Towards a Non-Discriminatory Artificial Intelligence in Healthcare: Ensuring Equal Access and Utilization for Rural and Underdeveloped Areas

Authors

  • Ris Heskiel Najogi Sitinjak Community Health Data Improvement Program (CHIP), Indonesia; Institute for Global Health, University College London, United Kingdom
  • Silvi May Angelia Purba Community Health Data Improvement Program (CHIP); Faculty of Medicine, Riau University; Arifin Achmad General Hospital, Indonesia
  • Salma Majidah Community Health Data Improvement Program (CHIP), Indonesia
  • Natasya Anggraeni Azis Community Health Data Improvement Program (CHIP), Indonesia

DOI:

https://doi.org/10.30641/ham.2025.16.177-196

Keywords:

artificial intelligence, social determinants of health, rural and underdeveloped areas, marginalization, equity in development

Abstract

The enjoyment of the highest attainable standard of health is one of the fundamental human rights. Artificial Intelligence (AI) is a ground-breaking innovation with huge potential to accelerate multisectoral progress, including in healthcare. Yet, its reliance on data availability, governance structures, infrastructure, and technical expertise can perpetuate biases against underrepresented communities and exacerbate existing inequalities. This paper explores strategies to develop a just, inclusive, and humane AI framework that enhances healthcare services while ensuring equal access and utilization for people in rural and underdeveloped areas (RUAs). A narrative review was conducted through targeted searches in scientific databases and verified sources from April to June 2024 using relevant keywords such as “health as a human right,” “AI and health,” “AI in rural areas,” “AI and inequality,” “rural development,” and “AI and social determinants of health,”. The review highlights the profound impact of AI on RUA residents, who are disproportionately marginalized by the interplay of spatial and socioeconomic limitations. These challenges are amplified by uneven technological progress and the demand for specialized skills across different regions. Health data equity for RUAs could be enhanced by promoting social innovation, together with active community participation and human capital development. In this context, targeted training initiatives and coordinated efforts among educational institutions, employers, healthcare facilities, and labor unions can empower workers in RUAs to engage with evolving AI-driven systems. Ultimately, ideal and unbiased AI should safeguard health as a human right by ensuring inclusivity and non-discriminatory frameworks, becoming sustainable in its respective communities, and upholding ethical conduct.

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References

Abouelmehdi, Karim, Abderrahim Beni-Hessane, and Hayat Khaloufi. "Big Healthcare Data: Preserving Security and Privacy." Journal of Big Data 5, no. 1 (2018/01/09 2018): 1. DOI: https://doi.org/10.1186/s40537-017-0110-7

Abu-Salih, Bilal, Pornpit Wongthongtham, Kevin Coutinho, Raneem Qaddoura, Omar Elshaweesh, and Mohammad Wedyan. "The Development of a Road Network Flood Risk Detection Model Using Optimised Ensemble Learning." Engineering Applications of Artificial Intelligence 122 (03/14 2023): 106081. DOI: https://doi.org/10.1016/j.engappai.2023.106081

Ahmed, Mohamed Ali Ag, Soumaila Laye Diakite, Koman Sissoko, Marie-Pierre Gagnon, and Sylvie Charron. "Factors Explaining the Shortage and Poor Retention of Qualified Health Workers in Rural and Remote Areas of the Kayes, Region of Mali: A Qualitative Study." Rural and Remote Health 20, no. 3 (2020).

Akhtar, Mohammad Amir Khusru, Mohit Kumar, and Anand Nayyar. "Ensuring Fairness and Non-Discrimination in Explainable Ai." In Towards Ethical and Socially Responsible Explainable Ai: Challenges and Opportunities, edited by Mohammad Amir Khusru Akhtar, Mohit Kumar and Anand Nayyar, 165-92. Cham: Springer Nature Switzerland, 2024. DOI: https://doi.org/10.1007/978-3-031-66489-2_6

Alayande, Ayantola. "Generative Ai in Low-Resourced Contexts: Considerations for Innovators and Policymakers." Bennett Institute for Public Policy, https://www.bennettinstitute.cam.ac.uk/blog/ai-in-low-resourced-contexts/.

