Case Study: AI for Pneumonia detection in LMICs
This case study will illustrate how Artificial Intelligence (AI) systems using symptoms/signs to detect respiratory diseases may improve early diagnosis especially in limited resource settings (LRS). Intelligible machine learning models can enhance symptom-based referral of respiratory disease such as pneumonia and bronchitis in LRS and in community settings. However, heterogeneity in such AI systems creates an ongoing need to analyze performance to inform future research. Case studies will be presented to demonstrate that there is strong evidence to support further investigations of AI systems to automatically detect respiratory disease based on easily recognizable symptoms and signs. To help improve the efficacy of future research, recommendations for designing and implementing AI tools based on the findings of these case studies will be provided during the lecture.
Lecture video: sorry, no video available at this time