سال 18، شماره 2 - ( فروردین - اردیبهشت 1403 )
جلد 18 شماره 2 صفحات 79-66 |
برگشت به فهرست نسخه ها
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Hossainpour H, Mahmoudi H. Revolutionizing Microbial Infection Diagnosis: The Role of Artificial Intelligence. Iran J Med Microbiol 2024; 18 (2) :66-79
URL: http://ijmm.ir/article-1-2313-fa.html
URL: http://ijmm.ir/article-1-2313-fa.html
حسین پور هادی، محمودی حسن. انقلابی در تشخیص عفونت های میکروبی: نقش هوش مصنوعی. مجله میکروب شناسی پزشکی ایران. 1403; 18 (2) :66-79
1- گروه میکروب شناسی، دانشکده پزشکی، دانشگاه علوم پزشکی کرمانشاه، کرمانشاه، ایران و کمیته تحقیقات دانشجویی، دانشکده پزشکی، دانشگاه علوم پزشکی کرمانشاه، کرمانشاه، ایران
2- گروه میکروب شناسی، دانشکده پزشکی، دانشگاه علوم پزشکی همدان، همدان، ایران & دانشکده علوم پزشکی نهاوند، دانشگاه علوم پزشکی همدان، همدان، ایران ،Hassanmahmoudi24@gmail.com
2- گروه میکروب شناسی، دانشکده پزشکی، دانشگاه علوم پزشکی همدان، همدان، ایران & دانشکده علوم پزشکی نهاوند، دانشگاه علوم پزشکی همدان، همدان، ایران ،
چکیده: (1620 مشاهده)
هوش مصنوعی (AI)، به عنوان الگوریتم‌های کامپیوتری توصیف می‌شود که ویژگی‌های شناختی، مانند توانایی‌های یادگیری را نشان می‌دهند و اکنون در بسیاری از زمینه‌ها بر زندگی ما تأثیر می‌گذارند. در زمینه پزشکی با آنالیز تصاویر با پشتیبانی هوش مصنوعی در پاتولوژی، رادیولوژی و پوست نقش اصلی تشخیص را بر عهده گرفته است. در این مطالعه مقالات از پایگاه‌های اطلاعاتی Web of Science، Scopus، PubMed و Google Scholar بررسی شد. همچنین مقالاتی بررسی شدند که استفاده از هوش مصنوعی را برای تجزیه و تحلیل تصاویر برای تشخیص بیماری‌های عفونی توصیف می کردند. دیجیتالی شدن در مراقبت های بهداشتی در حال حاضر تأثیر عمیقی بر بیماران دارد. انتظار می رود که توسعه ای که آغاز شده است به شتاب خود ادامه دهد. یادگیری ماشینی اساساً نحوه تعامل ما با داده‌های مرتبط با سلامت، از جمله داده‌های میکروبیولوژی بالینی و بیماری‌های عفونی را تغییر می‌دهد. ما احتمالاً از اینترنت اشیا به اینترنت بدن با دستگاه‌ها و ارائه داده‌های بهداشتی دقیق حتی در زمان‌های سلامت بیمار منتقل خواهیم شد. هدف این مطالعه بررسی دیدگاه‌های کنونی در مورد تلاش‌ها برای به کارگیری روش‌های هوش مصنوعی و همچنین جستجوی روش‌های امیدوارکننده کارآمد برای تشخیص بیماری‌های عفونی است.
