Governing AI for Pharmacovigilance in Low-Income Countries: Systems Perspective
Journal of Medical Internet Research ·
Gaps in pharmaceutical governance could widen with the adoption of AI, even as AI promises better pharmacovigilance in low-income countries (LICs). While advanced regulatory systems like Australia’s are integrating AI into pharmaceutical governance, LICs with underdeveloped regulatory capabilities, such as South Sudan, lag behind. The potential divergence disorients the World Health Organization’s “Medicine Without Harm” agenda and effective global pharmacovigilance. Moreover, evolving global governance initiatives, including the newly established United Nations scientific panel on AI, may be hampered by this global divergence in capabilities. This makes 3 critical interrelated questions: what are the moral trade-offs in the introduction of AI in health care, what power dynamics impact the introduction of AI into health systems, and how could AI be used for pharmacovigilance in LICs? This viewpoint aims at informing global policies and regulations on AI in pharmacovigilance. It uses clinical, policy, and regulatory practitioner insights to synthesize evidence on the ethical, economic, and clinical contours of AI in pharmacovigilance. It contrasts the high-income context of Australia with the low-income context of South Sudan and shows that national capabilities are instrumental for institutionalizing global practice. It identifies current ethical challenges with applying AI and digital health, which straddle epistemic, normative, and metaethical domains, such as misguidance, cultural devaluation, and trust deficit. These filter into demerits observed with current applications of AI to pharmacovigilance, from the detection of adverse drug events and adverse drug reactions to the simulation of clinical trials. The merits of current applications are multiple and depend on data quality, ranging from the detection of adverse drug reactions to real-time surveillance of medical errors and predictive application to population risk quantification of adverse drug events. The widening gaps in global capabilities amid rapid evolution of AI suggest the need for inclusive global governance in the early stages, especially because AI may be deterministic and effects may not be retrospectively surmountable. The viewpoint also assesses the sufficiency of current evaluation frameworks, noting that health economic models currently lag in capturing gains and losses from the adoption of AI in health systems, digital health frameworks are largely retrospective and overlook sociopolitical and financial contexts, and influential service-oriented frameworks for health systems overlook outcomes. It observes that, although AI could be harnessed across the breadth of the pharmaceutical system, effective evaluation of potential risks is hampered by upstream decisions in software development and procurement, which preclude aspects of subsequent application. This introduces inscrutability and weakens clinicians’ role in risk adjudication, which may worsen with nonrepresentative evolution of AI. Using these insights and a case study on the low-income context of South Sudan, the viewpoint commends an integrated health systems framework and country-level investments in infrastructure and regulatory capabilities as requisites for effective global governance and equitable use of AI in pharmacovigilance.
Gaps in pharmaceutical governance could widen with the adoption of AI, even as AI promises better pharmacovigilance in low-income countries (LICs). While advanced regulatory systems like Australia’s are integrating AI into pharmaceutical governance, LICs with underdeveloped regulatory capabilities, such as South Sudan, lag behind. The potential divergence disorients the World Health Organization’s “Medicine Without Harm” agenda and effective global pharmacovigilance. Moreover, evolving global governance initiatives, including the newly established United Nations scientific panel on AI, may be hampered by this global divergence in capabilities. This makes 3 critical interrelated questions: what are the moral trade-offs in the introduction of AI in health care, what power dynamics impact the introduction of AI into health systems, and how could AI be used for pharmacovigilance in LICs? This viewpoint aims at informing global policies and regulations on AI in pharmacovigilance. It uses clinical, policy, and regulatory practitioner insights to synthesize evidence on the ethical, economic, and clinical contours of AI in pharmacovigilance. It contrasts the high-income context of Australia with the low-income context of South Sudan and shows that national capabilities are instrumental for institutionalizing global practice. It identifies current ethical challenges with applying AI and digital health, which straddle epistemic, normative, and metaethical domains, such as misguidance, cultural devaluation, and trust deficit. These filter into demerits observed with current applications of AI to pharmacovigilance, from the detection of adverse drug events and adverse drug reactions to the simulation of clinical trials. The merits of current applications are multiple and depend on data quality, ranging from the detection of adverse drug reactions to real-time surveillance of medical errors and predictive application to population risk quantification of adverse drug events. The widening gaps in global capabilities amid rapid evolution of AI suggest the need for inclusive global governance in the early stages, especially because AI may be deterministic and effects may not be retrospectively surmountable. The viewpoint also assesses the sufficiency of current evaluation frameworks, noting that health economic models currently lag in capturing gains and losses from the adoption of AI in health systems, digital health frameworks are largely retrospective and overlook sociopolitical and financial contexts, and influential service-oriented frameworks for health systems overlook outcomes. It observes that, although AI could be harnessed across the breadth of the pharmaceutical system, effective evaluation of potential risks is hampered by upstream decisions in software development and procurement, which preclude aspects of subsequent application. This introduces inscrutability and weakens clinicians’ role in risk adjudication, which may worsen with nonrepresentative evolution of AI. Using these insights and a case study on the low-income context of South Sudan, the viewpoint commends an integrated health systems framework and country-level investments in infrastructure and regulatory capabilities as requisites for effective global governance and equitable use of AI in pharmacovigilance.