Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

Tytuł pozycji:

Artificial intelligence in genetic services delivery: Utopia or apocalypse?

Tytuł:
Artificial intelligence in genetic services delivery: Utopia or apocalypse?
Autorzy:
Kearney E; Mainstream Genomics, San Mateo, CA, USA.
Wojcik A; Minneapolis, MN, USA.
Babu D; St. Albert, AB, Canada.
Źródło:
Journal of genetic counseling [J Genet Couns] 2020 Feb; Vol. 29 (1), pp. 8-17. Date of Electronic Publication: 2019 Nov 20.
Typ publikacji:
Journal Article
Język:
English
Imprint Name(s):
Publication: 2019- : [Hoboken, NJ] : Wiley
Original Publication: New York, N.Y. : Human Sciences Press, c1992-
MeSH Terms:
Artificial Intelligence*
Genetic Services*
Genomics ; Health Personnel ; Humans
References:
23andMe. (2019). 23andMe offers new genetic report on Type 2 diabetes [product blog post]. Retrieved from https://blog.23andme.com/health-traits/type-2-diabetes/.
Abramoff, M. D., Lou, Y., Erginay, A., Clarida, W., Amelon, R., Folk, J. C., … Niemeijer, M. (2016). Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Investigative Ophthalmology & Visual Science., 57(13), 5200-5206. https://doi.org/10.1167/iovs.16-19964.
Addie, S., Alper, J., & Beachy, S. H. (Rapporteurs). (2018). Understanding Disparities in Access to Genomic Medicine: Proceedings of a Workshop. Retrieved from https://www.nap.edu/read/25277/chapter/1.
Alexander, E. K., Kennedy, G. C., Baloch, Z. W., Cibas, E. S., Chudova, D., Diggans, J., … Haugen, B. R. (2012). Preoperative diagnosis of benign thyroid nodules with indeterminate cytology. New England Journal of Medicine., 367(8), 705-715. https://doi.org/10.1056/NEJMoa1203208.
Applications of Fabric Enterprise. (2019). [product webpage]. Retrieved from https://fabricgenomics.com/products/applications/.
Centers for Medicare & Medicaid Services. (2019). Clinical laboratory improvement amendments (CLIA): LDT and CLIA FAQs [frequently asked questions]. Retrieved from https://www.cms.gov/Regulations-and-Guidance/Legislation/CLIA/Downloads/LDT-and-CLIA_FAQs.pdf.
Clark, M. M., Hildreth, A., Batalov, S., Ding, Y., Chowdhury, S., & Watkins, K., …Kingsmore, S. F. (2019). Science translational medicine. 11. https://doi.org/10.1126/scitranslmed.aat6177.
Clear Genetics Inc. (2019). Clear Genetics [company webpage]. Retrieved from https://www.cleargenetics.com/.
Computer History Museum. (2019). John McCarthy [biography]. Retrieved from https://www.computerhistory.org/fellowawards/hall/john-mccarthy/.
Dowd, M. (2017). Elon Musk's Billion-Dollar Crusade to Stop the A.I. Apocalypse [article]. Retrieved from https://www.vanityfair.com/news/2017/03/elon-musk-billion-dollar-crusade-to-stop-ai-space-x.
Dudbridge, F. (2013). Power and predictive accuracy of polygenic risk scores. Public Library of Sciences Genetics, 9(3), e1003348. https://doi.org/10.1371/journal.pgen.1003348.
Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (2017). Delivering cognitive behavioral therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): A randomized controlled trial. Journal of Medical Internet Research Mental Health., 4(2), e19. https://doi.org/10.2196/mental.7785.
Fried, J. (2019). “Reactions from the Field: Artificial Intelligence” [multi-speaker oral presentation at Health Information Management Systems Society 2019 conference]. Retrieved from https://365.himss.org/sites/himss365/files/365/handouts/552573543/handout-265.pdf?_ga=2.179000301.879597446.1561439179-1068918842.1561439179.
Glanz, J., Kaplan, T., & Nicas, J. (2019). In ethiopia crash, faulty sensor on boeing 737 max is suspected [article]. Retrieved from https://www.nytimes.com/2019/03/29/business/boeing-737-max-crash.html.
Gordon, E. S., Babu, D., & Laney, D. A. (2018). The future is now: Technology's impact on the practice of genetic counseling. American Journal of Medical Genetics C: Seminars in Medical Genetics, 178(1), 15-23. https://doi.org/10.1002/ajmg.c.31599.
Gurovich, Y., Hanani, Y., Bar, O., Nadav, G., Fleischer, N., Gelbman, D., … Gripp, K. W. (2019). Identifying facial phenotypes of genetic disorders using deep learning. Nature Medicine, 24, 60-64. https://doi.org/10.1038/s41591-018-0279-0.
