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Frontiers in Educational Research, 2024, 7(10); doi: 10.25236/FER.2024.071028.

Future Trends in Pharmacy Education Reform: Integration of Big Data and Intelligent Technologies

Author(s)

Zongyuan Zhou1, Xiaoqin Wang1, Jie Yan2

Corresponding Author:
Zongyuan Zhou
Affiliation(s)

1Sichuan Industrial Institute of Antibiotics, School of Pharmacy, Chengdu University, Chengdu, China

2School of Politics and Public Administration, Southwest University of Political Science and Law, Chongqing, China

Abstract

Pharmacy education is undergoing profound changes due to advances in big data and intelligent technologies. National policies, particularly educational reforms and the “Healthy China” strategy, emphasize innovation and practical orientation. Collaborative efforts among universities, industries, and research institutions are fostering interdisciplinary curricula and intelligent teaching models. The integration of intelligent technologies in drug development and education has significantly improved efficiency, precision, and practical skills, enhancing students' innovative abilities. Moving forward, pharmacy education will increasingly merge technological advancements with academic disciplines. Policy support, resource investment, and teaching innovations will continue to develop multi-disciplinary talent suited to the modern pharmaceutical industry. These changes are essential for strengthening the pharmaceutical sector and advancing the "Healthy China" strategy.

Keywords

Pharmacy education; Big Data; Intelligent Technologies; Healthy China; pharmaceutical industry

Cite This Paper

Zongyuan Zhou, Xiaoqin Wang, Jie Yan. Future Trends in Pharmacy Education Reform: Integration of Big Data and Intelligent Technologies. Frontiers in Educational Research (2024) Vol. 7, Issue 10: 176-180. https://doi.org/10.25236/FER.2024.071028.

References

[1] Paul, D. et al. Artificial intelligence in drug discovery and development. Drug Discov Today 26, 80-93 (2021).

[2] Zhang, H., Wei, Y. & Mani Saravanan, K. Artificial intelligence and Computer-Aided Drug Discovery: Methods Development and Application. Methods (2024).

[3] Aljofan, M. & Gaipov, A. Drug discovery and development: the role of artificial intelligence in drug repurposing. Future Med Chem 16, 583-585 (2024).

[4] Ashley, E. A. Towards precision medicine. Nat Rev Genet 17, 507-522 (2016).

[5] White, B. J., Amrine, D. E. & Larson, R. L. BIG DATA ANALYTICS AND PRECISION ANIMAL AGRICULTURE SYMPOSIUM: Data to decisions. J Anim Sci 96, 1531-1539 (2018).

[6] Bao, Y. et al. Assessing The Impact Of State Policies For Prescription Drug Monitoring Programs On High-Risk Opioid Prescriptions. Health Aff (Millwood) 37, 1596-1604 (2018).

[7] Frenk, J. et al. Health professionals for a new century: transforming education to strengthen health systems in an interdependent world. Lancet 376, 1923-1958 (2010).

[8] Arora, G. et al. Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19. Pathogens 10 (2021).

[9] Mak, K. K. & Pichika, M. R. Artificial intelligence in drug development: present status and future prospects. Drug Discov Today 24, 773-780 (2019).

[10] Topol, E. J. High-performance medicine: the convergence of human and artificial intelligence. Nat Med 25, 44-56 (2019).

[11] Paul, S. M. et al. How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nat Rev Drug Discov 9, 203-214 (2010).

[12] Wouters, O. J., McKee, M. & Luyten, J. Estimated Research and Development Investment Needed to Bring a New Medicine to Market, 2009-2018. JAMA 323, 844-853 (2020).

[13] Pushpakom, S. et al. Drug repurposing: progress, challenges and recommendations. Nat Rev Drug Discov 18, 41-58 (2019).

[14] Crunkhorn, S. Immunotherapy: Vaccine patch to treat melanoma. Nat Rev Drug Discov 17, 18 (2017).

[15] Hutson, M. How AI is being used to accelerate clinical trials. Nature 627, S2-S5 (2024).

[16] Zhavoronkov, A. et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat Biotechnol 37, 1038-1040 (2019).

[17] Stokes, J. M. et al. A Deep Learning Approach to Antibiotic Discovery. Cell 180, 688-702 e613 (2020).

[18] Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583-589 (2021).

[19] Smer-Barreto, V. et al. Discovery of senolytics using machine learning. Nat Commun 14, 3445 (2023).

[20] Lau, E. & Wu, J. C. Omics, Big Data, and Precision Medicine in Cardiovascular Sciences. Circ Res 122, 1165-1168 (2018).

[21] Alyass, A., Turcotte, M. & Meyre, D. From big data analysis to personalized medicine for all: challenges and opportunities. BMC Med Genomics 8, 33 (2015).

[22] Bonkhoff, A. K. & Grefkes, C. Precision medicine in stroke: towards personalized outcome predictions using artificial intelligence. Brain 145, 457-475 (2022).

[23] Schussler-Fiorenza Rose, S. M. et al. A longitudinal big data approach for precision health. Nat Med 25, 792-804 (2019).

[24] Zhang, Z. et al. Integrated analysis of single-cell and bulk RNA sequencing data reveals a pan-cancer stemness signature predicting immunotherapy response. Genome Med 14, 45 (2022).

[25] Xiong, X., Wang, X., Liu, C. C., Shao, Z. M. & Yu, K. D. Deciphering breast cancer dynamics: insights from single-cell and spatial profiling in the multi-omics era. Biomark Res 12, 107 (2024).

[26] Silva, R. O. S., de Araujo, D., Dos Santos Menezes, P. W., Neves, E. R. Z. & de Lyra, D. P., Jr. Digital pharmacists: the new wave in pharmacy practice and education. Int J Clin Pharm 44, 775-780 (2022).

[27] ElKhalifa, D. et al. Curriculum, competency development, and assessment methods of MSc and PhD pharmacy programs: a scoping review. BMC Med Educ 24, 989 (2024).

[28] Conley, M. & Yanny, A. M. Medicine's AI boom, <https://stanmed.stanford.edu/translating-ai-concepts-into-innovations/> (2020).

[29] Abdel Aziz, M. H. et al. A scoping review of artificial intelligence within pharmacy education. American Journal of Pharmaceutical Education 88 (2024).

[30] DESHENG, C. Xi: Build leading nation in education, <https://www.chinadaily.com. cn/a/202409/ 11/WS66e0ca93a3103711928a73ce.html> (2024).

[31] CHINA, T. P. S. R. O. China to promote national health during 14th Five-Year Plan, <https://english.www.gov.cn/policies/latestreleases/202205/20/content_WS62874295c6d02e533532b0bd.html> (2022).

[32] China, P. s. R. o., Council, T. S. & XinhuaNet. The CCPC and State Council Publishes ‘China's Education Modernisation 2035 Plan, <www.gov.cn/xinwen/2019-02/23/content_5367987.htm> (2019).