Искусственный интеллект: как работает и критерии оценки

1. Amisha, Malik P, Pathania M, Rathaur VK. Overview of artificial intelligence in medicine. J Family Med Prim Care. 2019;8(7):2328-31.
2. Dzobo K, Adotey S, Thomford NE, Dzobo W. Integrating artificial and human intelligence: a partnership for responsible innovation in biomedical engineering and medicine. Omics. 2020;24(5):247-63.
3. Stanford University. Available at: http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html. Accessed: 23.07.2021.
4. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-44.
5. Goodfellow IJ, Shlens J, Azegedy C. Explaining and harnessing adversarial examples. arXiv:1412.6572
6. Chockley K, Emanuel E. The end of radiology? Three threats to the future practice of radiology. J Am Coll Radiol. 2016;13(12 Pt. A):1415-20.
7. Li Z, Keel S, Liu C. An automated grading system for detection of vision-threatening referable diabetic retinopathy on the basis of color fundus photographs. Diabetes Care. 2018;41(12):2509-16.
8. Ting DSW, Cheung CY, Lim G, et al. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images fr om multiethnic populations with diabetes. JAMA. 2017;318(22):2211-23.
9. Li Z, He Y, Keel S, et al. Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs. Ophthalmology. 2018;125(8):1199-206.
10. Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology. 2017;284(2):574-82.
11. Halicek M, Lu G, Little JV, et al. Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging. J Biomed Opt. 2017;22(6):60503.
12. Rajpurkar P, Irvin J, Ball RL, et al. Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Med. 2018;15(11):e1002686.
13. Haenssle HA, Fink C, Toberer F, et al. Man against machine reloaded: performance of a market-approved convolutional neural network in classifying a broad spectrum of skin lesions in comparison with 96 dermatologists working under less artificial conditions. Ann Oncol. 2020;31(1):137-43.
14. Tschandl P, Codella N, Akay BN, et al. Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study. Lancet Oncol. 2019;20(7):938-47.
15. Abràmoff MD, Lavin PT, Birch M, et al. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med. 2018;1:39.
16. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56.
17. Pinto Dos Santos D, Giese D, Brodehl S, et al. Medical students’ attitude towards artificial intelligence: a multicentre survey. Eur Radiol. 2019;29(4):1640-6.
18. Sit C, Srinivasan R, Amlani A, et al. Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey. Insights Imaging. 2020;11(1):14.
19. Waymel Q, Badr S, Demondion X, et al. Impact of the rise of artificial intelligence in radiology: What do radiologists think? Diagn Interv Imaging. 2019;100(6):327-36.
20. Van Hoek J, Huber A, Leichtle A, et al. A survey on the future of radiology among radiologists, medical students and surgeons: Students and surgeons tend to be more skeptical about artificial intelligence and radiologists may fear that other disciplines take over. Eur J Radiol. 2019;121:108742.
21. Houghton LC, Howland RE, McDonald JA. Mobilizing breast cancer prevention research through smartphone apps: a systematic review of the literature. Front Public Health. 2019;7:298.
22. Sarwar S, Dent A, Faust K, et al. Physician perspectives on integration of artificial intelligence into diagnostic pathology. NPJ Digit Med. 2019;2:28.
23. Doraiswamy PM, Blease C, Bodner K. Artificial intelligence and the future of psychiatry: Insights fr om a global physician survey. Artif Intell Med. 2020;102:101753.
24. Blease C, Kaptchuk TJ, Bernstein MH, et al. Artificial intelligence and the future of primary care: exploratory qualitative study of UK general practitioners’ views. J Med Internet Res. 2019;21(3):e12802.
25. Oh S, Kim JH, Choi SW, et al. Physician confidence in artificial intelligence: an online mobile survey. J Med Internet Res. 2019;21(3):e12422.
26. Gong B, Nugent JP, Guest W, et al. Influence of artificial intelligence on Canadian medical students’ preference for radiology specialty: a national survey study. Acad Radiol. 2019;26(4):566-77.