Heath, N. (2018). What is deep learning? Everything you need to know [article]. Retrieved from https://www.zdnet.com/google-amp/article/what-is-deep-learning-everything-you-need-to-know/.
Helix. (2019). DNA testing for prostate cancer risk: Here's what you need to know [product blog post]. Retrieved from https://blog.helix.com/dna-testing-for-prostate-cancer/.
Hernandez, D., & Greenwald, T. (2018). IBM Has a Watson Dilemma. The Wall Street Journal. Retrieved from https://www.wsj.com/articles/ibm-bet-billions-that-watson-could-improve-cancer-treatment-it-hasnt-worked-1533961147.
Ho, D. S. W., Schierding, W., Wake, M., Saffery, R., & O'Sullivan, J. (2019). Machine learning SNP based prediction for precision medicine. Frontiers in Genetics., 10, 267. https://doi.org/10.3389/fgene.2019.00267.
IBM. (2019). IBM watson for genomics [product webpage]. Retrieved from https://www.ibm.com/us-en/marketplace/watson-for-genomics.
IBM Watson Health. (2018). Understanding AI's fundamental value to healthcare [white paper]. Retrieved from https://go.merge.com/2018-Q4-OC-WP-AI-for-Radiology.html.
Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., & Chouvarda, I. (2017). Machine learning and data mining methods in diabetes research. Computational and Structural Biotechnology Journal., 15, 104-116. https://doi.org/10.1016/j.csbj.2016.12.005.
Kretzschmar, K., Tyroll, H., Pavarini, G., Manzini, A., & Singh, I.; NeuroX Young People's Advisory Group. (2019). Can your phone be your therapist? Young people's ethical perspectives on the use of fully automated conversational agents (chatbots) in mental health support. Biomedical Informatics Insights, 11, 117822261982908. https://doi.org/10.1177/1178222619829083.
Launchbury, J. [DARPAtv]. (2017). A DARPA Perspective on Artificial Intelligence [Video file]. Retrieved from https://www.youtube.com/watch?v=-O01G3tSYpU&feature=youtu.be.
Libbrecht, M. W., & Noble, W. S. (2015). Machine learning applications in genetics and genomics. Nature Reviews Genetics, 16(6), 321-332. https://doi.org/10.1038/nrg3920.
Life Whisperer. (2019). Artificial Intelligence Non-Invasively Detects Down Syndrome in Embryos [product research description]. Retrieved from https://www.lifewhisperer.co/artificial-intelligence-non-invasively-detects-down-syndrome-in-embryos/.
Marcus, G. (2018). Deep learning: A critical appraisal. ArXiv, Retrieved from https://arxiv.org/pdf/1801.00631.pdf.
National Institutes of Health. (2019). All of Us Research Program [study webpage]. Retrieved from https://allofus.nih.gov/.
Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future - big data, machine learning, and clinical medicine. New England Journal of Medicine, 375(13), 1216-1219. https://doi.org/10.1056/NEJMp1606181.
OptraHEALTH. (2019). GeneFAX [product webpage]. Retrieved from https://www.optrahealth.com/genefax.
Paré, G., Mao, S., & Deng, W. Q. (2017). A machine-learning heuristic to improve gene score prediction of polygenic traits. Scientific Reports, 7(1), 12665. https://doi.org/10.1038/s41598-017-13056-1.
Patel, K. N., Angell, T. E., Babiarz, J., Barth, N. M., Blevins, T., & Duh, Q. Y., … Ladenson, P. W. (2018). Performance of a genomic sequencing classifier for the preoperative diagnosis of cytologically indeterminate thyroid nodules. Journal of the American Medical Journal Surgery, 153(9), 817-824. https://doi.org/10.1001/jamasurg.2018.1153.
Patwardhan, V. (2019). “Smarter Care: The AI Driven Patient Experience” [multi-speaker oral presentation at Health Information Management Systems Society 2019 conference]. Retrieved from https://365.himss.org/sites/himss365/files/365/handouts/552670821/handout-PTS07.pdf?_ga=2.18070384.879597446.1561439179-1068918842.1561439179.
Rajpurkar, P., Irvin, J., Ball, R. L., Zhu, K., Yang, B., Mehta, H., … Lungren, M. P. (2018). Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. Public Library of Science Medicine, 15(11), e1002686. https://doi.org/10.1371/journal.pmed.1002686.
Reddy, S., Fox, J., & Purohit, M. P. (2018). Artificial intelligence-enabled healthcare delivery. Journal of the Royal Society of Medicine, 112(1), 22-28. https://doi.org/10.1177/0141076818815510.
Scassellati, B., Boccanfuso, L., Huang, C.-M., Mademtzi, M., Qin, M., Salomons, N., … Shic, F. (2018). Improving social skills in children with ASD using a long-term, in-home social robot. Science Robotics, 3(21), eaat7544. https://doi.org/10.1126/scirobotics.aat7544.
