Transforming Healthcare: The Dual Impact of Artificial Intelligence on Vaccines and Patient Care
Keywords:
AI, healthcare, vaccination, development, distribution, and distribution of vaccines; customized medicine; fair access; healthcare disparities; ethics; transparency; prejudice; privacy; public health; innovationAbstract
Artificial intelligence (AI) has the potential to transform healthcare and immunization programs, enhance patient outcomes, and advance public health goals. This can be achieved by the incorporation of AI into these tactics. This study examines the complex effects of AI on vaccine distribution, development, efficacy tracking, personalized medicine, and fair access to healthcare. AI-driven methods speed up the development of vaccines by identifying candidates more quickly, improving the design of formulations, and making unprecedentedly accurate and fast predictions about their efficacy. Furthermore, AI improves supply chain management and vaccine distribution by streamlining scheduling, routing, and allocation procedures to provide fair access for all populations. By using AI to customize vaccination regimens based on unique traits, preferences, and risk profiles, personalized medicine techniques increase immunization efficacy and reduce side effects. In addition, AI reduces healthcare disparities by highlighting interventions for underrepresented groups, identifying underprivileged communities, reducing biases, and enhancing transparency. While AI has the potential to be a game-changer, in order to maintain moral standards and advance fair access to healthcare services, ethical issues like privacy, prejudice, transparency, and equity must be carefully considered. All things considered, the incorporation of AI into immunization programs and healthcare signifies a paradigm change that could help to mold a future in which everyone has access to more effective, equitable, and individualized healthcare.
References
Hwang TJ, Kesselheim AS, Vokinger KN. Lifecycle regulation of artificial intelligence- and machine learning-based software devices in medicine. JAMA. (2019) 322:2285–6. doi: 10.1001/jama.2019.16842
Adamson AS, Smith A. Machine learning and health care disparities in dermatology. JAMA Dermatol. (2018) 154:1247– 8. doi: 10.1001/jamadermatol.2018.2348 Frontiers in Medicine | www.frontiersin.org 12 September 2021 | Volume 8 | Article 704256 Wang et al. AI for COVID-19
Abdulaal A, Patel A, Charani E, Denny S, Mughal N, Moore L. Prognostic modeling of COVID-19 using artificial intelligence in the United Kingdom: model development and validation. J Med Internet Res. (2020) 22:e20259. doi: 10.2196/20259
Yousefzadeh M, Esfahanian P, Movahed SMS, Gorgin S, Rahmati D, Abedini A, et al. ai-corona: Radiologist-assistant deep learning framework for COVID-19 diagnosis in chest CT scans. PLoS ONE. (2021) 16:e0250952. doi: 10.1371/journal.pone.0250952
Bai HX, Wang R, Xiong Z, Hsieh B, Chang K, Halsey K, et al. Artificial intelligence augmentation of radiologist performance in distinguishing COVID-19 from pneumonia of other origin at chest CT. Radiology. (2020) 296:E156–65. doi: 10.1148/radiol.2020201491
Fang C, Bai S, Chen Q, Zhou Y, Xia L, Qin L, et al. Deep learning for predicting COVID-19 malignant progression. Med Image Anal. (2021) 72:102096. doi: 10.1016/j.media.2021.102096
Al-Qaness MAA, Saba AI, Elsheikh AH, Elaziz MA, Ibrahim RA, Lu S, et al. Efficient artificial intelligence forecasting models for COVID-19 outbreak in Russia and Brazil. Process Saf Environ Prot. (2021) 149:399– 409. doi: 10.1016/j.psep.2020.11.007
Ong E, Wong MU, Huffman A, He Y. COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning. Front Immunol. (2020) 11:1581. doi: 10.3389/fimmu.2020.01581 18. Naudé W. Artificial intelligence vs COVID-19: limitations, constraints and pitfalls. AI Soc. (2020) 35:761–65. doi: 10.1007/s00146-020-00978-0
Chen J, See KC. Artificial intelligence for COVID-19: rapid review. J Med Internet Res. (2020) 22:e21476. doi: 10.2196/21476
Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. (2009) 6:e1000097. doi: 10.1371/journal.pmed.1000097
Zhou P, Yang XL, Wang XG, Hu B, Zhang L, Zhang W, et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature. (2020) 579:270–3.doi: 10.1038/s41586-020-2012-7
Zhou T, Liu Q, Yang Z, Liao J, Yang K, Bai W, et al. Preliminary prediction of the basic reproduction number of the Wuhan novel coronavirus 2019-nCoV. J Evid Based Med. (2020) 13:3–7. doi: 10.1111/jebm.12376
Ma X, Wang Y, Gao T, He Q, He Y, Yue R, et al. Challenges and strategies to research ethics in conducting COVID-19 research. J Evid Based Med. (2020) 13:173–177. doi: 10.1111/jebm.12388
Mei X, Lee HC, Diao KY, Huang M, Lin B, Liu C, et al. Artificial intelligence– enabled rapid diagnosis of patients with COVID-19. Nat Med. (2020) 26:1224–8. doi: 10.1038/s41591-020-0931-3
Mishra AK, Das SK, Roy P, Bandyopadhyay S. Identifying COVID19 from chest CT images: a deep convolutional neural networks based approach. J Healthc Eng. (2020) 2020:8843664. doi: 10.1155/2020/8843664
