Publications

This selection of studies highlights the potential of real-world imaging data to advance AI applications in healthcare.

Featured Publication

Generating Synthetic Data for Medical Imaging

Radiol. 2024 Sep;312(3):e232471. Koetzier LR, et al.

Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging

npj Dig. Med. 2024 Jul;7(195). De Mortanges PA, et. Al.

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Quantitative Evaluation of Large Language Models to Streamline Radiology Report Impressions: A Multimodal Retrospective Analysis

Radiology. 2024 Mar 310(3):e231593. Doshi R, et al.

How AI is being used to accelerate clinical trials

Nature. 2024 Mar 627:S2-S5. Hutson M.

Federated Learning is not a Cure-all for Data Ethics

Nature Machine Intelligence. 2024 Mar 6(4). Bak M, et al.

Radiomic Predicts Early Response to CDK4/6 Inhibitors in Hormone Receptor Positive Metastatic Breast Cancer

npj Breast Cancer. 2023 Aug 9, 67. Khorrami, M. et al.

Real-World Evidence: A Primer

Pharmaceutical Medicine. 2023 Jan 37(1):25-36 Dang A.

Tumor Twistedness Serves as AI-Based Biomarker for Lung Cancer Immunotherapy

National Cancer Institute. 2023 Jan.

Data Preparation for Artificial Intelligence. Chapter In book: Artificial Intelligence in Cardiothoracic Imaging

Springer. 2022 Apr; Artificial Intelligence in Cardiothoracic Imaging (pp.37-43). ISBN 9783030920876. De Araujo AL, et al.

Generating Evidence for Artificial Intelligence-Based Medical Devices: A Framework for training, validation and evaluation

World Health Organization. 2021 Nov; Chapter 7. Internal Validation and Data Management (pp.33-35). ISBN: 9789240038462Digital Health and Innovation (DHI), Medical Devices and Diagnostics (MDD)

Artificial Intelligence and Machine Learning in Radiology: Current State and Considerations for Routine Clinical Implementation

Invest Radiol. 2020 Sep;55(9):619-627. Wichmann JL, et al.

Preparing Medical Imaging Data for Machine Learning

Radiology. 2020 Apr;295(1):4-15. Willemink MJ, et al.

Generating Synthetic Data for Medical Imaging

Radiol. 2024 Sep;312(3):e232471. Koetzier LR, et al.

Preparing Medical Imaging Data for Machine Learning

Radiology. 2020 Apr;295(1):4-15. Willemink MJ, et al.

Artificial Intelligence and Machine Learning in Radiology: Current State and Considerations for Routine Clinical Implementation

Invest Radiol. 2020 Sep;55(9):619-627. Wichmann JL, et al.

Data Preparation for Artificial Intelligence. Chapter In book: Artificial Intelligence in Cardiothoracic Imaging

Springer. 2022 Apr; Artificial Intelligence in Cardiothoracic Imaging (pp.37-43). ISBN 9783030920876. De Araujo AL, et al.

Generating Evidence for Artificial Intelligence-Based Medical Devices: A Framework for training, validation and evaluation

World Health Organization. 2021 Nov; Chapter 7. Internal Validation and Data Management (pp.33-35). ISBN: 9789240038462Digital Health and Innovation (DHI), Medical Devices and Diagnostics (MDD)

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Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging

npj Dig. Med. 2024 Jul;7(195). De Mortanges PA, et. Al.

Radiomic Predicts Early Response to CDK4/6 Inhibitors in Hormone Receptor Positive Metastatic Breast Cancer

npj Breast Cancer. 2023 Aug 9, 67. Khorrami, M. et al.

Tumor Twistedness Serves as AI-Based Biomarker for Lung Cancer Immunotherapy

National Cancer Institute. 2023 Jan.

Real-World Evidence: A Primer

Pharmaceutical Medicine. 2023 Jan 37(1):25-36 Dang A.

How AI is being used to accelerate clinical trials

Nature. 2024 Mar 627:S2-S5. Hutson M.

Federated Learning is not a Cure-all for Data Ethics

Nature Machine Intelligence. 2024 Mar 6(4). Bak M, et al.

Quantitative Evaluation of Large Language Models to Streamline Radiology Report Impressions: A Multimodal Retrospective Analysis

Radiology. 2024 Mar 310(3):e231593. Doshi R, et al.

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