نوع مطالعه: مقاله مروری |
موضوع مقاله:
هوش مصنوعی
دریافت: 1402/11/9 | پذیرش: 1403/2/31 | انتشار الکترونیک: 1403/3/5
دریافت: 1402/11/9 | پذیرش: 1403/2/31 | انتشار الکترونیک: 1403/3/5
فهرست منابع
1. Ghaderzadeh M, Hosseini A, Asadi F, Abolghasemi H, Bashash D, Roshanpoor A. Automated detection model in classification of B-lymphoblast cells from normal B-lymphoid precursors in blood smear microscopic images based on the majority voting technique. Sci Program. 2022;2022:1-8. [DOI:10.1155/2022/4801671]
2. Ghaderzadeh M, Asadi F, Ramezan Ghorbani N, Almasi S, Taami T. Toward artificial intelligence (AI) applications in the determination of COVID-19 infection severity: considering AI as a disease control strategy in future pandemics. Iran J Blood Cancer. 2023;15(3):93-111. [DOI:10.61186/ijbc.15.3.93]
3. Fasihfar Z, Rokhsati H, Sadeghsalehi H, Ghaderzadeh M, Gheisari M. AI-driven malaria diagnosis: developing a robust model for accurate detection and classification of malaria parasites. Iran J Blood Cancer. 2023;15(3):112-24. [DOI:10.61186/ijbc.15.3.112]
4. Gheisari M, Ghaderzadeh M, Li H, Taami T, Fernández-Campusano C, Sadeghsalehi H, Afzaal Abbasi A. Mobile Apps for COVID-19 Detection and Diagnosis for Future Pandemic Control: Multidimensional Systematic Review. JMIR mHealth and uHealth. 2024;12:e44406. [DOI:10.2196/44406] [PMID] [PMCID]
5. Haenlein M, Kaplan A. Guest editorial to the special issue, A brief history of AI: On the past, present, and future of artificial intelligence. Calif Manage Rev. 2019;61(4):5-14. [DOI:10.1177/0008125619864925]
6. Wiens J, Shenoy ES. Machine learning for healthcare: on the verge of a major shift in healthcare epidemiology. Clin Infect Dis. 2018;66(1):149-53. [DOI:10.1093/cid/cix731] [PMID] [PMCID]
7. Naugler C, Church DL. Automation and artificial intelligence in the clinical laboratory. Crit Rev Clin Lab Sci. 2019;56(2):98-110. [DOI:10.1080/10408363.2018.1561640] [PMID]
8. Peiffer-Smadja N, Rawson TM, Ahmad R, Buchard A, Georgiou P, Lescure F-X, et al. Machine learning for clinical decision support in infectious diseases: a narrative review of current applications. CMI. 2020;26(5):584-95. [DOI:10.1016/j.cmi.2019.09.009] [PMID]
9. Aminoshariae A, Kulild J, Nagendrababu V. Artificial intelligence in endodontics: current applications and future directions. J Endod. 2021;47(9):1352-7. [DOI:10.1016/j.joen.2021.06.003] [PMID]
10. Fanelli U, Pappalardo M, Chinè V, Gismondi P, Neglia C, Argentiero A, et al. Role of artificial intelligence in fighting antimicrobial resistance in pediatrics. Antibiotics. 2020;9(11):767. [DOI:10.3390/antibiotics9110767] [PMID] [PMCID]
11. Meraj SS, Yaakob R, Azman A, Rum SNM, Nazri AA. Artificial intelligence in diagnosing tuberculosis: a review. Int J Adv Sci Eng Inf Technol. 2019;9(1):81-91. [DOI:10.18517/ijaseit.9.1.7567]
12. Silver D, Schrittwieser J, Simonyan K, Antonoglou I, Huang A, Guez A, et al. Mastering the game of go without human knowledge. Nature. 2017;550(7676):354-9. [DOI:10.1038/nature24270] [PMID]
13. Agrebi S, Larbi A. Use of artificial intelligence in infectious diseases. Artificial intelligence in precision health: Elsevier; 2020. p. 415-38. [DOI:10.1016/B978-0-12-817133-2.00018-5] [PMCID]
