Rethinking lay consultation networks in the AI age: reflections on empirical evidence from informal settlements in Nigeria
1
School of Psychology, Social Work and Public Health, Oxford Brookes University, Oxford, United Kingdom
Popul. Med. 2026;8(Supplement Supplement 1):A906
ABSTRACT
BACKGROUND:
In informal urban settlements of Low-and-Middle-Income Countries (LMICs), health decision-making is shaped by lay consultation networks involving family members, neighbours, and trusted community actors1. Previous empirical studies in Nigeria documented the composition, use and influence of these networks2,3. However, this evidence predates the rapid expansion of Large Language Models (LLMs) e.g. ChatGPT. As AI is increasingly positioned to solve health information gaps, this paper reflects on empirical evidence on lay consultation behaviours, and how the use of LLMs for health advice-seeking may intersect with existing social realities in informal urban settings.
METHODS:
This paper adopts an empirically grounded reflective approach4. It draws on findings from a previously published mixed-methods study among adults (survey: n=480; interviews: n=30) in two informal urban settlements in Nigeria1,2. The reflection is guided by a conceptual framework developed from the original study, describing how lay consultation networks mediate health advice exchange and treatment-seeking decisions. The framework situates these empirical insights within ongoing public health debates on AI for public good, community readiness, and equity.
RESULTS:
The original findings highlighted the importance of trusted intermediaries, complexities of social life, and personal and community agency in shaping health advice-seeking. Linking the framework to current AI narratives, these findings suggest that the use of LLM for health advice-seeking in informal settlements will be shaped by existing relational and health-seeking structures. LLM information is likely to be filtered, interpreted, and legitimised through lay consultation networks. Community values such as “Ubuntu” (interconnectedness and solidarity), and infrastructural issues such as weak primary healthcare systems and disparities in digital health access will shape the public good derived from LLMs in informal urban settings.
CONCLUSIONS:
Lay consultation networks remain part of the everyday management of health. AI-driven health information interventions must consider local realities, structures, and systems.
In informal urban settlements of Low-and-Middle-Income Countries (LMICs), health decision-making is shaped by lay consultation networks involving family members, neighbours, and trusted community actors1. Previous empirical studies in Nigeria documented the composition, use and influence of these networks2,3. However, this evidence predates the rapid expansion of Large Language Models (LLMs) e.g. ChatGPT. As AI is increasingly positioned to solve health information gaps, this paper reflects on empirical evidence on lay consultation behaviours, and how the use of LLMs for health advice-seeking may intersect with existing social realities in informal urban settings.
METHODS:
This paper adopts an empirically grounded reflective approach4. It draws on findings from a previously published mixed-methods study among adults (survey: n=480; interviews: n=30) in two informal urban settlements in Nigeria1,2. The reflection is guided by a conceptual framework developed from the original study, describing how lay consultation networks mediate health advice exchange and treatment-seeking decisions. The framework situates these empirical insights within ongoing public health debates on AI for public good, community readiness, and equity.
RESULTS:
The original findings highlighted the importance of trusted intermediaries, complexities of social life, and personal and community agency in shaping health advice-seeking. Linking the framework to current AI narratives, these findings suggest that the use of LLM for health advice-seeking in informal settlements will be shaped by existing relational and health-seeking structures. LLM information is likely to be filtered, interpreted, and legitimised through lay consultation networks. Community values such as “Ubuntu” (interconnectedness and solidarity), and infrastructural issues such as weak primary healthcare systems and disparities in digital health access will shape the public good derived from LLMs in informal urban settings.
CONCLUSIONS:
Lay consultation networks remain part of the everyday management of health. AI-driven health information interventions must consider local realities, structures, and systems.
| eISSN: | 2654-1459 |
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