International Journal of Applied Health Care Analytics

Artificial Intelligence in Healthcare Systems of Low- and Middle-Income Countries: Requirements, Gaps, Challenges, and Potential Strategies

Aravind Sasidharan Pillai

Principal Data Architect, Data Engineering, Cox Automotive Inc., USA. ORCID: 0000-0001-7139-2804. Alumnus, Master in Data Science, University of Illinois Urbana-Champaign.

https://orcid.org/0000-0001-7139-2804

Keywords: AI in healthcare, digital divide, health data privacy, healthcare infrastructure, LMIC challenges, strategic framework, sustainable growth

Abstract

Despite the widespread deployment of AI applications in high-income countries, their utilization in the healthcare systems of economically disadvantaged countries is still nascent. The integration of artificial intelligence (AI) within the healthcare sectors of low- and middle-income countries faces an array of challenges that require a thorough, strategic framework to encourage equitable and sustainable growth. This study argued that essential to this framework are prerequisites such as robust healthcare infrastructure; enhanced education and skills training in AI and healthcare-related domains; clear healthcare policy and regulatory frameworks; increased funding and investment in health AI technologies; and strengthened collaborations among government health agencies, the private sector, non-governmental health organizations, and international health bodies. However, significant obstacles including the digital divide, limited research and development in health technologies, challenges in health data collection, storage, and analysis, and the lack of comprehensive ethical and legal guidelines for health data present critical barriers to the effective adoption of AI in these regions. Additionally, addressing resource constraints, the brain drain of health professionals, societal skepticism towards health AI, and concerns over patient data privacy and security are needed to get AI's potential for health advancement in those countries. Proposed strategic measures specific to healthcare include embracing open-source frameworks and collaborative health projects, tailoring AI applications to meet specific local health needs, fostering healthcare ecosystems through innovation hubs and health incubators, experimenting with AI health innovations within regulatory sandboxes, and enhancing STEM education with specialized training in health AI to build a competent health workforce. This study stresses that the proposed strategies requires the concerted efforts of diverse stakeholders to ensure that AI development in the healthcare sectors of LMICs contributes to inclusive growth and benefits the entire spectrum of society.