Альманах клинической медицины. 2019; 47: 630-633
Применение искусственного интеллекта для анализа медицинских данных
https://doi.org/10.18786/2072-0505-2019-47-071Аннотация
Список литературы
1. Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol. 2019;28(2):73–81. doi: 10.1080/13645706.2019.1575882.
2. Amisha, Malik P, Pathania M, Rathaur VK. Overview of artificial intelligence in medicine. J Family Med Prim Care. 2019;8(7):2328–31. doi: 10.4103/jfmpc.jfmpc_440_19.
3. Choy G, Khalilzadeh O, Michalski M, Do S, Samir AE, Pianykh OS, Geis JR, Pandharipande PV, Brink JA, Dreyer KJ. Current applications and future impact of machine learning in radiology. Radiology. 2018;288(2):318–28. doi: 10.1148/radiol.2018171820.
4. Panzarasa S, Quaglini S, Micieli G, Marcheselli S, Pessina M, Pernice C, Cavallini A, Stefanelli M. Improving compliance to guidelines through workflow technology: implementation and results in a stroke unit. Stud Health Technol Inform. 2007;129(Pt 2):834–9.
5. Forghani R, Chatterjee A, Reinhold C, PérezLara A, Romero-Sanchez G, Ueno Y, Bayat M, Alexander JWM, Kadi L, Chankowsky J, Seuntjens J, Forghani B. Head and neck squamous cell carcinoma: prediction of cervical lymph node metastasis by dual-energy CT texture analysis with machine learning. Eur Radiol. 2019;29(11):6172–81. doi: 10.1007/s00330-019-06159-y.
6. Aeffner F, Zarella MD, Buchbinder N, Bui MM, Goodman MR, Hartman DJ, Lujan GM, Molani MA, Parwani AV, Lillard K, Turner OC, Vemuri VNP, Yuil-Valdes AG, Bowman D. Introduction to digital image analysis in whole-slide imaging: a white paper from the digital pathology association. J Pathol Inform. 2019;10:9. doi: 10.4103/jpi.jpi_82_18.
7. Roach L. Artificial Intelligence. EyeNet Magazine. 2017;11:77–83.
8. Padhy SK, Takkar B, Chawla R, Kumar A. Artificial intelligence in diabetic retinopathy: A natural step to the future. Indian J Ophthalmol. 2019;67(7):1004–9. doi: 10.4103/ijo.IJO_1989_18.
9. Hsieh JC, Hsu MW. A cloud computing based 12-lead ECG telemedicine service. BMC Med Inform Decis Mak. 2012;12:77. doi: 10.1186/1472-6947-12-77.
10. Goumopoulos C. A high precision, wireless temperature measurement system for pervasive computing applications. Sensors (Basel). 2018;18(10):3445. doi: 10.3390/s18103445.
11. Yetisen AK, Martinez-Hurtado JL, Ünal B, Khademhosseini A, Butt H. Wearables in Medicine. Adv Mater. 2018;30(33):e1706910. doi: 10.1002/adma.201706910.
12. Benke K, Benke G. Artificial intelligence and big data in public health. Int J Environ Res Public Health. 2018;15(12):2796. doi: 10.3390/ijerph15122796.
13. Chen PH. Essential elements of natural language processing: what the radiologist should know. Acad Radiol. 2019;S1076–6332(19): 30417–9. doi: 10.1016/j.acra.2019.08.010.
14. Garg R, Oh E, Naidech A, Kording K, Prabhakaran S. Automating ischemic stroke subtype classification using machine learning and natural language processing. J Stroke Cerebrovasc Dis. 2019;28(7):2045–51. doi: 10.1016/j.jstrokecerebrovasdis.2019.02.004.
15. Powell ME, Rodriguez Cancio M, Young D, Nock W, Abdelmessih B, Zeller A, Perez Morales I, Zhang P, Garrett CG, Schmidt D, White J, Gelbard A. Decoding phonation with artificial intelligence (DeP AI): Proof of concept. Laryngoscope Investig Otolaryngol. 2019;4(3):328–34. doi: 10.1002/lio2.259.
16. Wu H, Soraghan J, Lowit A, Di Caterina G. Convolutional neural networks for pathological voice detection. Conf Proc IEEE Eng Med Biol Soc. 2018;2018:1–4. doi: 10.1109/EMBC.2018.8513222.
17. Slavich GM, Taylor S, Picard RW. Stress measurement using speech: Recent advancements, validation issues, and ethical and privacy considerations. Stress. 2019;22(4):408–13. doi: 10.1080/10253890.2019.1584180.
18. König A, Linz N, Zeghari R, Klinge X, Tröger J, Alexandersson J, Robert P. Detecting apathy in older adults with cognitive disorders using automatic speech analysis. J Alzheimers Dis. 2019;69(4):1183–93. doi: 10.3233/JAD-181033.
19. Sun O, Chen J, Magrabi F. Using voice-activated conversational interfaces for reporting patient safety incidents: A technical feasibility and pilot usability study. Stud Health Technol Inform. 2018;252:139–44.
20. Хау Дж. Краудсорсинг. Коллективный разум как инструмент развития бизнеса. М.: Альпина Паблишер; 2012. 288 с.
21. BGU researchers first to show how hackers can dupe radiologists and A.I. software by manipulating lung cancer scans. Ben-Gurion University of the Negev. 04.09.2019 [Internet]. Available from: https://in.bgu.ac.il/en/pages/news/scans_hacking.aspx.
