Raoof, S., et al., Interpretation of plain chest roentgenogram. Chest, 2012. 141(2): p. 545–558.

Article  PubMed  Google Scholar 

Çallı, E., et al., Deep learning for chest X-ray analysis: A survey. 2021. 72: p. 102125.

Litjens, G., et al., A survey on deep learning in medical image analysis. 2017. 42: p. 60–88.

Liu, X., et al., A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit Health, 2019. 1(6): p. e271-e297.

PubMed  Google Scholar 

Aggarwal, R., et al., Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis. NPJ Digit Med, 2021. 4(1): p. 65.

Article  PubMed  PubMed Central  Google Scholar 

Rajpurkar, P., et al., AI in health and medicine. Nat Med, 2022. 28(1): p. 31–38.

Article  PubMed  CAS  Google Scholar 

Willemink, M.J., et al., Preparing medical imaging data for machine learning. 2020. 295(1): p. 4–15.

Joskowicz, L., et al., Inter-observer variability of manual contour delineation of structures in CT. 2019. 29: p. 1391–1399.

Lutnick, B., et al., An integrated iterative annotation technique for easing neural network training in medical image analysis. 2019. 1(2): p. 112–119.

Esteva, A., et al., A guide to deep learning in healthcare. 2019. 25(1): p. 24–29.

Kim, H.E., et al., Transfer learning for medical image classification: a literature review. 2022. 22(1): p. 69.

Chen, T., et al. A simple framework for contrastive learning of visual representations. in International conference on machine learning. 2020. PMLR.

He, K., et al. Momentum contrast for unsupervised visual representation learning. in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020.

He, K., et al. Masked autoencoders are scalable vision learners. in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022.

Radford, A., et al. Learning transferable visual models from natural language supervision. in International conference on machine learning. 2021. PMLR.

Zhang, Y., et al. Contrastive learning of medical visual representations from paired images and text. in Machine Learning for Healthcare Conference. 2022. PMLR.

Zhang, S., et al., A Multimodal Biomedical Foundation Model Trained from Fifteen Million Image–Text Pairs. Nejm Ai, 2025. 2(1).

Wang, Z., et al., MedCLIP: Contrastive Learning from Unpaired Medical Images and Text. Proc Conf Empir Methods Nat Lang Process, 2022. 2022: p. 3876–3887.

Benjamens, S., P. Dhunnoo, and B.J.N.d.m. Meskó, The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. 2020. 3(1): p. 118.

Irvin, J., et al. Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison. in Proceedings of the AAAI conference on artificial intelligence. 2019.

Wang, X., et al. Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.

Huang, G., et al. Densely connected convolutional networks. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.

Wang, H., et al., Scientific discovery in the age of artificial intelligence. 2023. 620(7972): p. 47–60.

Manzari, O.N., et al., MedViT: a robust vision transformer for generalized medical image classification. 2023. 157: p. 106791.

Dosovitskiy, A., et al., An image is worth 16x16 words: Transformers for image recognition at scale. 2020.

Sellergren, A.B., et al., Simplified transfer learning for chest radiography models using less data. 2022. 305(2): p. 454–465.

Bannur, S., et al. Learning to exploit temporal structure for biomedical vision-language processing. in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023.

Sowrirajan, H., et al. Moco pretraining improves representation and transferability of chest x-ray models. in Medical Imaging with Deep Learning. 2021. PMLR.

Lauzier, P.T. and B.J.W. Chow, Artificial Intelligence Detection of Left Ventricular Systolic Dysfunction Using Chest X-Rays: Prospective Validation, Please. The Canadian journal of cardiology, 2022. 38(6): p. 720–722.

PubMed  Google Scholar 

Yu, A.C., B. Mohajer, and J.J.R.A.I. Eng, External validation of deep learning algorithms for radiologic diagnosis: a systematic review. 2022. 4(3): p. e210064.

van Ginneken, B.J.R., Deep learning for triage of chest radiographs: should every institution train its own system?Radiology, 2019. Radiological Society of North America: Radiology. p. 545–546.

Dunnmon, J.A., et al., Assessment of convolutional neural networks for automated classification of chest radiographs. 2019. 290(2): p. 537–544.

Tiu, E., et al., Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning. 2022. 6(12): p. 1399–1406.

Vaid, A., et al., A foundational vision transformer improves diagnostic performance for electrocardiograms. 2023. 6(1): p. 108.

Kumar, A., et al. Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution. in International Conference on Learning Representations. 2022.

Sellergren, A.B., et al., Simplified transfer learning for chest radiography models using less data. Radiology, 2022. 305(2): p. 454–465.

Article  PubMed  Google Scholar 

Rieke, N., et al., The future of digital health with federated learning. 2020. 3(1): p. 1–7.

Liu, W.-T., et al., A deep-learning algorithm-enhanced system integrating electrocardiograms and chest X-rays for diagnosing aortic dissection. 2022. 38(2): p. 160–168.

Rajpurkar, P., et al., Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. 2018. 15(11): p. e1002686.

Bluemke, D.A., et al., Assessing radiology research on artificial intelligence: a brief guide for authors, reviewers, and readers—from the radiology editorial board.Radiology, 2020. Radiological Society of North America: Radiology. p. 487–489.

Hsiang, C., et al., Detection of Left Ventricular Systolic Dysfunction Using an Artificial Intelligence-Enabled Chest X-Ray. The Canadian journal of cardiology, 2022. 38(6): p. 763–773.

PubMed  Google Scholar 

Seah, J.C., et al., Chest radiographs in congestive heart failure: visualizing neural network learning. 2019. 290(2): p. 514–522.

Pyrros, A., et al., Opportunistic detection of type 2 diabetes using deep learning from frontal chest radiographs. Nat Commun, 2023. 14(1): p. 4039.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Berland, L.L., et al., Managing incidental findings on abdominal CT: white paper of the ACR incidental findings committee. 2010. 7(10): p. 754–773.