Paper Type
Complete
Abstract
Digital innovations affect people's ability to stay healthy in underserved communities as resources are difficult to access. Artificial Intelligence (AI) can support access to socio-economic resources in mobile health applications. This paper investigates: How can generative AI enable access to socio-economic resources? Following a theoretical background this paper employs design science research to offer an improvement or new solution to known problems using generative artificial intelligence to support people in underserved communities looking for resources. The contribution of this paper lies in design science research that creates a “build-and-evaluate loop,” in which a problem is determined, a solution is designed, the solution is evaluated, and the solution is applied after training the model. The contribution of this paper is in offering a level 1 situated implementation of the artifact based on instantiations on the mhealthhelp.com application, which leads to the level 2 nascent design of the AI architecture.
Paper Number
1901
Recommended Citation
Qureshi, Sajda; Oladokun, Blessing Elijah; and Nadendla, Kavya, "Digital Innovations for Development: Design of Artificial Intelligence in Mobile Health" (2024). AMCIS 2024 Proceedings. 10.
https://aisel.aisnet.org/amcis2024/ict_global/ict_global/10
Digital Innovations for Development: Design of Artificial Intelligence in Mobile Health
Digital innovations affect people's ability to stay healthy in underserved communities as resources are difficult to access. Artificial Intelligence (AI) can support access to socio-economic resources in mobile health applications. This paper investigates: How can generative AI enable access to socio-economic resources? Following a theoretical background this paper employs design science research to offer an improvement or new solution to known problems using generative artificial intelligence to support people in underserved communities looking for resources. The contribution of this paper lies in design science research that creates a “build-and-evaluate loop,” in which a problem is determined, a solution is designed, the solution is evaluated, and the solution is applied after training the model. The contribution of this paper is in offering a level 1 situated implementation of the artifact based on instantiations on the mhealthhelp.com application, which leads to the level 2 nascent design of the AI architecture.
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