27. Collado-Mesa F, Alvarez E, Arheart K. The role of artificial intelligence in diagnostic radiology: a survey at a single radiology residency training program. J Am Coll Radiol. 2018;15(12):1753-7.
28. Pakdemirli E. Artificial intelligence in radiology: friend or foe? Where are we now and wh ere are we heading? Acta Radiol Open. 2019;8(2):2058460119830222.
29. Statista. Number of smartphone users worldwide from 2016 to 2021 (in billions). Available at: https://www.statista.com/statistics/330695/number-of-smartphone-users-worldwide. Accessed: 03.08.2019.
30. IQVIA. Evidence and Impact on Human Health and the Healthcare System. 2017. 
31. Kong FW, Horsham C, Ngoo A, et al. Review of smartphone mobile applications for skin cancer detection: what are the changes in availability, functionality, and costs to users over time? Int J Dermatol. 2021;60(3):289-308.
32. Ngoo A, Finnane A, McMeniman E, et al. Fighting melanoma with smartphones: a snapshot of wh ere we are a decade after app stores opened their doors. Int J Med Inform. 2018;118:99-112.
33. Kassianos AP, Emery JD, Murchie P, Walter FM. Smartphone applications for melanoma detection by community, patient and generalist clinician users: a review. Br J Dermatol. 2015;172(6):1507-18.
34. Börve A, Terstappen K, Sandberg C, Paoli J. Mobile teledermoscopy-there’s an app for that! Dermatol Pract Concept. 2013;3(2):41-8.
35. Petrie T, Samatham R, Goodyear SM, et al. MoleMapper: an application for crowdsourcing mole images to advance melanoma early-detection research. Semin Cutan Med Surg. 2019;38(1):E49-56.
36. Wadhawan T, Situ N, Lancaster K, et al. SkinScan©: A portable library for melanoma detection on handheld devices. Proc IEEE Int Symp Biomed Imaging. 2011;2011:133-6.
37. Thissen M, Udrea A, Hacking M, et al. mHealth app for risk assessment of pigmented and nonpigmented skin lesions – a study on sensitivity and specificity in detecting malignancy. Telemed J E Health. 2017;23(12):948-54.
38. Phillips M, Marsden H, Jaffe W, et al. Assessment of accuracy of an artificial intelligence algorithm to detect melanoma in images of skin lesions. JAMA Netw Open. 2019;2(10):e1913436.
39. Steeb T, Wessely A, Mastnik S, et al. Patient attitudes and their awareness towards skin cancer-related apps: cross-sectional survey. JMIR Mhealth Uhealth. 2019;7(7):e13844.
40. Giunti G, Giunta DH, Guisado-Fernandez E, et al. A biopsy of breast cancer mobile applications: state of the practice review. Int J Med Inform. 2018;110:1-9.
41. Bender JL, Yue RY, To MJ, et al. A lot of action, but not in the right direction: systematic review and content analysis of smartphone applications for the prevention, detection, and management of cancer. J Med Internet Res. 2013;15(12):e287.
42. Bini SA. Artificial intelligence, machine learning, deep learning, and cognitive computing: what do these terms mean and how will they impact health care? J Arthroplasty. 2018;33(8):2358-61.
43. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-8.
44. Ana FA, Loreto MS, José LM, et al. Mobile applications in oncology: a systematic review of health science databases. Int J Med Inform. 2020;133:104001.
45. Kessel KA, Vogel MM, Kessel C, et al. Mobile health in oncology: a patient survey about app-assisted cancer care. JMIR Mhealth Uhealth. 2017;5(6):e81.
46. Abbott LM, Smith SD. Smartphone apps for skin cancer diagnosis: implications for patients and practitioners. Australas J Dermatol. 2018;59(3):168-70.
47. IMDRF/SaMDWG/N10:2013 Software as a medical device: key definitions. 18.12.2013. Available at: http://www.imdrf.org/docs/imdrf/final/technical/imdrf-tech-131209-samd-key-definitions-140901.pdf. Accessed: 23.07.2021.
