Development of artificial intelligence in modern medicine

Vysotskyi A.A.,Surikov O.O.,Vasyliuk-Zaitseva S.V.

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Development of artificial intelligence in modern medicine

April 14, 2023

1264

Digital medicine

Vysotskyi A.A.

Surikov O.O.

Vasyliuk-Zaitseva S.V.

Keywords :

artificial intelligence,

Specialities :

Resume

The article provides an overview of the current state and development of artificial intelligence in the medical industry, existing implementations, and shows the need for implementation in medical institutions.

Aim: to research and highlight the current development of artificial intelligence in healthcare.

Results. The historical aspects, the current state of the development of artificial intelligence, its methods and tools in various fields of medicine, namely cardiology, orthopedics, ophthalmology, and laboratory diagnostics, are considered.

Conclusion. The article shows the relevance of introducing artificial intelligence into the healthcare sector to improve the accuracy of diagnosis, correct treatment and quality of patient care, as well as reduce the workload of medical specialists.

References

1. Anahtar M.N., Yang J.H., Kanjilal S. (2021) Applications of machine learning to the problem of antimicrobial resistance: an emerging model for translational research. J. Clin. Microbiol., 59(7): e0126020. doi: 10.1128/JCM.01260-20.
2. Benjamins J.W., Hendriks T., Knuuti J. et al. (2019) A primer in artificial intelligence in cardiovascular medicine. Neth. Heart. J., 27(9): 392–402. doi: 10.1007/s12471-019-1286-6.
3. Kamphuis B. (2018) Universiteiten kunnen belangstelling voor kunstmatige intelligentie niet aan. Ned. Omroep. Sticht. nos.nl/artikel/2241732-universiteiten-kunnen-belangstelling-voor-kunstmatige-intelligentie-niet-aan.html.
4. Mervis J. (2018) MIT to use $350 million gift to bolster computer sciences. Science. http://www.science.org/content/article/mit-use-350-million-gift-bolster-computer-sciences.
5. Nikkei Staff Writers (2018) Japan plans 10 «AI hospitals» to ease doctor shortages. asia.nikkei.com/Politics/Japan-plans-10-AI-hospitals-to-ease-doctor-shortages.
6. Jiang F., Jiang Y., Zhi H. et al. (2017) Artificial intelligence in healthcare: past, present and future. Stroke Vasc. Neurol., 2(4): 230–243. doi: 10.1136/svn-2017-000101.
7. Aung Y.Y.M., Wong D.C.S., Ting D.S.W. (2021) The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. Br. Med. Bull., 139(1): 4–15. doi: 10.1093/bmb/ldab016.
8. Balyen L., Peto T. (2019) Promising artificial intelligence-machine learning-deep learning algorithms in ophthalmology. Asia Pac. J. Ophthalmol. (Phila), 8(3): 264–272. doi: 10.22608/APO.2018479.
9. Juarez-Orozco L.E., Knol R.J.J., Sanchez-Catasus C.A. et al. (2020) Machine learning in the integration of simple variables for identifying patients with myocardial ischemia. J. Nucl. Cardiol., 27(1): 147–155. doi: 10.1007/s12350-018-1304-x.
10. Chen J., See K. (2020) Artificial intelligence for COVID-19: rapid review. J. Med. Internet. Res., 22: e21476. doi: 10.2196/21476.
11. Saeed U., Shah S.Y., Ahmad J. et al. (2022) Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review. J. Pharm. Anal., 12(2): 193–204. doi: 10.1016/j.jpha.2021.12.006.
12. Ostaschenko Т.М., Kozak N.D., Kozak D.О. (2021) Coordination aspects of pharmacovigilance system adjustment in terms of the global COVID-19 pandemic. Ukr. J. Mil. Med., 2(4): 161–165. DOI: 10.46847/ujmm.2021.4(2)-161.
13. Haymond S., McCudden C. (2021) Rise of the Machines: Artificial Intelligence and the Clinical Laboratory. J. Appl. Lab. Med., 6(6): 1640–1654. doi: 10.1093/jalm/jfab075.
14. Paranjape K., Schinkel M., Hammer R.D. et al. (2021) The value of artificial intelligence in laboratory medicine. Am. J. Clin. Pathol., 155(6): 823–831. doi: 10.1093/ajcp/aqaa170.
15. Dogan M.V., Grumbach I.M., Michaelson J.J. et al. (2018) Integrated genetic and epigenetic prediction of coronary heart disease in the Framingham Heart Study. PLoS ONE., 13: e0190549. doi: 10.1371/journal.pone.0190549.
16. Hui A.T., Alvandi L.M., Eleswarapu A.S. et al. (2022) Artificial intelligence in modern orthopaedics: current and future applications. JBJS Rev., 10(10). doi: 10.2106/JBJS.RVW.22.00086.
17. Federer S.J., Jones G.G. (2021) Artificial intelligence in orthopaedics: A scoping review. PLoS One., 16(11): e0260471. doi: 10.1371/journal.pone.0260471.
18. Saygılı A., Albayrak S. (2019) An efficient and fast computer-aided method for fully automated diagnosis of meniscal tears from magnetic resonance images. Artif. Intell. Med., 97: 118–130. doi: 10.1016/j.artmed.2018.11.008.
19. Carballido-Gamio J., Yu A., Wang L. et al. (2019) Hip fracture discrimination based on statistical multi-parametric modeling (SMPM). Ann. Biomed. Eng., 47(11): 2199–2212. doi: 10.1007/s10439-019-02298-x.
20. Ossowska A., Kusiak A., Świetlik D. (2022) Artificial intelligence in dentistry-narrative review. Int. J. Environ. Res. Public Health, 19(6): 3449. doi: 10.3390/ijerph19063449.
21. Geetha V., Aprameya K.S., Hinduja D.M. (2020) Dental caries diagnosis in digital radiographs using back-propagation neural network. Health Inf. Sci. Syst., 8: 1–14. doi: 10.1007/s13755-019-0096-y.
22. Orhan K., Bayrakdar I.S., Ezhov M. et al. (2020) Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans. Int. Endod. J., 53: 680–689. doi: 10.1111/iej.13265.
23. Pauwels R., Brasil D.M., Yamasaki M.C. et al. (2021) Artificial intelligence for detection of periapical lesions on intraoral radiographs: Comparison between convolutional neural networks and human observers. Oral. Surg. Oral Med. Oral Pathol. Oral Radiol., 131: 610–616. doi: 10.1016/j.oooo.2021.01.018.
24. Kim B.S., Yeom H.G., Lee J.H. et al. (2021) Deep learning-based prediction of paresthesia after third molar extraction: a preliminary study. Diagnostics, 11: 1572. doi: 10.3390/diagnostics11091572.
25. Liu Z., Liu J., Zhou Z. et al. (2021) Differential diagnosis of ameloblastoma and odontogenic keratocyst by machine learning of panoramic radiographs. Int. J. Comput. Assist. Radiol. Surg., 16: 415–422. doi: 10.1007/s11548-021-02309-0.