Stephens, Z. D., Lee, S. Y., Faghri, F., Campbell, R. H., Zhai, C., Efron, M. J., … Robinson, G. E.. (2015). Big data: Astronomical or genomical? Public Library of Science Biology, 13(7), e1002195. https://doi.org/10.1371/journal/pbio.1002195.
Szymczak, S., Biernacka, J. M., Cordell, H. J., González-Recio, O., König, I. R., Zhang, H., …Sun, Y. V. (2009). Machine learning in genome-wide association studies. Genetic Epidemiology, 33(S5), 1-7. https://doi.org/10.1002/gepi.20473.
The Office of the National Coordinator for Health Information Technology (ONC). (2019). Notice of proposed rulemaking to improve the interoperability of health information [official notice webpage]. Retrieved from https://www.healthit.gov/topic/laws-regulation-and-policy/notice-proposed-rulemaking-improve-interoperability-health.
ThinkGenetic Inc. (2019). ThinkGeneticSymptomMatcher [product webpage]. Retrieved from https://www.thinkgenetic.com/SymptomMatcher/.
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124-1131. https://doi.org/10.1126/science.185.4157.1124.
U.S. Department of Health & Human Services. (2019). HHS Proposes New Rules to Improve the Interoperability of Electronic Health Information [press release]. Retrieved from https://www.hhs.gov/about/news/2019/02/11/hhs-proposes-new-rules-improve-interoperability-electronic-health-information.html.
U.S. Food & Drug Administration. (2017a). Digital health innovation action plan [program description]. Retrieved from https://www.fda.gov/media/106331/download.
U.S. Food & Drug Administration. (2017b). Statement from FDA Commissioner Scott Gottlieb, M.D., on advancing new digital health policies to encourage innovation, bring efficiency and modernization to regulation [press release]. Retrieved from https://www.fda.gov/news-events/press-announcements/statement-fda-commissioner-scott-gottlieb-md-advancing-new-digital-health-policies-encourage.
U.S. Food & Drug Administration. (2018a). Laboratory developed tests [definition and description]. Retrieved from https://www.fda.gov/medical-devices/vitro-diagnostics/laboratory-developed-tests.
U.S. Food & Drug Administration. (2018b). FDA permits marketing of artificial intelligence-based device to detect certain diabetes-related eye problems [press release]. Retrieved from https://www.fda.gov/news-events/press-announcements/fda-permits-marketing-artificial-intelligence-based-device-detect-certain-diabetes-related-eye.
U.S. Food & Drug Administration. (2019a). Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) [discussion paper and request for feedback]. Retrieved from https://www.fda.gov/media/122535/download.
U.S. Food & Drug Administration. (2019b). Software precertification program: 2019 Test Plan [program description]. Retrieved from https://www.fda.gov/media/119723/download.
Uhlmann, W. R., Schuette, J. L., & Yashar, B. (Eds.) (2009). A Guide to Genetic Counseling (2nd ed.). Hoboken, NJ: Wiley-Blackwell.
Warner Bros. Entertainment Inc. (1968). 2001: A Space Odyssey [motion picture]. Retrieved from https://www.warnerbros.com/movies/2001-space-odyssey/.
Weir, W. (2018). Robots help children with autism improve social skills [article]. Retrieved from https://news.yale.edu/2018/08/22/robots-help-children-autism-improve-social-skills.
Zheutlin, A. B., Chekroud, A. M., Polimanti, R., Gelernter, J., Sabb, F. W., Bilder, R. M., … Cannon, T. D. (2018). Multivariate pattern analysis of genotype-phenotype relationships in schizophrenia. Schizophrenia Bulletin, 44(5), 1045-1052. https://doi.org/10.1093/schbul/sby005.
Contributed Indexing:
Keywords: access; artificial intelligence; genetic counseling; genetic counselors; genetic services; genomics; machine learning; medicine; service delivery models
Entry Date(s):
Date Created: 20191122 Date Completed: 20201123 Latest Revision: 20201123
Update Code:
20240104
DOI:
10.1002/jgc4.1192
PMID:
31749317
Czasopismo naukowe
Artificial intelligence (AI) technologies have a long history, with increasing presence and potential in society and medicine. Much of the medical literature is highly optimistic about AI and machine learning, but fears also exist that healthcare professionals will be replaced by machines. AI remains mysterious for many practitioners, so this paper aims to unwind both hype and fear related to the technology for genetics professionals. After an historical introduction to AI in understandable and practical terms, we review its limitations. Building upon this foundation, we discuss current AI applications in medicine, including genomics and genetic counseling, offering grounded ideas about the impact and role of AI in genetic counseling and delivery of genetic services. Since AI is already being used in genomics today, now is the time to fundamentally understand what it is, how it is being used, what its limitations are, and how it will continue to be integrated into genetics as we look ahead.
(© 2019 National Society of Genetic Counselors.)

Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies