Chinasa T. Okolo

As researchers and technology companies rush in to develop AI applications that aid the health of marginalized communities, it is critical to consider the needs and perceptions of the community health workers (CHWs) and other stakeholders involved in integrating these AI applications into the essential healthcare services provided to low-resource communities. For this work, we piloted a 3-month study, the first of its kind, examining CHW perceptions of AI in rural India. Under review at CHI 2021.

People in Africa are burdened with 71% of the global distribution of infectious diseases, despite only making up 12% of the global population. A shortage of medical professionals and lack of access to basic healthcare contributes heavily to this disparity. The ubiquity of mobile phones and strong mobile network coverage across the African continent has led to a surge of mobile health (mHealth) applications that have been developed to identify a variety of infectious and tropical diseases. Our work involves leveraging computer vision and deep learning methods such as RNNs and motion magnification to detect fine-grained respiratory motions such as chest indrawing, coughing, and other symptoms associated with pneumonia with the goal of improving healthcare access throughout the Global South. Work in progress.

In healthcare, the role of AI is continually evolving and understanding the challenges its introduction poses on relationships between healthcare providers and patients will require a regulatory and behavioural approach that can provide a guiding base for all users involved. Our work proposes a framework to set guidelines for how AI-enabled technologies should be formally introduced to patients in healthcare settings. The core principles of this framework are set into 5 parts: Acceptability, Comfortability, Informed Consent, Privacy, and Security. Preliminary work acccepted to the Data4Good: Designing for Diversity and Development Workshop (AVI), expanded work under review at FAccT 2021.