Page 17 - AIH-1-2

Artificial Intelligence in Health AI in the battle against COVID-19

9. Silva C, Saraee D. Literature review on epidemiological 20. Shortliffe EH. MYCIN: A Rule-Based Computer Program
modelling, spatial modelling and artificial intelligence for for Advising Physicians Regarding Antimicrobial Therapy
COVID-19. J Adv Med Med Res. 2021;33:8-21. Selection. PhD Thesis. Stanford University; 1974.
doi: 10.9734/jammr/2021/v33i530841 doi: 10.1145/1408800.1408906
10. Vicari R, Komendatova N. Systematic meta-analysis of 21. Shortliffe EH, Davis R, Axline SG, Buchanan BG,
research on AI tools to deal with misinformation on social Green CC, Cohen SN. Computer-based consultations in clinical
media during natural and anthropogenic hazards and therapeutics: Explanation and rule acquisition capabilities of
disasters. Humanit Soc Sci Commun. 2023;10:1-14. the MYCIN system. Comput Biomed Res. 1975;8:303-320.
doi: 10.1057/s41599-023-01838-0 doi: 10.1016/0010-4809(75)90009-9
11. Al Sulais E, Mosli M, AlAmeel T. The psychological impact 22. Miller RA, Pople HE Jr., Myers JD. Internist-I, an
of COVID-19 pandemic on physicians in Saudi Arabia: experimental computer-based diagnostic consultant for
A cross-sectional study. Saudi J Gastroenterol. 2020;26:249. general internal medicine. In: Computer-assisted Medical
doi: 10.4103/sjg.SJG_173_20 Decision Making. Berlin: Springer; 1985. p. 139-158.
12. Poon YS, Lin YP, Griffiths P, Yong KK, Seah B, Liaw SY. doi: 10.1007/978-1-4612-5108-8_8
A global overview of healthcare workers’ turnover intention 23. Bradley AP. Machine Learning for Medical Diagnostics:
amid COVID-19 pandemic: A systematic review with future Techniques for Feature Extraction, Classification, and
directions. Hum Resour Health. 2022;20:70. Evaluation. Australia: The University of Queensland; 1996.
doi: 10.1186/s12960-022-00764-7 24. Kononenko I. Inductive and Bayesian learning in medical
13. Mhlanga D. The role of artificial intelligence and machine diagnosis. Appl Artif Intell. 1993;7:317-337.
learning amid the COVID-19 pandemic: What lessons are doi: 10.1080/08839519308949977
we learning on 4IR and the sustainable development goals.
Int J Environ Res Public Health. 2022;19:1879. 25. Zupan B, Demšar J, Kattan MW, Beck JR, Bratko I. Machine
learning for survival analysis: A case study on recurrence of
doi: 10.3390/ijerph19041879 prostate cancer. Artif Intell Med. 2000;20:59-75.
14. Yogi MK, Garikipati J. Future scope of artificial intelligence doi: 10.1016/S0933-3657(00)00053-1
in healthcare for COVID-19. In: Emerging Technologies for
Combatting Pandemics. United Kingdom: Taylor & Francis; 26. Miller AS, Blott BH, Hames TK. Review of neural network
2022. p. 85-100. applications in medical imaging and signal processing. Med
Biol Eng Comput. 1992;30:449-464.
doi: 10.1201/9781003324447-5
doi: 10.1007/BF02441652
15. Pham QV, Nguyen DC, Huynh-The T, Hwang WJ,
Pathirana PN. Artificial intelligence (AI) and big data for 27. Lo SCB, Chan HP, Lin JS, Li H, Freedman MT, Mun SK.
coronavirus (COVID-19) pandemic: A survey on the state- Artificial convolution neural network for medical image
of-the-arts. IEEE Access. 2020;8:130820-130839. pattern recognition. Neural Netw. 1995;8:1201-1214.
doi: 10.1109/ACCESS.2020.3009328 doi: 10.1016/0893-6080(95)00061-5
16. Naudé W. Artificial Intelligence against COVID-19: An Early 28. Larranaga P, Calvo B, Santana R, et al. Machine learning in
Review. IZA Discussion Paper No. 13110; 2020. bioinformatics. Brief Bioinform. 2006;7:86-112.
doi: 10.2139/ssrn.3568314 doi: 10.1093/bib/bbk007
17. Adly AS, Adly AS, Adly MS. Approaches based on artificial 29. Dubitzky W, Granzow M, Berrar DP. Fundamentals of Data
intelligence and the internet of intelligent things to prevent Mining in Genomics and Proteomics. Berlin: Springer Science
the spread of COVID-19: Scoping review. J Med Internet Res. & Business Media; 2007.
2020;22:e19104. doi: 10.1007/978-0-387-47509-7
doi: 10.2196/19104 30. Hayes WS, Borodovsky M. How to interpret an anonymous
18. Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in bacterial genome: Machine learning approach to gene
healthcare: Past, present and future. Stroke Vasc Neurol. identification. Genome Res. 1998;8:1154-1171.
2017;2:230-243. doi: 10.1101/gr.8.11.1154
doi: 10.1136/svn-2017-000101 31. Zhavoronkov A, Ivanenkov YA, Aliper A, et al. Deep
19. Hamet P, Tremblay J. Artificial intelligence in medicine. learning enables rapid identification of potent DDR1 kinase
Metabolism. 2017;69:S36-S40. inhibitors. Nat Biotechnol. 2019;37:1038-1040.
doi: 10.1016/j.metabol.2017.01.011 doi: 10.1038/s41587-019-0224-x

Volume 1 Issue 2 (2024) 11 doi: 10.36922/aih.2401