Ouyang X, Huo J, Xia L, Shan F, Liu J, Mo Z, et al. DualSampling attention network for diagnosis of COVID-19 from community acquired pneumonia. IEEE Trans Med Imaging. (2020) 39:2595–605. doi: 10.1109/TMI.2020.2995508
Sakagianni A, Feretzakis G, Kalles D, Koufopoulou C, Kaldis V. Setting up an easy-to-use machine learning pipeline for medical decision support: a case study for COVID-19 diagnosis based on deep learning with CT scans. Stud Health Technol Inform. (2020) 272:13–6. doi: 10.3233/SHTI200481
Sharma S. Drawing insights from COVID-19-infected patients using CT scan images and machine learning techniques: a study on 200 patients. Environ Sci Pollut Res Int. (2020) 27:37155–63. doi: 10.1007/s11356-020-10133-3
Wang J, Bao Y, Wen Y, Lu H, Luo H, Xiang Y, et al. Prior-Attention residual learning for more discriminative COVID-19 screening in CT images. IEEE Trans Med Imaging. (2020) 39:2572–83. doi: 10.1109/TMI.2020.2994908
Wang S, Zha Y, Li W, Wu Q, Li X, Niu M, et al. A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis. Eur Respir J. (2020) 56:2000775. doi: 10.1183/13993003.00775-2020
Wu X, Hui H, Niu M, Li L, Wang L, He B, et al. Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: a multicentre study. Eur J Radiol. (2020) 128:109041. doi: 10.1016/j.ejrad.2020.109041
Yan T, Wong PK, Ren H, Wang H, Wang J, Li Y. Automatic distinction between COVID-19 and common pneumonia using multi-scale convolutional neural network on chest CT scans. Chaos Solitons Fractals. (2020) 140:110153. doi: 10.1016/j.chaos.2020.110153
Jamal, A. (2023). Vaccines: Advancements, Impact, and the Road Ahead in Medicine. BULLET: Jurnal Multidisiplin Ilmu, 2(5).
Zhang K, Liu X, Shen J, Li Z, Sang Y, Wu X, et al. Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of COVID-19 pneumonia using computed tomography. Cell. (2020) 181:1423– 33.e11. doi: 10.1016/j.cell.2020.04.045
Harmon SA, Sanford TH, Xu S, Turkbey EB, Roth H, Xu Z, et al. Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets. Nat Commun. (2020) 11:4080. doi: 10.1038/s41467-020-17971-2
Elaziz MA, Hosny KM, Salah A, Darwish MM, Lu S, Sahlol AT. New machine learning method for image-based diagnosis of COVID-19. PLoS ONE. (2020) 15:e0235187. doi: 10.1371/journal.pone.0235187
Wiersinga WJ, Rhodes A, Cheng AC, Peacock SJ, Prescott HC. Pathophysiology, transmission, diagnosis, and treatment of coronavirus disease 2019 (COVID-19): a review. JAMA. (2020) 324:782–93. doi: 10.1001/jama.2020.12839
Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. (2020) 395:497–506. doi: 10.1016/S0140-6736(20)30183-5 6.
Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, et al. clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA. (2020) 323:1061–9. doi: 10.1001/jama.2020.1585
Yassine HM, Shah Z. How could artificial intelligence aid in the fight against coronavirus? Expert Rev Anti Infect Ther. (2020) 18:493– 7. doi: 10.1080/14787210.2020.1744275
Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Fut Healthc J. (2019) 6:94–8. doi: 10.7861/futurehosp. 6-2-94
Shen J, Zhang CJP, Jiang B, Chen J, Song J, Liu Z, et al. Artificial intelligence versus clinicians in disease diagnosis: systematic review. JMIR Med Informat. (2019) 7:e10010. doi: 10.2196/10010
Moons KGM, Wolff RF, Riley RD, Whiting PF, Westwood M, Collins GS, et al. PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med. (2019) 170:W1–33. doi: 10.7326/M18-1377
Wolff RF, Moons KGM, Riley RD, Whiting PF, Westwood M, Collins GS, et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med. (2019) 170:51–8. doi: 10.7326/M18-1376
Abbasian Ardakani A, Acharya UR, Habibollahi S, Mohammadi A. COVIDiag: a clinical CAD system to diagnose COVID19 pneumonia based on CT findings. Eur Radiol. (2020) 31:121–30. doi: 10.1007/s00330-020-07087-y
Ardakani AA, Kanafi AR, Acharya UR, Khadem N, Mohammadi A. Application of deep learning technique to manage COVID19 in routine clinical practice using CT images: Results of 10 convolutional neural networks. Comput Biol Med. (2020) 121:103795. doi: 10.1016/j.compbiomed.2020.103795
Han Z, Wei B, Hong Y, Li T, Cong J, Zhu X, et al. Accurate screening of COVID-19 using attention-based deep 3D multiple instance learning. IEEE Trans Med Imaging. (2020) 39:2584–94. doi:10.1109/TMI.2020.2996256
Ko H, Chung H, Kang WS, Kim KW, Shin Y, Kang SJ, et al. COVID-19 pneumonia diagnosis using a simple 2d deep learning framework with a single chest CT image: model development and validation. J Med Internet Res. (2020) 22:e19569. doi: 10.2196/19569
Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, et al. Using artificial intelligence to detect COVID-19 and community-acquired pneumonia based on pulmonary CT: evaluation of the diagnostic accuracy. Radiology. (2020) 296:E65–71. doi: 10.1148/radiol.2020200905
Liu C, Wang X, Liu C, Sun Q, Peng W. Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning. Biomed Eng Online. (2020) 19:66. doi: 10.1186/s12938-020-00809-9