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Reseach Article

A Comprehensive Survey on the Role of the Medical AI for the Healthcare Support

by Abdelrahman M. Helmi, Sayed A. AbdelGaber, Samah A. Bastawy
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 31
Year of Publication: 2023
Authors: Abdelrahman M. Helmi, Sayed A. AbdelGaber, Samah A. Bastawy
10.5120/ijca2023923073

Abdelrahman M. Helmi, Sayed A. AbdelGaber, Samah A. Bastawy . A Comprehensive Survey on the Role of the Medical AI for the Healthcare Support. International Journal of Computer Applications. 185, 31 ( Aug 2023), 30-37. DOI=10.5120/ijca2023923073

@article{ 10.5120/ijca2023923073,
author = { Abdelrahman M. Helmi, Sayed A. AbdelGaber, Samah A. Bastawy },
title = { A Comprehensive Survey on the Role of the Medical AI for the Healthcare Support },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2023 },
volume = { 185 },
number = { 31 },
month = { Aug },
year = { 2023 },
issn = { 0975-8887 },
pages = { 30-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number31/32894-2023923073/ },
doi = { 10.5120/ijca2023923073 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:27:35.026055+05:30
%A Abdelrahman M. Helmi
%A Sayed A. AbdelGaber
%A Samah A. Bastawy
%T A Comprehensive Survey on the Role of the Medical AI for the Healthcare Support
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 31
%P 30-37
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The COVID-19 pandemic has caused widespread devastation, affected millions of lives and placed immense pressure on healthcare systems worldwide. As the viruses continues to ravage communities, the need for solutions that can support the early diagnosis of diseases and automate critical processes in the healthcare system has become more pressing than ever before. Artificial Intelligence (AI) has emerged as a versatile technology in various industries due to its ability to save time and lives by enabling machines to learn from experience, adapt to new inputs, and perform human-like tasks. In the medical industry, AI has become a crucial tool in developing new applications to combat diseases and save lives. Therefore, researchers have focused on exploring the potential of AI in the fight against diseases and commonly coronavirus. This survey paper presents a comprehensive review of recent AI models and techniques used in virus diagnosis, showcasing the different approaches used in both released and novel solutions depending on different types of inputs, such as images and biomedical data. The paper provides an overview of the models' performance, highlighting their strengths and limitations. The study emphasizes the contributions of previous trials, demonstrating how AI has helped in detecting, tracking, and predicting the spread of viruses. Moreover, the paper highlights the challenges encountered while developing AI models for virus diagnosis, including the limited availability of high-quality data, the need for more diverse datasets, and the ethical challenges related to data privacy and security. The study offers insights to guide future research in developing more accurate and effective models. The paper provides a sensible outlook for the future researchers to develop highly effective models by highlighting the need to address the challenges encountered in previous trials. Finally, the paper concludes by emphasizing the importance of continued research and development in AI-based solutions to combat diseases.