14. Desai SA, Mahitha G. Ayurnano: A Solution Towards Herbal Therapeutics Using Artificial Intelligence Approach. In Artificial Intelligence for Innovative Healthcare Informatics. Cham, Springer International Publishing: Berlin, Chermany. 2022. pp. 247-262. [DOI:10.1007/978-3-030-96569-3_12]
15. Luo W, Nguyen T, Nichols M, Tran T, Rana S, Gupta S, et al. Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset. PloS One. 2015;10(5):e0125602. [DOI:10.1371/journal.pone.0125602] [PMID] [PMCID]
16. Abdulrazak A, Ibrahim Bitar Z, Ayesh Al-Shamali A, Ahmed Mobasher L. Bacteriological study of diabetic foot infections. J Diabetes Complicat. 2005;19(3):138-41. [DOI:10.1016/j.jdiacomp.2004.06.001] [PMID]
17. Roeding F, Borner J, Kube M, Klages S, Reinhardt R, Burmester T. A 454 sequencing approach for large scale phylogenomic analysis of the common emperor scorpion (Pandinus imperator). Mol Phylogenetics Evol. 2009;53(3):826-34. [DOI:10.1016/j.ympev.2009.08.014] [PMID]
18. Colubri A, Silver T, Fradet T, Retzepi K, Fry B, Sabeti P. Transforming clinical data into actionable prognosis models: machine-learning framework and field-deployable app to predict outcome of Ebola patients. PLoS Negl Trop Dis. 2016;10(3):e0004549. [DOI:10.1371/journal.pntd.0004549] [PMID] [PMCID]
19. Park JY, Kricka LJ. One hundred years of clinical laboratory automation: 1967-2067. Clin Biochem. 2017;50(12):639-44. [DOI:10.1016/j.clinbiochem.2017.03.004] [PMID]
20. Delaney NF, Rojas Echenique JI, Marx CJ. Clarity: an open-source manager for laboratory automation. J Lab Autom. 2013;18(2):171-7. [DOI:10.1177/2211068212460237] [PMID] [PMCID]
21. Chapman T. Lab automation and robotics: Automation on the move. Nature. 2003;421(6923):661-3. [DOI:10.1038/421661a] [DOI:10.1038/421661b] [PMID]
22. Shaikh N, Shope TR, Hoberman A, Vigliotti A, Kurs-Lasky M, Martin JM. Association between uropathogen and pyuria. Pediatrics. 2016;138(1):e20160087. [DOI:10.1542/peds.2016-0087] [PMID]
23. Meraj M, Singh S, Johri P, Quasim MT, editors. An investigation on infectious disease patterns using Internet of Things (IoT). 2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE); 2020: IEEE. [DOI:10.1109/ICSTCEE49637.2020.9276922]
24. Smith KP, Wang H, Durant TJ, Mathison BA, Sharp SE, Kirby JE, et al. Applications of artificial intelligence in clinical microbiology diagnostic testing. Clin Microbiol Newsl. 2020;42(8):61-70. [DOI:10.1016/j.clinmicnews.2020.03.006]
25. Patel SJ, Chamberlain DB, Chamberlain JM. A machine learning approach to predicting need for hospitalization for pediatric asthma exacerbation at the time of emergency department triage. Acad Emerg Med.2018;25(12):1463-70. [DOI:10.1111/acem.13655] [PMID]
26. Miller MB, Atrzadeh F, Burnham C-AD, Cavalieri S, Dunn J, Jones S, et al. Clinical utility of advanced microbiology testing tools. J Clin Microbiol. 2019;57(9):10-11282. [DOI:10.1128/JCM.00495-19] [PMID] [PMCID]
27. Salazar BM, Balczewski EA, Ung CY, Zhu S. Neuroblastoma, a paradigm for big data science in pediatric oncology. Int J Mol Sci. 2016;18(1):37. [DOI:10.3390/ijms18010037] [PMID] [PMCID]
28. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69:S36-S40. [DOI:10.1016/j.metabol.2017.01.011] [PMID]
29. Abdi J, Al-Hindawi A, Ng T, Vizcaychipi MP. Scoping review on the use of socially assistive robot technology in elderly care. BMJ Open. 2018;8(2):e018815. [DOI:10.1136/bmjopen-2017-018815] [PMID] [PMCID]
