Andrew Beam

Artificial Intelligence · 2018–2025

A researcher in the Science corpus: 14 papers; trusts 28 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 186 citations into it; #30,138 of 35,345 by trust here, #7,858 by citations
• Radiology, Nuclear Medicine and Imaging — 78 citations into it
• Health Informatics — 62 citations into it
• Computer Vision and Pattern Recognition — 61 citations into it
• Safety, Robustness & Interpretability — 37 citations into it
• Medical & Health AI — 30 citations into it
• Probabilistic & Bayesian Methods — 23 citations into it
• Evaluation & Benchmarks — 16 citations into it

Whom Andrew Beam trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Yarin Gal
5. Alex Krizhevsky
6. Yann LeCun
7. Thomas G. Dietterich
8. Alex Graves
9. Radford M. Neal
10. Ilya Sutskever

Who trusts Andrew Beam overall

1. Alvin Rajkomar
2. Evangelia Christodoulou
3. Marzyeh Ghassemi
4. S. Lo Piano
5. Meghavi Rana
6. Michaela Hardt
7. Gary S. Collins
8. Jie Ma
9. Zhaoyi Xu
10. Jenni A. M. Sidey-Gibbons

Papers from Andrew Beam's perspective

Ordered by how strongly the researchers Andrew Beam trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Challenges to the Reproducibility of Machine Learning Models in Health Care (2020) — 338 citations
• Adversarial attacks on medical machine learning (2019) — 963 citations
• Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension (2020) — 1,116 citations
• Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension (2020) — 1,148 citations
• Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence (2021) — 676 citations
• TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods (2024) — 2,500 citations
• PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods (2025) — 435 citations
• Illuminating protein space with a programmable generative model (2022) — 605 citations
• The false hope of current approaches to explainable artificial intelligence in health care. (2021) — 1,184 citations
• Second opinion needed: communicating uncertainty in medical machine learning (2021) — 463 citations
• Artificial intelligence in healthcare (2018) — 1,715 citations
• A Review of Challenges and Opportunities in Machine Learning for Health. (2018) — 398 citations
• Opportunities in Machine Learning for Healthcare (2018) — 112 citations
• Big Data and Machine Learning in Health Care. (2018) — 1,854 citations