Artificial intelligence in healthcare and human resource management:  a review of the global divide, Pakistan's landscape and institutional innovations

Umair Maqbool; Lutaf Ali Mangrio; Mazhar Iqbal

doi:10.18203/2349-3933.ijam20263729

Authors

Umair Maqbool Sindh Integrated Emergency and Health Services, SIEHS, Health Department, Government of Sindh, Karachi, Pakistan
Lutaf Ali Mangrio Sindh Integrated Emergency and Health Services, SIEHS, Health Department, Government of Sindh, Karachi, Pakistan
Mazhar Iqbal Sindh Integrated Emergency and Health Services, SIEHS, Health Department, Government of Sindh, Karachi, Pakistan

DOI:

https://doi.org/10.18203/2349-3933.ijam20263729

Keywords:

Artificial intelligence, Healthcare, Human resource management, Low- and middle-income countries, Pakistan, Telemedicine, Digital divide

Abstract

Artificial intelligence (AI) is reshaping healthcare and organizational management worldwide, yet its benefits remain unevenly distributed between high-income and low- and middle-income countries (LMICs). This review examines the structural drivers of this global AI divide, situates Pakistan's healthcare and digital infrastructure challenges within this context, and presents an institutional case study of four AI-enabled interventions implemented at the Sindh Integrated Emergency and Health Services (SIEHS), an emergency and health services organization under the Health Department, Government of Sindh, Pakistan: an AI-driven recruitment and candidate-job matching system (Talent Match Live), a facial-recognition and geolocation-based field monitoring application, an AI-assisted CCTV training-monitoring system, and an AI-augmented telemedicine triage service embedded within SIEHS's Teletabeeb telehealth department. Each intervention is evaluated against comparable systems documented in the international literature to assess feasibility, evidence of effectiveness, and associated ethical risks. A responsible-adoption framework is proposed to guide organizations in resource-constrained settings toward AI integration that is effective, equitable, and ethically governed. The review concludes that while AI offers meaningful efficiency and access gains for LMIC public-sector organizations such as SIEHS, sustainable adoption requires deliberate attention to data protection, algorithmic bias, workforce consent, and alignment with international ethical guidance.

References

GTAI, Roland Berger. Projected global digital health market size from 2019 to 2025 (in billion U.S. dollars). Statista; 2019. Available at: https://www.statista.com/statistics/1092869/global-digital-health-market-size-forecast/. Accessed on 22 July 2026.

Demaidi MN. Artificial intelligence national strategy in a developing country. AI Soc. 2025;40:423-35.

Oxford Insights. Government AI Readiness Index 2022. London: Oxford Insights; 2022. Available at: https://oxfordinsights.com. Accessed on 22 July 2026.

World Bank. World Bank country classifications by income level for 2024-2025. Washington (DC): World Bank Group; 2024. Available at: https://blogs.worldbank.org/en/opendata/world-bank-country-classifications-by-income-level-for-2024-2025. Accessed on 22 July 2026.

National Institute of Population Studies (NIPS) [Pakistan], ICF. Pakistan Demographic and Health Survey 2017-18. Islamabad, Pakistan, and Rockville, Maryland, USA: NIPS and ICF. 2019.

GBD 2019 Healthcare Access and Quality Collaborators. Assessing performance of the Healthcare Access and Quality Index, overall and by select age groups, for 204 countries and territories, 1990-2019: a systematic analysis from the Global Burden of Disease Study 2019. Lancet Glob Health. 2022;10(12):e1715-43.

World Health Organization. Ethics and governance of artificial intelligence for health: WHO guidance. Geneva: World Health Organization. 2021. Available at: https://www.who.int/publications/i/item/ 9789240029200. Accessed on 3 June 2026.

Haider SI. Integrating artificial intelligence for patient safety in Pakistan: opportunities, challenges, and lessons from low- and middle-income countries. J Coll Physicians Surg Pak. 2025;35(6):679-80.

Amjad AI, Aslam S. E-Health implementation in Pakistan: challenges, opportunities, and the path forward. Front Digit Health. 2026;8:1747740.

Köchling A, Wehner MC. Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development. Bus Res. 2020;13(3):795-848.

Albaroudi E, Mansouri T, Alameer A. A comprehensive review of AI techniques for addressing algorithmic bias in job hiring. AI. 2024;5(1):383-404.

Stark L, Stanhaus A, Anthony DL. "I don't want someone to watch me while I'm working": gendered views of facial recognition technology in workplace surveillance. J Assoc Inf Sci Technol. 2020;71(9):1074-88.

Wang X, Wu YC, Zhou M, Fu H. Beyond surveillance: privacy, ethics, and regulations in face recognition technology. Front Big Data. 2024;7:1337465.

Trabelsi Z, Alnajjar F, Parambil MMA, Gochoo M, Ali L. Real-time attention monitoring system for classroom: a deep learning approach for student's behavior recognition. Big Data Cogn Comput. 2023;7(1):48.

Nahar N, Ruparel RH, Kabir S, Khan ST, Saha S, Rashid M. AmarDoctor: an AI-driven, multilingual, voice-interactive digital health application for primary care triage and patient management to bridge the digital health divide for Bengali speakers. arXiv:2510.24724. 2025.

Khurram SS, Aga IZ, Muzzamil M, Karim M, Hashmi S. Transitioning from crisis to continuity post-COVID-19 pandemic: adoption of telehealth by Sehat Kahani healthcare providers, Karachi, Pakistan. Telehealth Med Today. 2024;9(6):1-7.