30. Murphy M, Killen C, Burnham R, Sarvari F, Wu K, Brown N. Artificial intelligence accurately identifies total hip arthroplasty implants: a tool for revision surgery. Hip Int. 2022;32(6):766-70. [DOI:10.1177/1120700020987526] [PMID]
31. Samorani M, Blount LG. Machine learning and medical appointment scheduling: creating and perpetuating inequalities in access to health care. Am J Public Health. 2020;110(4):440-1. [DOI:10.2105/AJPH.2020.305570] [PMID] [PMCID]
32. Bhandari M, Zeffiro T, Reddiboina M. Artificial intelligence and robotic surgery: current perspective and future directions. Curr Opin Urol. 2020;30(1):48-54. [DOI:10.1097/MOU.0000000000000692] [PMID]
33. Lonner JH, Zangrilli J, Saini S. Emerging Robotic Technologies and Innovations for Hospital Process Improvement. In: Lonner, J. (eds) Robotics in Knee and Hip Arthroplasty. Springer, Cham: London, U.K.; 2019. pp. 233-43. [DOI:10.1007/978-3-030-16593-2_23]
34. Huang S, Yang J, Fong S, Zhao Q. Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges. Cancer Lett. 2020;471:61-71. [DOI:10.1016/j.canlet.2019.12.007] [PMID]
35. Prados-Privado M, García Villalón J, Martínez-Martínez CH, Ivorra C, Prados-Frutos JC. Dental caries diagnosis and detection using neural networks: a systematic review. J Clin Med. 2020;9(11):3579. [DOI:10.3390/jcm9113579] [PMID] [PMCID]
36. Orhan K, Bayrakdar I, Ezhov M, Kravtsov A, Özyürek T. Evaluation of artificial intelligence for detecting periapical pathosis on cone‐beam computed tomography scans. Int Endod J. 2020;53(5):680-9. [DOI:10.1111/iej.13265] [PMID]
37. Johari M, Esmaeili F, Andalib A, Garjani S, Saberkari H. Detection of vertical root fractures in intact and endodontically treated premolar teeth by designing a probabilistic neural network: an ex vivo study. Dentomaxillofac Radiol. 2017;46(2):20160107. [DOI:10.1259/dmfr.20160107] [PMID] [PMCID]
38. Crippa A, Salvatore C, Perego P, Forti S, Nobile M, Molteni M, et al. Use of machine learning to identify children with autism and their motor abnormalities. J Autism Dev Disord. 2015;45:2146-56. [DOI:10.1007/s10803-015-2379-8] [PMID]
39. Shu L-Q, Sun Y-K, Tan L-H, Shu Q, Chang AC. Application of artificial intelligence in pediatrics: past, present and future. World J Clin Pediatr. 2019;15(2):105-8. [DOI:10.1007/s12519-019-00255-1] [PMID]
40. Middleton B, Sittig D, Wright A. Clinical decision support: a 25 year retrospective and a 25 year vision. Yearb Med Inform. 2016;25(S 01):S103-S16. [DOI:10.15265/IYS-2016-s034] [PMID] [PMCID]
41. Chumbita M, Cillóniz C, Puerta-Alcalde P, Moreno-García E, Sanjuan G, Garcia-Pouton N, et al. Can artificial intelligence improve the management of pneumonia. J Clin Med. 2020;9(1):248. [DOI:10.3390/jcm9010248] [PMID] [PMCID]
42. Schmidt-Erfurth U, Bogunovic H, Sadeghipour A, Schlegl T, Langs G, Gerendas BS, et al. Machine learning to analyze the prognostic value of current imaging biomarkers in neovascular age-related macular degeneration. Ophthalmol Retina. 2018;2(1):24-30. [DOI:10.1016/j.oret.2017.03.015] [PMID]
43. Nicholson C. Artificial Intelligence (AI) vs. Machine Learning vs. Deep Learning. Skymind, https://skymind ai/wiki/ai-vs-machine-learning-vs-deep-learning, printed Oct. 2019;9:6.
