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Presenting machine learning model information to clinical end users with model facts labels

NPJ digital medicine, 2020-03, Vol.3 (1), p.41-41, Article 41

2020

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Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study

PLoS medicine, 2018-11, Vol.15 (11), p.e1002701-e1002701

2018

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A framework for understanding label leakage in machine learning for health care

Journal of the American Medical Informatics Association : JAMIA, 2023-12, Vol.31 (1), p.274-280

2023

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Integrating a Machine Learning System Into Clinical Workflows: Qualitative Study

Journal of medical Internet research, 2020-11, Vol.22 (11), p.e22421-e22421

2020

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Looking for clinician involvement under the wrong lamp post: The need for collaboration measures

Journal of the American Medical Informatics Association : JAMIA, 2021-10, Vol.28 (11), p.2541-2542

2021

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Strengthening the use of artificial intelligence within healthcare delivery organizations: balancing regulatory compliance and patient safety

Journal of the American Medical Informatics Association : JAMIA, 2024-06, Vol.31 (7), p.1622-1627

2024

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Respiratory Syncytial Virus During the COVID-19 Pandemic Compared to Historic Levels: A Retrospective Cohort Study of a Health System

The Journal of infectious diseases, 2022-08, Vol.226 (Supplement_2), p.S175-S183

2022

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Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study

JMIR medical informatics, 2020-07, Vol.8 (7), p.e15182-e15182

2020

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Social determinants of health data in solid organ transplantation: National data sources and future directions

American journal of transplantation, 2022-10, Vol.22 (10), p.2293-2301

2022

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From pre-test and post-test probabilities to medical decision making

BMC medical informatics and decision making, 2024-07, Vol.24 (1), p.210-8, Article 210

2024

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Barriers to Achieving Economies of Scale in Analysis of EHR Data. A Cautionary Tale

Applied clinical informatics, 2017-08, Vol.8 (3), p.826-831

2017

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Preliminary results of a clinical research and innovation scholarship to prepare medical students to lead innovations in health care

Healthcare : the journal of delivery science and innovation, 2021-09, Vol.9 (3), p.100555-100555, Article 100555

2021

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Accelerating health system innovation: principles and practices from the Duke Institute for Health Innovation

Patterns (New York, N.Y.), 2023-04, Vol.4 (4), p.100710-100710, Article 100710

2023

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Development and preliminary testing of Health Equity Across the AI Lifecycle (HEAAL): A framework for healthcare delivery organizations to mitigate the risk of AI solutions worsening health inequities

PLOS digital health, 2024-05, Vol.3 (5), p.e0000390-e0000390

2024

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Frameworks for procurement, integration, monitoring, and evaluation of artificial intelligence tools in clinical settings: A systematic review

PLOS digital health, 2024-05, Vol.3 (5), p.e0000514-e0000514

2024

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Editorial: Surfacing best practices for AI software development and integration in healthcare

Frontiers in digital health, 2023, Vol.5, p.1150875

2023

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The algorithm journey map: a tangible approach to implementing AI solutions in healthcare

NPJ digital medicine, 2024-04, Vol.7 (1), p.87-87, Article 87

2024

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Empowering US healthcare delivery organizations: Cultivating a community of practice to harness AI and advance health equity

PLOS Digital Health, 2024-06, Vol.3 (6), p.e0000513

2024

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Development and Integration of Machine Learning Algorithm to Identify Peripheral Arterial Disease: Multistakeholder Qualitative Study

JMIR formative research, 2023-09, Vol.7, p.e43963-e43963

2023

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Use of convolutional neural networks in skin lesion analysis using real world image and non-image data

Frontiers in medicine, 2022-10, Vol.9, p.946937-946937

2022

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Do no harm: a roadmap for responsible machine learning for health care

Nature medicine, 2019-09, Vol.25 (9), p.1337-1340

2019

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Barriers to Achieving Economies of Scale in Analysis of EHR Data

Applied clinical informatics, 2017, Vol.8 (3), p.826-831

2017

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AI on the Front Lines

MIT Sloan management review, 2022-06, Vol.63 (4), p.44-50

2022

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In reply

Annals of emergency medicine, 2024-09, Vol.84 (3), p.334

2024

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Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

Nature medicine, 2022-05, Vol.28 (5), p.924-933

2022

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Presenting machine learning model information to clinical end users with model facts labels

Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study

A framework for understanding label leakage in machine learning for health care

Integrating a Machine Learning System Into Clinical Workflows: Qualitative Study

Looking for clinician involvement under the wrong lamp post: The need for collaboration measures

Strengthening the use of artificial intelligence within healthcare delivery organizations: balancing regulatory compliance and patient safety

Respiratory Syncytial Virus During the COVID-19 Pandemic Compared to Historic Levels: A Retrospective Cohort Study of a Health System

Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study

Social determinants of health data in solid organ transplantation: National data sources and future directions

From pre-test and post-test probabilities to medical decision making

Barriers to Achieving Economies of Scale in Analysis of EHR Data. A Cautionary Tale

Preliminary results of a clinical research and innovation scholarship to prepare medical students to lead innovations in health care

Accelerating health system innovation: principles and practices from the Duke Institute for Health Innovation

Development and preliminary testing of Health Equity Across the AI Lifecycle (HEAAL): A framework for healthcare delivery organizations to mitigate the risk of AI solutions worsening health inequities

Frameworks for procurement, integration, monitoring, and evaluation of artificial intelligence tools in clinical settings: A systematic review

Editorial: Surfacing best practices for AI software development and integration in healthcare

The algorithm journey map: a tangible approach to implementing AI solutions in healthcare

Empowering US healthcare delivery organizations: Cultivating a community of practice to harness AI and advance health equity

Development and Integration of Machine Learning Algorithm to Identify Peripheral Arterial Disease: Multistakeholder Qualitative Study

Use of convolutional neural networks in skin lesion analysis using real world image and non-image data

Do no harm: a roadmap for responsible machine learning for health care

Barriers to Achieving Economies of Scale in Analysis of EHR Data

AI on the Front Lines

In reply

Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

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