48. IMDRF/SaMDWG/N12:2014 Software as a medical device: possible framework for risk categorization and corresponding considerations. 14.09.2014. Available at: http://www.imdrf.org/docs/imdrf/final/technical/imdrf-tech-140918-samd-framework-risk-categorization.... Accessed: 23.07.2021.
49. IMDRF/SaMDWG/N23:2015 Software as a medical device: application of quality management system. 02.10.2015. Available at: http://www.imdrf.org/docs/imdrf/final/technical/imdrf-tech-151002-samd-qms.pdf. Accessed: 23.07.2021.
50. IMDRF/SaMDWG/N41:2017 Software as a medical device: clinical evaluation. 21.09.2017. Available at: http://www.imdrf.org/docs/imdrf/final/technical/imdrf-tech-131209-samd-key-definitions-140901.pdf. Accessed: 23.07.2021.
51. Номенклатурная классификация медицинских изделий по видам. Режим доступа: http://www.roszdravnadzor.ru/services/mi_reesetr/documents/46242. Ссылка активна на 23.07.2021 [Nomenklaturnaia klassifikatsiia meditsinskikh izdelii po vidam. Available at: http://www.roszdravnadzor.ru/services/mi_reesetr/documents/46242. Accessed: 23.07.2021 (in Russian)].
52. Kim DW, Jang HY, Kim KW, et al. Design characteristics of studies reporting the performance of artificial intelligence algorithms for diagnostic analysis of medical images: results from recently published papers. Korean J Radiol. 2019;20(3):405-10.

________________________________________________

1. Amisha, Malik P, Pathania M, Rathaur VK. Overview of artificial intelligence in medicine. J Family Med Prim Care. 2019;8(7):2328-31.
2. Dzobo K, Adotey S, Thomford NE, Dzobo W. Integrating artificial and human intelligence: a partnership for responsible innovation in biomedical engineering and medicine. Omics. 2020;24(5):247-63.
3. Stanford University. Available at: http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html. Accessed: 23.07.2021.
4. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-44.
5. Goodfellow IJ, Shlens J, Azegedy C. Explaining and harnessing adversarial examples. arXiv:1412.6572
6. Chockley K, Emanuel E. The end of radiology? Three threats to the future practice of radiology. J Am Coll Radiol. 2016;13(12 Pt. A):1415-20.
7. Li Z, Keel S, Liu C. An automated grading system for detection of vision-threatening referable diabetic retinopathy on the basis of color fundus photographs. Diabetes Care. 2018;41(12):2509-16.
8. Ting DSW, Cheung CY, Lim G, et al. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images fr om multiethnic populations with diabetes. JAMA. 2017;318(22):2211-23.
9. Li Z, He Y, Keel S, et al. Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs. Ophthalmology. 2018;125(8):1199-206.
10. Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology. 2017;284(2):574-82.
11. Halicek M, Lu G, Little JV, et al. Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging. J Biomed Opt. 2017;22(6):60503.
12. Rajpurkar P, Irvin J, Ball RL, et al. Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Med. 2018;15(11):e1002686.
13. Haenssle HA, Fink C, Toberer F, et al. Man against machine reloaded: performance of a market-approved convolutional neural network in classifying a broad spectrum of skin lesions in comparison with 96 dermatologists working under less artificial conditions. Ann Oncol. 2020;31(1):137-43.
14. Tschandl P, Codella N, Akay BN, et al. Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study. Lancet Oncol. 2019;20(7):938-47.
15. Abràmoff MD, Lavin PT, Birch M, et al. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med. 2018;1:39.
16. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56.
17. Pinto Dos Santos D, Giese D, Brodehl S, et al. Medical students’ attitude towards artificial intelligence: a multicentre survey. Eur Radiol. 2019;29(4):1640-6.
18. Sit C, Srinivasan R, Amlani A, et al. Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey. Insights Imaging. 2020;11(1):14.
