Apply Artificial Intelligence for Improving Medical Accessibility in Rural Areas
DOI:
https://doi.org/10.54097/xvn4xq38Keywords:
Artificial Intelligence, healthcare, rural area, accessibility, equity.Abstract
One enduring and unsolved global issue is the disparity in healthcare between urban and rural areas. Low health literacy, antiquated equipment, and a lack of medical professionals are common problems in rural areas. One promising way to help close this gap is artificial intelligence (AI). AI has demonstrated significant promise in China's rural healthcare system for increasing diagnostic precision, facilitating remote consultations, and allocating resources as efficiently as possible. The particular uses of AI in these fields, its multifaceted effects, and the difficulties in promoting and implementing it are all covered in this essay. Hardware constraints that impede the deployment of technology, such as inadequate infrastructure, insufficient network coverage, and restricted access to processing power in remote areas, present the first challenge. The second is a lack of trust in rural communities and software-related concerns about patient rights and legal compliance, including data privacy and security risks in the gathering, sharing, and storing of health information. To truly achieve equitable healthcare for all, AI must be implemented successfully, which calls for scalable and flexible solutions backed by robust policy frameworks and ongoing stakeholder engagement.
Downloads
References
[1] Keng C, DiGiorgio A, Ehrenfeld J M, et al. Unburdening Patients and Clinicians Through Automation and Artificial Intelligence: Informatics Strategies for Reducing Administrative Burden. Journal of Medical Systems, 2025, 49 (1): 1 - 4. DOI: https://doi.org/10.1007/s10916-025-02265-1
[2] Yang Y. Digital Assistance for Xinjiang, Safeguarding Brightness - Wenzhou Medical University Joins Hands with Aksu, Xinjiang to Build a Digital and Intelligent Eye Health Consortium. Guangming Network. 2025. Available at: https://news.gmw.cn/2025-10/13/content_38337039.htm. Accessed 23 October 2025.
[3] Yuxi Network. Smart Healthcare Brings New Model for People's Medical Treatment - Health Brain + Cloud Hospital Facilitates Transformation of Yuxi Medical Service System. Yuxinet, 2025. Available at: http://yuxinet.cn/c/2025/10/13/1074005.shtml. Accessed 23 October 2025.
[4] Qinghai. Huangyuan County, Qinghai Province, has established an integrated smart chronic disease prevention and control system - Public Services - Qinghai Provincial Government Website. 2025. Available at: http://www.qinghai.gov.cn/msfw/system/2025/10/13/030083760.shtml. Accessed 23 October 2025.
[5] Sina_mobile. AI Breaks Through in Primary Healthcare: A Digital Transformation for County-level Healthcare Services Affecting 800 million People. Sina.cn. 2025. Available at: https://k.sina.cn/article_1838672663_6d97eb1702001jj8u.html?cre=wappage&from=finance&kdurlshow=1&loc=4&mod=r&r=0&rfunc=21&tj=cx_wap_news_relate. Accessed 23 October 2025.
[6] Kong X, Ai B, Kong Y, et al. Artificial intelligence: a key to relieve China’s insufficient and unequally-distributed medical resources. American Journal of Translational Research, 2019, 11 (5): 2632.
[7] Lamem M F H, Sahid M I, Ahmed A. Artificial intelligence for access to primary healthcare in rural settings. Journal of Medicine, Surgery, and Public Health, 2025, 5: 100173. DOI: https://doi.org/10.1016/j.glmedi.2024.100173
[8] Goh J H L, Lim Z W, Fang X, et al. Artificial intelligence for cataract detection and management. Asia-Pacific journal of ophthalmology, 2020, 9 (2): 88 - 95. DOI: https://doi.org/10.1097/01.APO.0000656988.16221.04
[9] Perez K, Wisniewski D, Ari A, et al. Investigation into application of AI and telemedicine in rural communities: a systematic literature review//Healthcare. MDPI, 2025, 13 (3): 324. DOI: https://doi.org/10.3390/healthcare13030324
[10] Ademeji F, Okoro E, Akingbulere G, et al. Predictive Analytics for Healthcare Resource Allocation in Underserved Communities. Journal of Research in Engineering and Computer Sciences, 2024, 2 (6): 21 - 37. DOI: https://doi.org/10.63002/jrecs.26.726
[11] Miller A C, Cavanaugh J E, Arakkal A T, et al. A comprehensive framework to estimate the frequency, duration, and risk factors for diagnostic delays using bootstrapping-based simulation methods. BMC medical informatics and decision making, 2023, 23 (1): 68. DOI: https://doi.org/10.1186/s12911-023-02148-w
[12] Jeong J, Kim S, Pan L, et al. Reducing the workload of medical diagnosis through artificial intelligence: A narrative review. Medicine, 2025, 104 (6): e41470. DOI: https://doi.org/10.1097/MD.0000000000041470
[13] Khanna N, Maindarkar M A, Viswanathan V, et al. Economics of artificial intelligence in healthcare: diagnosis vs. treatment//Healthcare. MDPI, 2022, 10 (12): 2493. DOI: https://doi.org/10.3390/healthcare10122493
[14] Apoorv Gehlot. AI in Rural Healthcare: A New Dawn for Equitable Medical Access. Medium. 2025. Available at: https://medium.com/@apoorv-gehlot/ai-in-rural-healthcare-a-new-dawn-for-equitable-medical-access-fb3379635f38. Accessed 23 October 2025.
[15] Maita K C, Maniaci M J, Haider C R, et al. The impact of digital health solutions on bridging the health care gap in rural areas: a scoping review. The Permanente Journal, 2024, 28 (3): 130. DOI: https://doi.org/10.7812/TPP/23.134
[16] Krahe M A, Baker S, Woods L, et al. Factors that influence digital health implementation in rural, regional, and remote Australia: An overview of reviews and recommended strategies. Australian Journal of Rural Health, 2025, 33 (2): e70045. DOI: https://doi.org/10.1111/ajr.70045
[17] Norori N, Hu Q, Aellen F M, et al. Addressing bias in big data and AI for health care: A call for open science. Patterns, 2021, 2 (10). DOI: https://doi.org/10.1016/j.patter.2021.100347
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Highlights in Business, Economics and Management
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
