi, Stephen.

Intern Med J ; 51(9): 1539-1542, 2021 Sep.

Artículo en Inglés | MEDLINE | ID: mdl-34541769

3.

Deep Learning Natural Language Processing Successfully Predicts the Cerebrovascular Cause of Transient Ischemic Attack-Like Presentations.

Bacchi, Stephen; Oakden-Rayner, Luke; Zerner, Toby; Kleinig, Timothy; Patel, Sandy; Jannes, Jim.

Stroke ; 50(3): 758-760, 2019 03.

Artículo en Inglés | MEDLINE | ID: mdl-30653397

4.

Assessing the accuracy of ⁶⁸ Ga-PSMA PET/CT compared with MRI in the initial diagnosis of prostate malignancy: A cohort analysis of 114 consecutive patients.

Paterson, Felix; Nottage, Michelle; Kitchener, Michael; Jarvis, Matthew; Reid, Jessica; Oakden-Rayner, Luke.

J Med Imaging Radiat Oncol ; 66(3): 319-323, 2022 Apr.

Artículo en Inglés | MEDLINE | ID: mdl-34250746

5.

Prospective and external validation of stroke discharge planning machine learning models.

Bacchi, Stephen; Oakden-Rayner, Luke; Menon, David K; Moey, Andrew; Jannes, Jim; Kleinig, Timothy; Koblar, Simon.

J Clin Neurosci ; 96: 80-84, 2022 Feb.

Artículo en Inglés | MEDLINE | ID: mdl-34999495

6.

The false hope of current approaches to explainable artificial intelligence in health care.

Ghassemi, Marzyeh; Oakden-Rayner, Luke; Beam, Andrew L.

Lancet Digit Health ; 3(11): e745-e750, 2021 11.

Artículo en Inglés | MEDLINE | ID: mdl-34711379

7.

Chest radiographs and machine learning - Past, present and future.

Jones, Catherine M; Buchlak, Quinlan D; Oakden-Rayner, Luke; Milne, Michael; Seah, Jarrel; Esmaili, Nazanin; Hachey, Ben.

J Med Imaging Radiat Oncol ; 65(5): 538-544, 2021 Aug.

Artículo en Inglés | MEDLINE | ID: mdl-34169648

8.

Assessment of the effect of a comprehensive chest radiograph deep learning model on radiologist reports and patient outcomes: a real-world observational study.

Jones, Catherine M; Danaher, Luke; Milne, Michael R; Tang, Cyril; Seah, Jarrel; Oakden-Rayner, Luke; Johnson, Andrew; Buchlak, Quinlan D; Esmaili, Nazanin.

BMJ Open ; 11(12): e052902, 2021 12 20.

Artículo en Inglés | MEDLINE | ID: mdl-34930738

9.

A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology.

Scheetz, Jane; Rothschild, Philip; McGuinness, Myra; Hadoux, Xavier; Soyer, H Peter; Janda, Monika; Condon, James J J; Oakden-Rayner, Luke; Palmer, Lyle J; Keel, Stuart; van Wijngaarden, Peter.

Sci Rep ; 11(1): 5193, 2021 03 04.

Artículo en Inglés | MEDLINE | ID: mdl-33664367

10.

Do comprehensive deep learning algorithms suffer from hidden stratification? A retrospective study on pneumothorax detection in chest radiography.

Seah, Jarrel; Tang, Cyril; Buchlak, Quinlan D; Milne, Michael Robert; Holt, Xavier; Ahmad, Hassan; Lambert, John; Esmaili, Nazanin; Oakden-Rayner, Luke; Brotchie, Peter; Jones, Catherine M.

BMJ Open ; 11(12): e053024, 2021 12 07.

Artículo en Inglés | MEDLINE | ID: mdl-34876430

11.

Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study.

Seah, Jarrel C Y; Tang, Cyril H M; Buchlak, Quinlan D; Holt, Xavier G; Wardman, Jeffrey B; Aimoldin, Anuar; Esmaili, Nazanin; Ahmad, Hassan; Pham, Hung; Lambert, John F; Hachey, Ben; Hogg, Stephen J F; Johnston, Benjamin P; Bennett, Christine; Oakden-Rayner, Luke; Brotchie, Peter; Jones, Catherine M.

Lancet Digit Health ; 3(8): e496-e506, 2021 08.

Artículo en Inglés | MEDLINE | ID: mdl-34219054

12.

Exploring Large-scale Public Medical Image Datasets.

Oakden-Rayner, Luke.

Acad Radiol ; 27(1): 106-112, 2020 01.

Artículo en Inglés | MEDLINE | ID: mdl-31706792

13.

Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging.

Oakden-Rayner, Luke; Dunnmon, Jared; Carneiro, Gustavo; Ré, Christopher.

Proc ACM Conf Health Inference Learn (2020) ; 2020: 151-159, 2020 Apr.

Artículo en Inglés | MEDLINE | ID: mdl-33196064

14.

Stroke prognostication for discharge planning with machine learning: A derivation study.

Bacchi, Stephen; Oakden-Rayner, Luke; Menon, David K; Jannes, Jim; Kleinig, Timothy; Koblar, Simon.

J Clin Neurosci ; 79: 100-103, 2020 Sep.

Artículo en Inglés | MEDLINE | ID: mdl-33070874

15.

Deep Learning in the Prediction of Ischaemic Stroke Thrombolysis Functional Outcomes: A Pilot Study.

Bacchi, Stephen; Zerner, Toby; Oakden-Rayner, Luke; Kleinig, Timothy; Patel, Sandy; Jannes, Jim.

Acad Radiol ; 27(2): e19-e23, 2020 02.

Artículo en Inglés | MEDLINE | ID: mdl-31053480

16.

Deep learning in the detection of high-grade glioma recurrence using multiple MRI sequences: A pilot study.

Bacchi, Stephen; Zerner, Toby; Dongas, John; Asahina, Adon Toru; Abou-Hamden, Amal; Otto, Sophia; Oakden-Rayner, Luke; Patel, Sandy.

J Clin Neurosci ; 70: 11-13, 2019 Dec.

Artículo en Inglés | MEDLINE | ID: mdl-31648967

17.

Deep learning predicts hip fracture using confounding patient and healthcare variables.

Badgeley, Marcus A; Zech, John R; Oakden-Rayner, Luke; Glicksberg, Benjamin S; Liu, Manway; Gale, William; McConnell, Michael V; Percha, Bethany; Snyder, Thomas M; Dudley, Joel T.

NPJ Digit Med ; 2: 31, 2019.

Artículo en Inglés | MEDLINE | ID: mdl-31304378

18.

Directrices para presentación de informes de ensayos clínicos sobre intervenciones con inteligencia artificial: extensión CONSORT-AI / Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension / Diretrizes para relatórios de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão CONSORT-AI

Liu, Xiaoxuan; Cruz Rivera, Samantha; Moher, David; Calvert, Melanie J.; Denniston, Alastair K.; Denniston, Alastair K.; Chan, An-Wen; Darzi, Ara; Holmes, Christopher; Yau, Christopher; Moher, David; Ashrafian, Hutan; Deeks, Jonathan J.; Ferrante di Ruffano, Lavinia; Faes, Livia; Calvert, Melanie J.; Keane, Pearse A.; Cruz Rivera, Samantha; Vollmer, Sebastian J.; Liu, Xiaoxuan; Lee, Aaron Y.; Jonas, Adrian; Esteva, Andre; Beam, Andrew L.; Chan, An-Wen; Panico, Maria Beatrice; Lee, Cecilia S.; Haug, Charlotte; Kelly, Christophe J.; Yau, Christopher; Mulrow, Cynthia; Espinoza, Cyrus; Fletcher, John; Moher, David; Paltoo, Dina; Manna, Elaine; Price, Gary; Collins, Gary S.; Harvey, Hugh; Matcham, James; Monteiro, Joao; Khair ElZarrad, M.; Ferrante di Ruffano, Lavinia; Oakden-Rayner, Luke; Calvert, Melanie J.; McCradden, Melissa; Keane, Pearse A.; Savage, Richard; Golub, Robert; Sarkar, Rupa; Rowley, Samuel; Grupo de Trabajo SPIRIT-AI y CONSORT-AI; GRUPO DE DIRECCIÓN SPIRIT-AI Y CONSORT-AI; Grupo de Consenso SPIRIT-AI y CONSORT-AI.

Rev. panam. salud pública ; 48: e13, 2024. tab, graf

Artículo en Español | LILACS-Express | ID: biblio-1536672

19.

