Use of Artificial Machines in Medical Sciences
Artificial Machines
DOI:
https://doi.org/10.1071/ejmbs.v2i2.22Keywords:
Artificial intelligence, cancer, efficiency, radiology, surgeryAbstract
The application of artificial intelligence (AI) has grown across numerous industries, including healthcare. Health care providers of all shapes, sizes, and specializations are increasingly very interested in how artificial intelligence has developed and is assisting with patient requirements and care, as well as lowering costs and boosting efficiency. Radiology represents one of the most significant fields in artificial intelligence, with numerous opportunities for advancement. With the implementation of Machine Learning methods, the precision of cancer diagnosis findings has grown by 15-20% in recent years. The purpose of this research is to assist shape public perception of the use of artificial intelligence in many fields of medicine such as cancer, radiography, surgery, emergency care and the future of such machines in medical field.
References
Abernethy, A. P., Etheredge, L. M., Ganz, P. A., Wallace, P., German, R. R., Neti, C., Murphy, S. B. (2010). Rapid-learning system for cancer care. J Clin Oncol, 28(27), 4268-4278.
Badnjevic, A., Gurbeta, L., Custovic, E. (2018). An expert diagnostic system to automatically identify asthma and chronic obstructive pulmonary disease in clinical settings. Scientific Reports, 8(1), 1-9.
Badnjević, A., Pokvić, L. G., Džemić, Z., Bečić, F. (2020). Risks of emergency use authorizations for medical products during outbreak situations: a COVID-19 case study. Biomed Eng Online, 19(1), 1-14.
Business Research Company. [Online resource] Last accessed: 03.05.2021. https://www.globenewswire.com/ news-release/2020/10/27/2114984/0/en/Global-MedicalDevice-Market-2020-Size-To-Increase-Due-To-Rising-Infectious-And-Chronic-Disease-Cases-As-Per-The-BusinessResearch-Company-s-Medical-Devices-Global-Market-Opportuni.html
Catic, A., Gurbeta, L., Kurtovic-Kozaric, A., Mehmedbasic, S., Badnjevic, A. (2018). Application of Neural Networks for classification of Patau, Edwards, Down, Turner and Klinefelter Syndrome based on first trimester maternal serum screening data, ultrasonographic findings and patient demographics. BMC Medical Genetics, 11(1), 1-12.
Deo, R. C. (2015). Machine learning in medicine. Circulation, 132(20), 1920-1930.
European Council’s Conclusions on Health in the Digital Society. Specifically, Council of The European Union, 2017
Food and Drug Administration. Software as medical device SaMD. https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Webster, D. R. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama, 316(22), 2402-2410.
Gurbeta, L., Badnjevic, A., Maksimovic, M., Omanovic-Miklicanin, E., Sejdic, E. (2018). A telehealth system for automated diagnosis of asthma and chronical obstructive pulmonary disease. J Am Med Inform Assoc, 25(9), 1213-1217.
Hashimoto, D. A., Rosman, G., Rus, D., Meireles, O. R. (2018). Artificial intelligence in surgery: promises and perils. Ann Surg, 268(1), 70.
Jiang F., Jiang Y., Zhi, H. (2017). Artificial intelligence in healthcare: past, present, and future. Stroke Vasc Neurol, 2, 230-243
Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., Fotiadis, D. I. (2015). Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J, 13, 8-17.
LeCun, Y., Bengio, Y., Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Long, J. B., Ehrenfeld, J. M. (2020). The role of augmented intelligence (AI) in detecting and preventing the spread of novel coronavirus. J Med Syst, 44(3), 1-2.
Miller, D. D., Brown, E. W. (2018). Artificial intelligence in medical practice: the question to the answer?. Am J Med, 131(2), 129-133.
Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G. Z. (2016). Deep learning for health informatics. J Biomed Health Inform, 21(1), 4-21.
Sarić, R., Jokić, D., Beganović, N., Pokvić, L. G., Badnjević, A. (2020). FPGA-based real-time epileptic seizure classification using Artificial Neural Network. Biomed Signal Process Control, 62, 102106.
Senders, J.T., Arnaout, O., Karhade, A.V. (2017). Natural and artificial intelligence inneurosurgery: a systemic review. Neurosurgery, 83, 181-192
Thrall, J. H., Li, X., Li, Q., Cruz, C., Do, S., Dreyer, K., Brink, J. (2018). Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. J Am Coll Radiol, 15(3), 504-508.
Walton, O. B., Garoon, R. B., Weng, C. Y., Gross, J., Young, A. K., Camero, K. A., Chu, Y. I. (2016). Evaluation of automated teleretinal screening program for diabetic retinopathy. JAMA Ophthalmology, 134(2), 204-209.
Wang, D., Khosla, A., Gargeya, R., Irshad, H., Beck, A. H. (2016). Deep learning for identifying breast cancer. Proceedings of the International Society on Biomedical Imaging (ISBI), Quantitative Methods.
Ye, H., Shen, H., Dong, Y. (2017). Using Evidence-Based medicine through Advanced Data Analytics to work toward a National Standard for Hospital-based acute ischemic Stroke treatment. Mainland China, 65.