Almanac of Clinical Medicine. 2019; 47: 630-633
Application of artificial intelligence in medical data analysis
https://doi.org/10.18786/2072-0505-2019-47-071Abstract
References
1. Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol. 2019;28(2):73–81. doi: 10.1080/13645706.2019.1575882.
2. Amisha, Malik P, Pathania M, Rathaur VK. Overview of artificial intelligence in medicine. J Family Med Prim Care. 2019;8(7):2328–31. doi: 10.4103/jfmpc.jfmpc_440_19.
3. Choy G, Khalilzadeh O, Michalski M, Do S, Samir AE, Pianykh OS, Geis JR, Pandharipande PV, Brink JA, Dreyer KJ. Current applications and future impact of machine learning in radiology. Radiology. 2018;288(2):318–28. doi: 10.1148/radiol.2018171820.
4. Panzarasa S, Quaglini S, Micieli G, Marcheselli S, Pessina M, Pernice C, Cavallini A, Stefanelli M. Improving compliance to guidelines through workflow technology: implementation and results in a stroke unit. Stud Health Technol Inform. 2007;129(Pt 2):834–9.
5. Forghani R, Chatterjee A, Reinhold C, PérezLara A, Romero-Sanchez G, Ueno Y, Bayat M, Alexander JWM, Kadi L, Chankowsky J, Seuntjens J, Forghani B. Head and neck squamous cell carcinoma: prediction of cervical lymph node metastasis by dual-energy CT texture analysis with machine learning. Eur Radiol. 2019;29(11):6172–81. doi: 10.1007/s00330-019-06159-y.
6. Aeffner F, Zarella MD, Buchbinder N, Bui MM, Goodman MR, Hartman DJ, Lujan GM, Molani MA, Parwani AV, Lillard K, Turner OC, Vemuri VNP, Yuil-Valdes AG, Bowman D. Introduction to digital image analysis in whole-slide imaging: a white paper from the digital pathology association. J Pathol Inform. 2019;10:9. doi: 10.4103/jpi.jpi_82_18.
7. Roach L. Artificial Intelligence. EyeNet Magazine. 2017;11:77–83.
8. Padhy SK, Takkar B, Chawla R, Kumar A. Artificial intelligence in diabetic retinopathy: A natural step to the future. Indian J Ophthalmol. 2019;67(7):1004–9. doi: 10.4103/ijo.IJO_1989_18.
9. Hsieh JC, Hsu MW. A cloud computing based 12-lead ECG telemedicine service. BMC Med Inform Decis Mak. 2012;12:77. doi: 10.1186/1472-6947-12-77.
10. Goumopoulos C. A high precision, wireless temperature measurement system for pervasive computing applications. Sensors (Basel). 2018;18(10):3445. doi: 10.3390/s18103445.
11. Yetisen AK, Martinez-Hurtado JL, Ünal B, Khademhosseini A, Butt H. Wearables in Medicine. Adv Mater. 2018;30(33):e1706910. doi: 10.1002/adma.201706910.
12. Benke K, Benke G. Artificial intelligence and big data in public health. Int J Environ Res Public Health. 2018;15(12):2796. doi: 10.3390/ijerph15122796.
13. Chen PH. Essential elements of natural language processing: what the radiologist should know. Acad Radiol. 2019;S1076–6332(19): 30417–9. doi: 10.1016/j.acra.2019.08.010.
14. Garg R, Oh E, Naidech A, Kording K, Prabhakaran S. Automating ischemic stroke subtype classification using machine learning and natural language processing. J Stroke Cerebrovasc Dis. 2019;28(7):2045–51. doi: 10.1016/j.jstrokecerebrovasdis.2019.02.004.
15. Powell ME, Rodriguez Cancio M, Young D, Nock W, Abdelmessih B, Zeller A, Perez Morales I, Zhang P, Garrett CG, Schmidt D, White J, Gelbard A. Decoding phonation with artificial intelligence (DeP AI): Proof of concept. Laryngoscope Investig Otolaryngol. 2019;4(3):328–34. doi: 10.1002/lio2.259.
16. Wu H, Soraghan J, Lowit A, Di Caterina G. Convolutional neural networks for pathological voice detection. Conf Proc IEEE Eng Med Biol Soc. 2018;2018:1–4. doi: 10.1109/EMBC.2018.8513222.
17. Slavich GM, Taylor S, Picard RW. Stress measurement using speech: Recent advancements, validation issues, and ethical and privacy considerations. Stress. 2019;22(4):408–13. doi: 10.1080/10253890.2019.1584180.
18. König A, Linz N, Zeghari R, Klinge X, Tröger J, Alexandersson J, Robert P. Detecting apathy in older adults with cognitive disorders using automatic speech analysis. J Alzheimers Dis. 2019;69(4):1183–93. doi: 10.3233/JAD-181033.
19. Sun O, Chen J, Magrabi F. Using voice-activated conversational interfaces for reporting patient safety incidents: A technical feasibility and pilot usability study. Stud Health Technol Inform. 2018;252:139–44.
20. Khau Dzh. Kraudsorsing. Kollektivnyi razum kak instrument razvitiya biznesa. M.: Al'pina Pablisher; 2012. 288 s.
21. BGU researchers first to show how hackers can dupe radiologists and A.I. software by manipulating lung cancer scans. Ben-Gurion University of the Negev. 04.09.2019 [Internet]. Available from: https://in.bgu.ac.il/en/pages/news/scans_hacking.aspx.
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