44. Asada K, Kaneko S, Takasawa K, Machino H, Takahashi S, Shinkai N, et al. Integrated analysis of whole genome and epigenome data using machine learning technology: Toward the establishment of precision oncology. Front Oncol. 2021;11:66:6937. [DOI:10.3389/fonc.2021.666937] [PMID] [PMCID]
45. Yasutomi S, Arakaki T, Matsuoka R, Sakai A, Komatsu R, Shozu K, et al. Shadow estimation for ultrasound images using auto-encoding structures and synthetic shadows. Appl Sci. 2021;11(3):1127. [DOI:10.3390/app11031127]
46. Hamamoto R, Suvarna K, Yamada M, Kobayashi K, Shinkai N, Miyake M, et al. Application of artificial intelligence technology in oncology: Towards the establishment of precision medicine. Cancers. 2020;12(2):3532 [DOI:10.3390/cancers12123532] [PMID] [PMCID]
47. Leo S, Cherkaoui A, Renzi G, Schrenzel J. Mini review: clinical routine microbiology in the era of automation and digital health. Front Cell Infect. 2020;10:582028. [DOI:10.3389/fcimb.2020.582028] [PMID] [PMCID]
48. Ruppé E, Schrenzel J. Messages from the second international conference on clinical metagenomics (ICCMg2). Microbes Infect. 2018;20(4):222-7. [DOI:10.1016/j.micinf.2018.02.005] [PMID]
49. Kanaujia R, Biswal M, Angrup A, Ray P. Inhale, then exhale: start afresh to diagnose Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) by non-invasive face-mask sampling technique. Clin Microbiol Infect. 2020;26(12):1701-2. [DOI:10.1016/j.cmi.2020.06.034] [PMID] [PMCID]
50. Rafeeq H, Zia MA, Hussain A, Safdar A, Bilal M, Iqbal HM. Role of Streptokinase as a Thrombolytic Agent for Medical Applications. In Industrial Applications of Microbial Enzymes. 1st Ed. CRC Press: Florida, U.S.; 2022. p. 271-93. [DOI:10.1201/9781003202998-14]
51. Mahmoudi H, Moradi MH. The Progress and Future of Artificial Intelligence in Nursing Care: A Review. Open Public Health. 2024;17(1): E18749445304699. [DOI:10.2174/0118749445304699240416074458]
52. Tran NK, Howard T, Walsh R, Pepper J, Loegering J, Phinney B, et al. Novel application of automated machine learning with MALDI-TOF-MS for rapid high-throughput screening of COVID-19: A proof of concept. Sci Rep.2021;11(1):8219. [DOI:10.1038/s41598-021-87463-w] [PMID] [PMCID]
53. Bibin D, Nair MS, Punitha P. Malaria parasite detection from peripheral blood smear images using deep belief networks. IEEE Access. 2017;5:9099-108. [DOI:10.1109/ACCESS.2017.2705642]
54. Mathison BA, Kohan JL, Walker JF, Smith RB, Ardon O, Couturier MR. Detection of intestinal protozoa in trichrome-stained stool specimens by use of a deep convolutional neural network. J Clin Microbiol. 2020;58(6):10-128. [DOI:10.1128/JCM.02053-19] [PMID] [PMCID]
55. Tice AM, Farag HA. Machine learning in microbiology: Finding the signal in the noise. Clin Microbiol Newsl. 2019;41(14):121-7. [DOI:10.1016/j.clinmicnews.2019.06.004]
56. Haataja K, Gao X-Z, Toivanen P. Investigating drug peddling in Nigeria using a machine learning approach. In Intelligent Systems Design and Applicafions: 21st Internafional Conference on Intelligent Systems Design and Applicafions (ISDA 2021) Held During December 13; Vol. 15, No. 2021, Springer Nature: Berlin, Germany; 2022.