Amisha, Paras Malik, Monika Pathania, and Vyas Kumar Rathaur. "Overview of Artificial Intelligence in Medicine." Journal of Family Medicine and Primary Care 8, no. 7 (2019): 2328-31. DOI: https://doi.org/10.4103/jfmpc.jfmpc_440_19

Arfianto, Arif, and Ahmad Balahmar. "Pemberdayaan Masyarakat Dalam Pembangunan Ekonomi Desa." JKMP (Jurnal Kebijakan dan Manajemen Publik) 2 (11/07 2016): 53. DOI: https://doi.org/10.21070/jkmp.v2i1.408

Arora, G., J. Joshi, R. S. Mandal, N. Shrivastava, R. Virmani, and T. Sethi. "Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against Covid-19." [In eng]. Pathogens 10, no. 8 (Aug 18 2021). DOI: https://doi.org/10.3390/pathogens10081048

Ashique, S., N. Mishra, S. Mohanto, A. Garg, F. Taghizadeh-Hesary, B. H. J. Gowda, and D. K. Chellappan. "Application of Artificial Intelligence (Ai) to Control Covid-19 Pandemic: Current Status and Future Prospects." [In eng]. Heliyon 10, no. 4 (Feb 29 2024): e25754. DOI: https://doi.org/10.1016/j.heliyon.2024.e25754

Assembly, United Nations General. "Digital Innovation, Technologies, and the Right to Health: Report of the Special Rappoteur on the Right of Everyone to the Enjoyment of the Highest Attainable Standard of Physical and Mental Health." Paris: United Nations, 2023.

———. "Universal Declaration of Human Rights." Paris: United Nations, 1948.

Awasthi, R., K. K. Guliani, S. A. Khan, A. Vashishtha, M. S. Gill, A. Bhatt, A. Nagori, et al. "Vacsim: Learning Effective Strategies for Covid-19 Vaccine Distribution Using Reinforcement Learning." [In eng]. Intell Based Med 6 (2022): 100060. DOI: https://doi.org/10.1016/j.ibmed.2022.100060

Backman, Isabella. "Eliminating Racial Bias in Health Care Ai: Expert Panel Offers Guidelines." Yale School of Medicine, https://medicine.yale.edu/news-article/eliminating-racial-bias-in-health-care-ai-expert-panel-offers-guidelines/.

Bekemeier, B., S. Park, U. Backonja, I. Ornelas, and A. M. Turner. "Data, Capacity-Building, and Training Needs to Address Rural Health Inequities in the Northwest United States: A Qualitative Study." [In eng]. J Am Med Inform Assoc 26, no. 8-9 (Aug 1 2019): 825-34. DOI: https://doi.org/10.1093/jamia/ocz037

Bock, Bettina. "Rural Marginalisation and the Role of Social Innovation: A Turn Towards Nexogenous Development and Rural Reconnection." Sociologia Ruralis 56 (12/01 2015): n/a-n/a. DOI: https://doi.org/10.1111/soru.12119

Bock, Bettina, Katalin Kovács, and Mark Shucksmith. "Changing Social Characteristics, Patterns of Inequality and Exclusion." 193-211, 2014.

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Published

12/18/2025

How to Cite

Sitinjak, Ris Heskiel Najogi, Silvi May Angelia Purba, Salma Majidah, and Natasya Anggraeni Azis. “Towards a Non-Discriminatory Artificial Intelligence in Healthcare: Ensuring Equal Access and Utilization for Rural and Underdeveloped Areas”. Jurnal HAM 16, no. 3 (December 18, 2025): 177–196. Accessed September 17, 2026. https://lawpolicyjournal.id/index.php/ham/article/view/5236.

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