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Gaps and Risks of AI in the Life Sciences

rachel.fast.ai · published 9 September 2024

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https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9122957/
ncbi.nlm.nih.gov
https://arxiv.org/abs/1901.02547
arxiv.org
https://github.com/MimiOnuoha/missing-datasets
github.com
https://dl.acm.org/doi/fullHtml/10.1145/3465416.3483305
dl.acm.org
https://research.google/pubs/everyone-wants-to-do-the-model-work-not-the-data-work-data-cascades-in-high-stakes-ai/
research.google
https://arxiv.org/abs/1803.09010
arxiv.org
https://ojs.aaai.org/index.php/AAAI/article/view/26682
ojs.aaai.org
https://arxiv.org/abs/2305.18654
arxiv.org
https://www.nobelprize.org/prizes/medicine/2002/brenner/interview/
nobelprize.org
https://www.nature.com/articles/d41586-021-02480-z
nature.com
https://slideslive.com/38917533/lessons-learned-from-helping-200000-nonml-experts-use-ml
slideslive.com
https://www.sciencedirect.com/science/article/pii/S2666389921001847
sciencedirect.com
https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11314/1131416/Hazards-of-data-leakage-in-machine-learning--a-study/10.1117/12.2549313.short
spiedigitallibrary.org
https://dl.acm.org/doi/10.1145/3351095.3372829
dl.acm.org
https://blog.neurips.cc/2021/04/07/announcing-the-neurips-2021-datasets-and-benchmarks-track/
blog.neurips.cc
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