57. Frøssing L, Hartvig Lindkaer Jensen T, Østrup Nielsen J, Hvidtfeldt M, Silberbrandt A, Parker D, et al. Automated cell differential count in sputum is feasible and comparable to manual cell count in identifying eosinophilia. J Asthma. 2022;59(3):552-60. [DOI:10.1080/02770903.2020.1868498] [PMID]
58. Savardi M, Ferrari A, Signoroni A. Automatic hemolysis identification on aligned dual-lighting images of cultured blood agar plates. Comput Methods Programs Biomed. 2018;156:13-24. [DOI:10.1016/j.cmpb.2017.12.017] [PMID]
59. Smith KP, Kang AD, Kirby JE. Automated interpretation of blood culture gram stains by use of a deep convolutional neural network. J Clin Microbiol. 2018;56(3):10-128. [DOI:10.1128/JCM.01521-17] [PMID] [PMCID]
60. Henry KE, Hager DN, Pronovost PJ, Saria S. A targeted real-time early warning score (TREWScore) for septic shock. Sci Transl Med. 2015;7(299):299ra122. [DOI:10.1126/scitranslmed.aab3719] [PMID]
61. Kaushal V, Gupta R. Role of Artificial Intelligence in Diagnosis of Infectious Diseases. In Biomedical Translational Research: Technologies for Improving Healthcare. Springer Nature Singapore: Singapore; 2022. pp. 115-133. [DOI:10.1007/978-981-16-4345-3_8]
62. Mala R, Sood S, Kapil A, Gupta S, Singh N. Comparison of Amsel's criteria with low and high Nugent's scores for the diagnosis of bacterial vaginosis. Indian J Sex Transm Dis AIDS. 2022;43(1):56-8. [DOI:10.4103/ijstd.ijstd_67_21] [PMID] [PMCID]
63. Fanelli U, Chiné V, Pappalardo M, Gismondi P, Esposito S. Improving the quality of hospital antibiotic use: Impact on multidrug-resistant bacterial infections in children. Front pharmacol. 2020;11:745. [DOI:10.3389/fphar.2020.00745] [PMID] [PMCID]
64. Smith KP, Kirby JE. Image analysis and artificial intelligence in infectious disease diagnostics. Clin Microbiol Infect. 2020;26(10):1318-23. [DOI:10.1016/j.cmi.2020.03.012] [PMID] [PMCID]
65. Cho SI, Han B, Hur K, Mun JH. Perceptions and attitudes of medical students regarding artificial intelligence in dermatology. J Eur Acad Dermatol Venereol. 2021;35(1):e72-e73. [DOI:10.1111/jdv.16812]
66. Madeleine M, Patel N, Plasmeijer E, Engels E, Bouwes Bavinck J, Toland A, et al. Epidemiology of keratinocyte carcinomas after organ transplantation. Br J Dermatol. 2017;177(5):1208-16. [DOI:10.1111/bjd.15931] [PMID]
67. Gomolin A, Netchiporouk E, Gniadecki R, Litvinov IV. Artificial intelligence applications in dermatology: where do we stand?. Front Med. 2020;7:100. [DOI:10.3389/fmed.2020.00100] [PMID] [PMCID]
68. Scull WR, Perkins MA, Carrier JW, Barber M. Community college institutional researchers' knowledge, experience, and perceptions of machine learning. Community Coll J Res Pract. 2023;47(5):354-68. [DOI:10.1080/10668926.2022.2043202]
69. Lim SS, Ohn J, Mun J-H. Diagnosis of onychomycosis: from conventional techniques and dermoscopy to artificial intelligence. Front Med. 2021;8:637216. [DOI:10.3389/fmed.2021.637216] [PMID] [PMCID]
70. Picek L, Šulc M, Matas J, Heilmann-Clausen J, Jeppesen TS, Lind E. Automatic fungi recognition: deep learning meets mycology. Sensors. 2022;22(2):633. [DOI:10.3390/s22020633] [PMID] [PMCID]
