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Big Data and Machine Learning in Health Care

JAMA : the journal of the American Medical Association, 2018-04, Vol.319 (13), p.1317

2018

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Challenges to the Reproducibility of Machine Learning Models in Health Care

JAMA : the journal of the American Medical Association, 2020-01, Vol.323 (4), p.305

2020

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

The Lancet. Digital health, 2021-11, Vol.3 (11), p.e745-e750

2021

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Adversarial attacks on medical machine learning

Science (American Association for the Advancement of Science), 2019-03, Vol.363 (6433), p.1287-1289

2019

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Translating Artificial Intelligence Into Clinical Care

JAMA : the journal of the American Medical Association, 2016-12, Vol.316 (22), p.2368

2016

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Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence

BMJ open, 2021-07, Vol.11 (7), p.e048008-e048008

2021

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Artificial intelligence in healthcare

Nature biomedical engineering, 2018-10, Vol.2 (10), p.719-731

2018

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Artificial Intelligence in Medicine

The New England journal of medicine, 2023-03, Vol.388 (13), p.1220-1221

2023

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Machine Learning and Statistics in Clinical Research Articles-Moving Past the False Dichotomy

JAMA pediatrics, 2023-05, Vol.177 (5), p.448

2023

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Sharpening the resolution on data matters: a brief roadmap for understanding deep learning for medical data

The spine journal, 2021-10, Vol.21 (10), p.1606-1609

2021

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Artificial intelligence in the neonatal intensive care unit: the time is now

Journal of perinatology, 2024-01, Vol.44 (1), p.131-135

2024

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Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension

Nature medicine, 2020-09, Vol.26 (9), p.1364-1374

2020

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Bayesian neural networks for detecting epistasis in genetic association studies

BMC bioinformatics, 2014-11, Vol.15 (1), p.368-368, Article 368

2014

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Illuminating protein space with a programmable generative model

Nature (London), 2023-11, Vol.623 (7989), p.1070-1078

2023

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Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension

Nature medicine, 2020-09, Vol.26 (9), p.1351-1363

2020

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Concordance between gene expression in peripheral whole blood and colonic tissue in children with inflammatory bowel disease

PloS one, 2019-10, Vol.14 (10), p.e0222952

2019

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Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension

BMJ (Online), 2020-09, Vol.370, p.m3164

2020

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Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures

Entropy (Basel, Switzerland), 2021-11, Vol.23 (12), p.1608

2021

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Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension

BMJ (Online), 2020-09, Vol.370, p.m3210

2020

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Time to reality check the promises of machine learning-powered precision medicine

The Lancet. Digital health, 2020-12, Vol.2 (12), p.e677-e680

2020

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Estimates of healthcare spending for preterm and low-birthweight infants in a commercially insured population: 2008-2016

Journal of perinatology, 2020-07, Vol.40 (7), p.1091-1099

2020

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Prediction of extubation failure among low birthweight neonates using machine learning

Journal of perinatology, 2023-02, Vol.43 (2), p.209-214

2023

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Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2020, Vol.25, p.295

2020

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Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension

The Lancet. Digital health, 2020-10, Vol.2 (10), p.e537-e548

2020

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Machine learning for patient risk stratification: standing on, or looking over, the shoulders of clinicians?

NPJ digital medicine, 2021-03, Vol.4 (1), p.62-62, Article 62

2021

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Big Data and Machine Learning in Health Care

Challenges to the Reproducibility of Machine Learning Models in Health Care

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

Adversarial attacks on medical machine learning

Translating Artificial Intelligence Into Clinical Care

Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence

Artificial intelligence in healthcare

Artificial Intelligence in Medicine

Machine Learning and Statistics in Clinical Research Articles-Moving Past the False Dichotomy

Sharpening the resolution on data matters: a brief roadmap for understanding deep learning for medical data

Artificial intelligence in the neonatal intensive care unit: the time is now

Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension

Bayesian neural networks for detecting epistasis in genetic association studies

Illuminating protein space with a programmable generative model

Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension

Concordance between gene expression in peripheral whole blood and colonic tissue in children with inflammatory bowel disease

Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension

Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures

Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension

Time to reality check the promises of machine learning-powered precision medicine

Estimates of healthcare spending for preterm and low-birthweight infants in a commercially insured population: 2008-2016

Prediction of extubation failure among low birthweight neonates using machine learning

Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data

Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension

Machine learning for patient risk stratification: standing on, or looking over, the shoulders of clinicians?

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