References
Alsentzer, Emily, John R Murphy, Willie Boag, Wei-Hung Weng, Di Jin,
Tristan Naumann, and Matthew BA McDermott. 2019. “Publicly
Available Clinical BERT Embeddings.” arXiv
Preprint arXiv:1904.03323.
Devlin, Jacob, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018.
“BERT: Pre-Training of Deep Bidirectional Transformers for
Language Understanding.” arXiv Preprint
arXiv:1810.04805.
Dwork, Cynthia, and Aaron Roth. 2014. “The Algorithmic Foundations
of Differential Privacy.” Foundations and Trends in
Theoretical Computer Science 9 (3–4): 211–407.
Goodfellow, Ian, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David
Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014.
“Generative Adversarial Nets.” Advances in Neural
Information Processing Systems 27.
He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016.
“Deep Residual Learning for Image Recognition,” 770–78.
Hochreiter, Sepp, and Jürgen Schmidhuber. 1997. “Long Short-Term
Memory.” Neural Computation 9 (8): 1735–80.
Jumper, John, Richard Evans, Alexander Pritzel, Tim Green, Michael
Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, et al. 2021.
“Highly Accurate Protein Structure Prediction with
AlphaFold.” Nature 596 (7873): 583–89.
Kingma, Diederik P, and Max Welling. 2013. “Auto-Encoding
Variational Bayes.” arXiv Preprint
arXiv:1312.6114.
Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E Hinton. 2012.
“ImageNet Classification with Deep Convolutional Neural
Networks.” Advances in Neural Information Processing
Systems 25.
LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. 2015. “Deep
Learning.” Nature 521 (7553): 436–44.
Lee, Jinhyuk, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan
Ho So, and Jaewoo Kang. 2020. “BioBERT: A Pre-Trained
Biomedical Language Representation Model for Biomedical Text
Mining.” Bioinformatics 36 (4): 1234–40.
Lundberg, Scott M, and Su-In Lee. 2017. “A Unified Approach to
Interpreting Model Predictions.” Advances in Neural
Information Processing Systems 30.
Mitchell, Margaret, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy
Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and
Timnit Gebru. 2019. “Model Cards for Model Reporting.”
Proceedings of the Conference on Fairness, Accountability, and
Transparency, 220–29.
Muehlematter, Urs J, Paola Daniore, and Kerstin N Vokinger. 2021.
“Approval of Artificial Intelligence and Machine Learning-Based
Medical Devices in the USA and Europe
(2015–20): A Comparative Analysis.” The Lancet Digital
Health 3 (3): e195–203.
Nori, Harsha, Nicholas King, Scott Mayer McKinney, Dean Carignan, and
Eric Horvitz. 2023. “Capabilities of GPT-4 on Medical
Challenge Problems.” arXiv Preprint arXiv:2303.13375.
Obermeyer, Ziad, Brian Powers, Christine Vogeli, and Sendhil
Mullainathan. 2019. “Dissecting Racial Bias in an Algorithm Used
to Manage the Health of Populations.” Science 366
(6464): 447–53.
Rajpurkar, Pranav, Jeremy Irvin, Kaylie Zhu, Brandon Yang, Hershel
Mehta, Tony Duan, Daisy Ding, et al. 2017. “ChexNet:
Radiologist-Level Pneumonia Detection on Chest x-Rays with Deep
Learning.” arXiv Preprint arXiv:1711.05225.
Rieke, Nicola, Jonny Hancox, Wenqi Li, Fausto Milletari, Holger R Roth,
Shadi Albarqouni, Spyridon Bakas, et al. 2020. “The Future of
Digital Health with Federated Learning.” NPJ Digital
Medicine 3 (1): 1–7.
Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. 2015. “U-Net:
Convolutional Networks for Biomedical Image Segmentation.”
International Conference on Medical Image Computing and
Computer-Assisted Intervention, 234–41.
Selvaraju, Ramprasaath R, Michael Cogswell, Abhishek Das, Ramakrishna
Vedantam, Devi Parikh, and Dhruv Batra. 2017.
“Grad-CAM: Visual Explanations from Deep Networks via
Gradient-Based Localization.” Proceedings of the IEEE
International Conference on Computer Vision, 618–26.
Singhal, Karan, Shekoofeh Azizi, Tao Tu, S Sara Mahdavi, Jason Wei,
Hyung Won Chung, Nathan Scales, et al. 2023. “Large Language
Models Encode Clinical Knowledge.” Nature 620 (7972):
172–80.
Singhal, Karan, Tao Tu, Juraj Gottweis, Rory Sayres, Ellery Wulczyn, Le
Hou, Kevin Clark, et al. 2023. “Towards Expert-Level Medical
Question Answering with Large Language Models.” arXiv
Preprint arXiv:2305.09617.
Steyerberg, Ewout W, Andrew J Vickers, Nancy R Cook, Thomas Gerds,
Mithat Gonen, Nancy Obuchowski, Michael J Pencina, and Michael W Kattan.
2010. “Assessing the Performance of Prediction Models: A Framework
for Traditional and Novel Measures.” Epidemiology 21
(1): 128–38.
Topol, Eric J. 2019. “High-Performance Medicine: The Convergence
of Human and Artificial Intelligence.” Nature Medicine
25 (1): 44–56.
Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion
Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017.
“Attention Is All You Need.” Advances in Neural
Information Processing Systems 30.
Vickers, Andrew J, and Elena B Elkin. 2006. “Decision Curve
Analysis: A Novel Method for Evaluating Prediction Models.”
Medical Decision Making 26 (6): 565–74.