71. Tahir MW, Zaidi NA, Rao AA, Blank R, Vellekoop MJ, Lang W. A fungus spores dataset and a convolutional neural network based approach for fungus detection. IEEE Trans Nanobioscience. 2018;17(3):281-90. [DOI:10.1109/TNB.2018.2839585] [PMID]
72. Van De Vooren J, Polder G, Van der Heijden G. Identification of mushroom cultivars using image analysis. Trans ASABE. 1992;35(1):347-50. [DOI:10.13031/2013.28610]
73. Zieliński B, Sroka-Oleksiak A, Rymarczyk D, Piekarczyk A, Brzychczy-Włoch M. Deep learning approach to describe and classify fungi microscopic images. PloS One. 2020;15(6):e0234806. [DOI:10.1371/journal.pone.0234806] [PMID] [PMCID]
74. Fricker M, Boddy L, Bebber D. Network organisation of mycelial fungi. In Biology of the fungal cell. Springer Berlin Heidelberg: Berlin, Germany. 2007. pp. 309-330. [DOI:10.1007/978-3-540-70618-2_13]
75. Zhang J, Lu S, Wang X, Du X, Ni G, Liu J, et al. Automatic identification of fungi in microscopic leucorrhea images. J Opt Soc Am A. 2017;34(9):1484-9. [DOI:10.1364/JOSAA.34.001484] [PMID]
76. Santosh K, Ghosh S, GhoshRoy D. Deep learning for COVID-19 screening using chest x-rays in 2020: A systematic review. Int J Pattern Recognit Artif Intell. 2022;36(05):2252010. [DOI:10.1142/S0218001422520103]
77. Arora N, Banerjee AK, Narasu ML. The role of artificial intelligence in tackling COVID-19. Future Virol. 2020;15(11):717-24. [DOI:10.2217/fvl-2020-0130] [PMCID]
78. Gong H, Wang M, Zhang H, Elahe MF, Jin M. An explainable AI approach for the rapid diagnosis of COVID-19 using ensemble learning algorithms. Front Public Health. 2022;10:874455. [DOI:10.3389/fpubh.2022.874455] [PMID] [PMCID]
79. Ghosal A, Gupta N, Nandi E, Somolu H. Biomedical Data Driven COVID-19 Prediction Using Machine Learning Approach. In Artificial Intelligence and Machine Learning Methods in COVID-19 and Related Health Diseases. Cham, Springer International Publishing: Berlin, Germany. 2022. pp. 123-138. [DOI:10.1007/978-3-031-04597-4_6]
80. Huffman A, Ong E, Hur J, D'Mello A, Tettelin H, He Y. COVID-19 vaccine design using reverse and structural vaccinology, ontology-based literature mining and machine learning. Brief Bioinform. 2022;23(4):bbac190. [DOI:10.1093/bib/bbac190] [PMID] [PMCID]
81. David L, Brata AM, Mogosan C, Pop C, Czako Z, Muresan L, et al. Artificial Intelligence and Antibiotic Discovery. Antibiotics. 2021;10(11):1376. [DOI:10.3390/antibiotics10111376] [PMID] [PMCID]
82. Kumar N, Kharkwal N, Kohli R, Choudhary S. Ethical aspects and future of artificial intelligence. In 2016 International Conference on Innovation and Challenges in Cyber Security (ICICCS-INBUSH). 2016 Feb 3 (pp. 111-114). IEEE: Greater Noida, India. 2016. pp. 111-114. [DOI:10.1109/ICICCS.2016.7542339]
83. Briganti G, Le Moine O. Artificial intelligence in medicine: today and tomorrow. Front Med. 2020;7:509744. [DOI:10.3389/fmed.2020.00027] [PMID] [PMCID]
ارسال پیام به نویسنده مسئول
| بازنشر اطلاعات | |
 |
این مقاله تحت شرایط Creative Commons Attribution-NonCommercial 4.0 International License قابل بازنشر است. |