References
  1. Jamshidi, M.B., Roshani, S., Talla, J., Lalbakhsh, A., Peroutka, Z., Roshani, S., Parandin, F., Malek, Z., Daneshfar, F., Niazkar, H.R. and Lotfi, S., 2022. A review of the potential of artificial intelligence approaches to forecasting COVID-19 spreading. Ai, 3(2), pp.493-511.
  2. Corbin, J.H., Oyene, U.E., Manoncourt, E., Onya, H., Kwamboka, M., Amuyunzu-Nyamongo, M., Sørensen, K., Mweemba, O., Barry, M.M., Munodawafa, D. and Bayugo, Y.V., 2021. A health promotion approach to emergency management: effective community engagement strategies from five cases. Health promotion international, 36(Supplement_1), pp.i24-i38.
  3. Khero, K., Usman, M. and Fong, A., 2023. Deep learning framework for early detection of COVID-19 using X-ray images. Multimedia Tools and Applications, pp.1-26.
  4. Hassan, H., Ren, Z., Zhao, H., Huang, S., Li, D., Xiang, S., Kang, Y., Chen, S. and Huang, B., 2022. Review and classification of AI-enabled COVID-19 CT imaging models based on computer vision tasks. Computers in biology and medicine, 141, p.105123.
  5. Mei, X., Lee, H.C., Diao, K.Y., Huang, M., Lin, B., Liu, C., Xie, Z., Ma, Y., Robson, P.M., Chung, M. and Bernheim, A., 2020. Artificial intelligence–enabled rapid diagnosis of patients with COVID-19. Nature medicine, 26(8), pp.1224-1228.
  6. Chen, B., Liu, Y., Zhang, Z., Li, Y., Zhang, Z., Lu, G. and Yu, H., 2021. Deep active context estimation for automated COVID-19 diagnosis. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 17(3s), pp.1-22.
  7. Qi, S., Xu, C., Li, C., Tian, B., Xia, S., Ren, J., Yang, L., Wang, H. and Yu, H., 2021. DR-MIL: deep represented multiple instance learning distinguishes COVID-19 from community-acquired pneumonia in CT images. Computer Methods and Programs in Biomedicine, 211, p.106406.
  8. Quiroz, J.C., Feng, Y.Z., Cheng, Z.Y., Rezazadegan, D., Chen, P.K., Lin, Q.T., Qian, L., Liu, X.F., Berkovsky, S., Coiera, E. and Song, L., 2021. Development and validation of a machine learning approach for automated severity assessment of COVID-19 based on clinical and imaging data: retrospective study. JMIR Medical Informatics, 9(2), p.e24572.
  9. Chieregato, M., Frangiamore, F., Morassi, M., Baresi, C., Nici, S., Bassetti, C., Bnà, C. and Galelli, M., 2022. A hybrid machine learning/deep learning COVID-19 severity predictive model from CT images and clinical data. Scientific reports, 12(1), p.4329.
  10. Hatani, F., 2022. JAPAN’S ‘ARTIFICIAL-INTELLIGENCE HOSPITAL’PROJECT: CAN IT HELP THE AGEING POPULATION?. International Perspectives on Artificial Intelligence, pp.43-51.
  11. Pirolli, D., Righino, B., Camponeschi, C., Ria, F., Di Sante, G. and De Rosa, M.C., 2023. Virtual screening and molecular dynamics simulations provide insight into repurposing drugs against SARS-CoV-2 variants Spike protein/ACE2 interface. Scientific Reports, 13(1), p.1494.
  12. Galetsi, P., Katsaliaki, K. and Kumar, S., 2022. The medical and societal impact of big data analytics and artificial intelligence applications in combating pandemics: A review focused on Covid-19. Social Science & Medicine, 301, p.114973.
  13. Iwendi, C., Mohan, S., Ibeke, E., Ahmadian, A. and Ciano, T., 2022. Covid-19 fake news sentiment analysis. Computers and electrical engineering, 101, p.107967.
  14. Gupta, R., Pandey, G., Chaudhary, P. and Pal, S.K., 2021. Technological and analytical review of contact tracing apps for COVID-19 management. Journal of Location Based Services, 15(3), pp.198-237.
  15. Wang, Z., Xiong, H., Zhang, J., Yang, S., Boukhechba, M., Zhang, D., Barnes, L.E. and Dou, D., 2022. From personalized medicine to population health: a survey of mHealth sensing techniques. IEEE Internet of Things Journal, 9(17), pp.15413-15434.
