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How to develop machine learning models for healthcare

Nature materials, 2019-05, Vol.18 (5), p.410-414

2019

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Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer

NPJ digital medicine, 2019-06, Vol.2 (1), p.48-48, Article 48

2019

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Interpretable survival prediction for colorectal cancer using deep learning

NPJ digital medicine, 2021-04, Vol.4 (1), p.71-71, Article 71

2021

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Deep learning for distinguishing normal versus abnormal chest radiographs and generalization to two unseen diseases tuberculosis and COVID-19

Scientific reports, 2021-09, Vol.11 (1), p.15523-15523, Article 15523

2021

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Comparative analysis of machine learning approaches to classify tumor mutation burden in lung adenocarcinoma using histopathology images

Scientific reports, 2021-08, Vol.11 (1), p.16605-16605, Article 16605

2021

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Chest Radiograph Interpretation with Deep Learning Models: Assessment with Radiologist-adjudicated Reference Standards and Population-adjusted Evaluation

Radiology, 2020-02, Vol.294 (2), p.421-431

2020

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An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis

Nature medicine, 2019-09, Vol.25 (9), p.1453-1457

2019

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Vulnerability of Antarctica’s ice shelves to meltwater-driven fracture

Nature (London), 2020-08, Vol.584 (7822), p.574-578

2020

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Deep learning-based survival prediction for multiple cancer types using histopathology images

PloS one, 2020-06, Vol.15 (6), p.e0233678-e0233678

2020

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Artificial intelligence in digital breast pathology: Techniques and applications

Breast (Edinburgh), 2020-02, Vol.49, p.267-273

2020

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How to Read Articles That Use Machine Learning: Users' Guides to the Medical Literature

JAMA : the journal of the American Medical Association, 2019-11, Vol.322 (18), p.1806

2019

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Development and Validation of a Deep Learning Algorithm for Gleason Grading of Prostate Cancer From Biopsy Specimens

JAMA oncology, 2020-09, Vol.6 (9), p.1372

2020

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An End-to-End Platform for Digital Pathology Using Hyperspectral Autofluorescence Microscopy and Deep Learning-Based Virtual Histology

Modern pathology, 2024-02, Vol.37 (2), p.100377-100377, Article 100377

2024

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Evaluation of artificial intelligence on a reference standard based on subjective interpretation

The Lancet. Digital health, 2021-11, Vol.3 (11), p.e693-e695

2021

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Evaluation of the Use of Combined Artificial Intelligence and Pathologist Assessment to Review and Grade Prostate Biopsies

JAMA Network Open, 2020-11, Vol.3 (11), p.e2023267-e2023267

2020

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Pathologist Validation of a Machine Learning-Derived Feature for Colon Cancer Risk Stratification

JAMA network open, 2023-03, Vol.6 (3), p.e2254891-e2254891

2023

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Closing the translation gap: AI applications in digital pathology

Biochimica et biophysica acta. Reviews on cancer, 2021-01, Vol.1875 (1), p.188452-188452, Article 188452

2021

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Deep learning models for histologic grading of breast cancer and association with disease prognosis

NPJ breast cancer, 2022-10, Vol.8 (1), p.113-113, Article 113

2022

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Health equity assessment of machine learning performance (HEAL): a framework and dermatology AI model case study

EClinicalMedicine, 2024-04, Vol.70, p.102479-102479, Article 102479

2024

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Current and future applications of artificial intelligence in pathology: a clinical perspective

Journal of clinical pathology, 2021-07, Vol.74 (7), p.409-414

2021

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Deep Learning Detection of Active Pulmonary Tuberculosis at Chest Radiography Matched the Clinical Performance of Radiologists

Radiology, 2023-01, Vol.306 (1), p.124-137

2023

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Reply: ‘The importance of study design in the application of artificial intelligence methods in medicine’

NPJ digital medicine, 2019-10, Vol.2 (1), p.100-100, Article 100

2019

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Clinical-Grade Validation of an Autofluorescence Virtual Staining System With Human Experts and a Deep Learning System for Prostate Cancer

Modern pathology, 2024-11, Vol.37 (11), p.100573, Article 100573

2024

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How to develop machine learning models for healthcare

Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer

Interpretable survival prediction for colorectal cancer using deep learning

Deep learning for distinguishing normal versus abnormal chest radiographs and generalization to two unseen diseases tuberculosis and COVID-19

Comparative analysis of machine learning approaches to classify tumor mutation burden in lung adenocarcinoma using histopathology images

Chest Radiograph Interpretation with Deep Learning Models: Assessment with Radiologist-adjudicated Reference Standards and Population-adjusted Evaluation

An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis

Vulnerability of Antarctica’s ice shelves to meltwater-driven fracture

Deep learning-based survival prediction for multiple cancer types using histopathology images

Artificial intelligence in digital breast pathology: Techniques and applications

How to Read Articles That Use Machine Learning: Users' Guides to the Medical Literature

Development and Validation of a Deep Learning Algorithm for Gleason Grading of Prostate Cancer From Biopsy Specimens

An End-to-End Platform for Digital Pathology Using Hyperspectral Autofluorescence Microscopy and Deep Learning-Based Virtual Histology

Evaluation of artificial intelligence on a reference standard based on subjective interpretation

Evaluation of the Use of Combined Artificial Intelligence and Pathologist Assessment to Review and Grade Prostate Biopsies

Pathologist Validation of a Machine Learning-Derived Feature for Colon Cancer Risk Stratification

Closing the translation gap: AI applications in digital pathology

Deep learning models for histologic grading of breast cancer and association with disease prognosis

Health equity assessment of machine learning performance (HEAL): a framework and dermatology AI model case study

Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge

Current and future applications of artificial intelligence in pathology: a clinical perspective

Deep Learning Detection of Active Pulmonary Tuberculosis at Chest Radiography Matched the Clinical Performance of Radiologists

Reply: ‘The importance of study design in the application of artificial intelligence methods in medicine’

Clinical-Grade Validation of an Autofluorescence Virtual Staining System With Human Experts and a Deep Learning System for Prostate Cancer

Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging

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