110. Strojové učenie v medicíne: Nádejné riešenia a obrovské výzvy (I. časť)
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Show Notes
V ďalšej epizóde Pravidelnej dávky si s Mirom povieme niečo o strojovom učení, jeho výhodách a o jeho využití v medicíne. Je to prvá zo série zameranej na strojové učenie a umelú inteligenciu v medicíne.
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Referencie:
[1] Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., … Greene, C. S. (2018). Opportunities and obstacles for deep learning in biology and medicine. Journal of the Royal Society Interface, 15(141). https://doi.org/10.1098/rsif.2017.0387
[2] Komorowski, M., Celi, L. A., Badawi, O., Gordon, A. C., & Faisal, A. A. (2018). The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care. Nature Medicine, 24(11), 1716–1720. https://doi.org/10.1038/s41591-018-0213-5
[3] Rigby, M. J. (2019, February 1). Ethical dimensions of using artificial intelligence in health care. AMA Journal of Ethics. American Medical Association. https://doi.org/10.1001/amajethics.2019.121
[4] Yu, K. H., & Kohane, I. S. (2019, March 1). Framing the challenges of artificial intelligence in medicine. BMJ Quality and Safety. BMJ Publishing Group. https://doi.org/10.1136/bmjqs-2018-008551
[5] Poplin, R., Varadarajan, A. V., Blumer, K., Liu, Y., McConnell, M. V., Corrado, G. S., … Webster, D. R. (2018). Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nature Biomedical Engineering, 2(3), 158–164. https://doi.org/10.1038/s41551-018-0195-0
[6] Day, N., Hemmaplardh, A., Thurman, R. E., Stamatoyannopoulos, J. A., & Noble, W. S. (2007). Unsupervised segmentation of continuous genomic data. Bioinformatics, 23(11), 1424–1426. https://doi.org/10.1093/bioinformatics/btm096
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