19. Waymel Q, Badr S, Demondion X, et al. Impact of the rise of artificial intelligence in radiology: What do radiologists think? Diagn Interv Imaging. 2019;100(6):327-36.
20. Van Hoek J, Huber A, Leichtle A, et al. A survey on the future of radiology among radiologists, medical students and surgeons: Students and surgeons tend to be more skeptical about artificial intelligence and radiologists may fear that other disciplines take over. Eur J Radiol. 2019;121:108742.
21. Houghton LC, Howland RE, McDonald JA. Mobilizing breast cancer prevention research through smartphone apps: a systematic review of the literature. Front Public Health. 2019;7:298.
22. Sarwar S, Dent A, Faust K, et al. Physician perspectives on integration of artificial intelligence into diagnostic pathology. NPJ Digit Med. 2019;2:28.
23. Doraiswamy PM, Blease C, Bodner K. Artificial intelligence and the future of psychiatry: Insights fr om a global physician survey. Artif Intell Med. 2020;102:101753.
24. Blease C, Kaptchuk TJ, Bernstein MH, et al. Artificial intelligence and the future of primary care: exploratory qualitative study of UK general practitioners’ views. J Med Internet Res. 2019;21(3):e12802.
25. Oh S, Kim JH, Choi SW, et al. Physician confidence in artificial intelligence: an online mobile survey. J Med Internet Res. 2019;21(3):e12422.
26. Gong B, Nugent JP, Guest W, et al. Influence of artificial intelligence on Canadian medical students’ preference for radiology specialty: a national survey study. Acad Radiol. 2019;26(4):566-77.
27. Collado-Mesa F, Alvarez E, Arheart K. The role of artificial intelligence in diagnostic radiology: a survey at a single radiology residency training program. J Am Coll Radiol. 2018;15(12):1753-7.
28. Pakdemirli E. Artificial intelligence in radiology: friend or foe? Where are we now and wh ere are we heading? Acta Radiol Open. 2019;8(2):2058460119830222.
29. Statista. Number of smartphone users worldwide from 2016 to 2021 (in billions). Available at: https://www.statista.com/statistics/330695/number-of-smartphone-users-worldwide. Accessed: 03.08.2019.
30. IQVIA. Evidence and Impact on Human Health and the Healthcare System. 2017. 
31. Kong FW, Horsham C, Ngoo A, et al. Review of smartphone mobile applications for skin cancer detection: what are the changes in availability, functionality, and costs to users over time? Int J Dermatol. 2021;60(3):289-308.
32. Ngoo A, Finnane A, McMeniman E, et al. Fighting melanoma with smartphones: a snapshot of wh ere we are a decade after app stores opened their doors. Int J Med Inform. 2018;118:99-112.
33. Kassianos AP, Emery JD, Murchie P, Walter FM. Smartphone applications for melanoma detection by community, patient and generalist clinician users: a review. Br J Dermatol. 2015;172(6):1507-18.
34. Börve A, Terstappen K, Sandberg C, Paoli J. Mobile teledermoscopy-there’s an app for that! Dermatol Pract Concept. 2013;3(2):41-8.
35. Petrie T, Samatham R, Goodyear SM, et al. MoleMapper: an application for crowdsourcing mole images to advance melanoma early-detection research. Semin Cutan Med Surg. 2019;38(1):E49-56.
36. Wadhawan T, Situ N, Lancaster K, et al. SkinScan©: A portable library for melanoma detection on handheld devices. Proc IEEE Int Symp Biomed Imaging. 2011;2011:133-6.
37. Thissen M, Udrea A, Hacking M, et al. mHealth app for risk assessment of pigmented and nonpigmented skin lesions – a study on sensitivity and specificity in detecting malignancy. Telemed J E Health. 2017;23(12):948-54.
38. Phillips M, Marsden H, Jaffe W, et al. Assessment of accuracy of an artificial intelligence algorithm to detect melanoma in images of skin lesions. JAMA Netw Open. 2019;2(10):e1913436.