  16. Dao, T.P., Hoang, X.H.T., Nguyen, D.N., Huynh, N.Q., Pham, T.T., Nguyen, D.T., Nguyen, H.B., Do, N.H., Nguyen, H.V., Dao, C.H. and Nguyen, N.V., 2022. A geospatial platform to support visualization, analysis, and prediction of tuberculosis notification in space and time. Frontiers in Public Health, 10, p.973362.
  17. Li, Q. and Huang, Y., 2022. Optimizing global COVID-19 vaccine allocation: An agent-based computational model of 148 countries. PLoS computational biology, 18(9), p.e1010463.
  18. Kumar, K., Kumar, P., Deb, D., Unguresan, M.L. and Muresan, V., 2023, January. Artificial intelligence and machine learning based intervention in medical infrastructure: a review and future trends. In Healthcare (Vol. 11, No. 2, p. 207). MDPI.
  19. Kumar, Mukesh, Rana, Lokesh, Artificial Intelligence: A Tool for COVID-19 Surface Detection. International Journal of Innovation and Learning. (2020): 60-63.
  20. H. Maghded, "A Novel AI-enabled Framework to Diagnose Coronavirus COVID-19 using Smartphone Embedded Sensors: Design Study", IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI), Las Vegas, NV, USA, (2020): 180-187.
  21. Laguarta, J., Hueto, F, Subirana, B, COVID-19 Artificial Intelligence Diagnosis using only Cough Recordings. IEEE Open Journal of Engineering in Medicine and Biology, (2020)
  22. Gladstone Institutes. "New CRISPR-based test for COVID-19 uses a smartphone camera: The rapid, one-step mobile test could help combat the pandemic and fully reopen communities." ScienceDaily, (2020).
  23. Mandel, H.L., Colleen, G., Abedian, S., Ammar, N., Bailey, L.C., Bennett, T.D., Brannock, M.D., Brosnahan, S.B., Chen, Y., Chute, C.G. and Divers, J., 2023. Risk of post-acute sequelae of SARS-CoV-2 infection associated with pre-coronavirus disease obstructive sleep apnea diagnoses: an electronic health record-based analysis from the RECOVER initiative. Sleep, p.zsad126.
  24. Hui, W.X., Aneja, N., Aneja, S. and Naim, A.G., 2023. Conversational chat system using attention mechanism for COVID-19 inquiries. International Journal of Intelligent Networks.
  25. Sameni, M., Mirmotalebisohi, S.A., Dehghan, Z., Abooshahab, R., Khazaei-Poul, Y., Mozafar, M. and Zali, H., 2023. Deciphering molecular mechanisms of SARS-CoV-2 pathogenesis and drug repurposing through GRN motifs: a comprehensive systems biology study. 3 Biotech, 13(4), p.117.
  26. Kumar, D., Sood, S.K. and Rawat, K.S., 2023. IoT-enabled technologies for controlling COVID-19 Spread: A scientometric analysis using CiteSpace. Internet of Things, p.100863.
  27. Solfa, F.D.G. and Simonato, F.R., 2023. Big Data Analytics in Healthcare: Exploring the Role of Machine Learning in Predicting Patient Outcomes and Improving Healthcare Delivery. International Journal of Computations, Information and Manufacturing (IJCIM), 3(1), pp.1-9.
  28. Umair, A., Masciari, E. and Ullah, M.H., 2023. Vaccine sentiment analysis using BERT+ NBSVM and geo-spatial approaches. The Journal of Supercomputing, pp.1-31.
  29. Gupta, N.S. and Kumar, P., 2023. Perspective of artificial intelligence in healthcare data management: A journey towards precision medicine. Computers in Biology and Medicine, p.107051.
  30. Shareefa, P., Maheshwari, P.U., Donald, A.D., Srinivas, T.A.S. and Krishna, T.M., 2023. Forecasting the Future: Predicting COVID-19 Trends with Machine Learning. International Open-Access, Double-Blind, Peer-Reviewed, Refereed, Multidisciplinary Online, 3(4).
  31. Malik, I.A. and Shah, S.A., 2023. Economic impact of COVID-19 on Ethiopian micro, small, and medium enterprises and policy measures. The Scientific Temper, 14(02), pp.288-293.
  32. Al Banna, M.H., Ghosh, T., Nahian, M.J.A., Kaiser, M.S., Mahmud, M., Taher, K.A., Hossain, M.S. and Andersson, K., 2023. A Hybrid Deep Learning Model to Predict the Impact of COVID-19 on Mental Health from Social Media Big Data. IEEE Access.
Index Terms

Computer Science
Information Sciences

Keywords

Medical AI Novel Models Diagnoses Cloud-based AI Inclusion Criteria.