Directrices para los protocolos de ensayos clínicos de intervenciones con inteligencia artificial: la extensión SPIRIT-AI / Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension / Diretrizes para protocolos de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão SPIRIT-AI

Cruz Rivera, Samantha; Liu, Xiaoxuan; Chan, An-Wen; Denniston, Alastair K.; Calvert, Melanie J.; Denniston, Alastair K.; Chan, An-Wen; Darzi, Ara; Holmes, Christopher; Yau, Christopher; Moher, David; Ashrafian, Hutan; Deeks, Jonathan J.; Ferrante di Ruffano, Lavinia; Faes, Livia; Calvert, Melanie J.; Keane, Pearse A.; Cruz Rivera, Samantha; Vollmer, Sebastian J.; Liu, Xiaoxuan; Lee, Aaron Y.; Jonas, Adrian; Esteva, Andre; Beam, Andrew L.; Chan, An-Wen; Panico, Maria Beatrice; Lee, Cecilia S.; Haug, Charlotte; Kelly, Christophe J.; Yau, Christopher; Mulrow, Cynthia; Espinoza, Cyrus; Fletcher, John; Moher, David; Paltoo, Dina; Manna, Elaine; Price, Gary; Collins, Gary S.; Harvey, Hugh; Matcham, James; Monteiro, Joao; Khair ElZarrad, M.; Ferrante di Ruffano, Lavinia; Oakden-Rayner, Luke; Calvert, Melanie J.; McCradden, Melissa; Keane, Pearse A.; Savage, Richard; Golub, Robert; Sarkar, Rupa; Rowley, Samuel; Grupo de Trabajo SPIRIT-AI y CONSORT-AI; Grupo Directivo SPIRIT-AI y CONSORT-AI; Grupo de Consenso SPIRIT-AI y CONSORT-AI.

Rev. panam. salud pública ; 48: e12, 2024. tab, graf

Artículo en Español | LILACS-Express | ID: biblio-1536674

20.

Directrices para los protocolos de ensayos clínicos de intervenciones con inteligencia artificial: la extensión SPIRIT-AI / Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension / Diretrizes para protocolos de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão SPIRIT-AI

Cruz Rivera, Samantha; Liu, Xiaoxuan; Chan, An-Wen; Denniston, Alastair K.; Calvert, Melanie J.; Denniston, Alastair K.; Chan, An-Wen; Darzi, Ara; Holmes, Christopher; Yau, Christopher; Moher, David; Ashrafian, Hutan; Deeks, Jonathan J.; Ferrante di Ruffano, Lavinia; Faes, Livia; Calvert, Melanie J.; Keane, Pearse A.; Cruz Rivera, Samantha; Vollmer, Sebastian J.; Liu, Xiaoxuan; Lee, Aaron Y.; Jonas, Adrian; Esteva, Andre; Beam, Andrew L.; Chan, An-Wen; Panico, Maria Beatrice; Lee, Cecilia S.; Haug, Charlotte; Kelly, Christophe J.; Yau, Christopher; Mulrow, Cynthia; Espinoza, Cyrus; Fletcher, John; Moher, David; Paltoo, Dina; Manna, Elaine; Price, Gary; Collins, Gary S.; Harvey, Hugh; Matcham, James; Monteiro, Joao; Khair ElZarrad, M.; Ferrante di Ruffano, Lavinia; Oakden-Rayner, Luke; Calvert, Melanie J.; McCradden, Melissa; Keane, Pearse A.; Savage, Richard; Golub, Robert; Sarkar, Rupa; Rowley, Samuel; Grupo de Trabajo SPIRIT-AI y CONSORT-AI; Grupo Directivo SPIRIT-AI y CONSORT-AI; Grupo de Consenso SPIRIT-AI y CONSORT-AI.

Rev. panam. salud pública ; 47: e149, 2023. tab, graf

Artículo en Español | LILACS-Express | ID: biblio-1536665

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Machine learning in the prediction of medical inpatient length of stay.

Medical student knowledge and critical appraisal of machine learning: a multicentre international cross-sectional study.

Deep Learning Natural Language Processing Successfully Predicts the Cerebrovascular Cause of Transient Ischemic Attack-Like Presentations.

Assessing the accuracy of ⁶⁸ Ga-PSMA PET/CT compared with MRI in the initial diagnosis of prostate malignancy: A cohort analysis of 114 consecutive patients.

Prospective and external validation of stroke discharge planning machine learning models.

The false hope of current approaches to explainable artificial intelligence in health care.

Chest radiographs and machine learning - Past, present and future.

Assessment of the effect of a comprehensive chest radiograph deep learning model on radiologist reports and patient outcomes: a real-world observational study.

A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology.

Do comprehensive deep learning algorithms suffer from hidden stratification? A retrospective study on pneumothorax detection in chest radiography.

Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study.

Exploring Large-scale Public Medical Image Datasets.

Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging.

Stroke prognostication for discharge planning with machine learning: A derivation study.

Deep Learning in the Prediction of Ischaemic Stroke Thrombolysis Functional Outcomes: A Pilot Study.

Deep learning in the detection of high-grade glioma recurrence using multiple MRI sequences: A pilot study.

Deep learning predicts hip fracture using confounding patient and healthcare variables.