39. Steeb T, Wessely A, Mastnik S, et al. Patient attitudes and their awareness towards skin cancer-related apps: cross-sectional survey. JMIR Mhealth Uhealth. 2019;7(7):e13844.
40. Giunti G, Giunta DH, Guisado-Fernandez E, et al. A biopsy of breast cancer mobile applications: state of the practice review. Int J Med Inform. 2018;110:1-9.
41. Bender JL, Yue RY, To MJ, et al. A lot of action, but not in the right direction: systematic review and content analysis of smartphone applications for the prevention, detection, and management of cancer. J Med Internet Res. 2013;15(12):e287.
42. Bini SA. Artificial intelligence, machine learning, deep learning, and cognitive computing: what do these terms mean and how will they impact health care? J Arthroplasty. 2018;33(8):2358-61.
43. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-8.
44. Ana FA, Loreto MS, José LM, et al. Mobile applications in oncology: a systematic review of health science databases. Int J Med Inform. 2020;133:104001.
45. Kessel KA, Vogel MM, Kessel C, et al. Mobile health in oncology: a patient survey about app-assisted cancer care. JMIR Mhealth Uhealth. 2017;5(6):e81.
46. Abbott LM, Smith SD. Smartphone apps for skin cancer diagnosis: implications for patients and practitioners. Australas J Dermatol. 2018;59(3):168-70.
47. IMDRF/SaMDWG/N10:2013 Software as a medical device: key definitions. 18.12.2013. Available at: http://www.imdrf.org/docs/imdrf/final/technical/imdrf-tech-131209-samd-key-definitions-140901.pdf. Accessed: 23.07.2021.
48. IMDRF/SaMDWG/N12:2014 Software as a medical device: possible framework for risk categorization and corresponding considerations. 14.09.2014. Available at: http://www.imdrf.org/docs/imdrf/final/technical/imdrf-tech-140918-samd-framework-risk-categorization.... Accessed: 23.07.2021.
49. IMDRF/SaMDWG/N23:2015 Software as a medical device: application of quality management system. 02.10.2015. Available at: http://www.imdrf.org/docs/imdrf/final/technical/imdrf-tech-151002-samd-qms.pdf. Accessed: 23.07.2021.
50. IMDRF/SaMDWG/N41:2017 Software as a medical device: clinical evaluation. 21.09.2017. Available at: http://www.imdrf.org/docs/imdrf/final/technical/imdrf-tech-131209-samd-key-definitions-140901.pdf. Accessed: 23.07.2021.
51. Nomenklaturnaia klassifikatsiia meditsinskikh izdelii po vidam. Available at: http://www.roszdravnadzor.ru/services/mi_reesetr/documents/46242. Accessed: 23.07.2021 (in Russian).
52. Kim DW, Jang HY, Kim KW, et al. Design characteristics of studies reporting the performance of artificial intelligence algorithms for diagnostic analysis of medical images: results from recently published papers. Korean J Radiol. 2019;20(3):405-10.

И.Л. Шливко1, О.Е. Гаранина*1, И.А. Клеменова1, К.А. Ускова1, А.М. Миронычева1, В.И. Дардык2, В.Н. Ласьков3

1 ФГБОУ ВО «Приволжский исследовательский медицинский университет» Минздрава России, Нижний Новгород, Россия;
2 ООО «AIMED», Москва, Россия;
3 Карлов университет, Прага, Чехия
*oksanachekalkina@yandex.ru

________________________________________________

Irena L. Shlivko1, Oxana Ye. Garanina*1, Irina A. Klemenova1, Kseniia A. Uskova1, Anna M. Mironycheva1, Veniamin I. Dardyk2, Viktor N. Laskov3

1 Privolzhsky Research Medical University, Nizhny Novgorod, Russia;
2 "AIMED" LLC, Moscow, Russia;
3 Charles University, Prague, Czech Republic
*oksanachekalkina@yandex.ru