From Theoretical Models to Practical Deployment: A Perspective and Case Study of Opportunities and Challenges in AI-driven Cardiac Auscultation Research for Low-Income Settings

Abstract

This paper critically explores the opportunities and challenges of deploying Artificial Intelligence (AI) in healthcare. This study has two parallel components: (1) A narrative literature summary, which assesses the capacity of AI to aid in addressing the observed disparity in healthcare between high- and low-income countries. Despite the development of machine learning models for a wide range of diseases, many are never deployed in practice. We highlight various challenges that contribute to the lack of deployed models. A main challenge that is not always sufficiently addressed in the literature is the evaluation of model generalisation. For example, by using a multi-site set-up with test sets that were collected separately to the train and validation sets, or by using evaluation metrics which are both understandable and clinically applicable. Moreover, we discuss how the emerging trend of human-centred deployment research is a promising avenue for overcoming barriers towards deployment. (2) A case study on developing and evaluating a predictive AI model tailored for low-income environments. The focus of this case study is heart murmur detection in rural Brazil. Our Binary Bayesian ResNet model leverages overlapping log mel spectrograms of patient heart sound recordings and integrates demographic data and signal features via XGBoost to optimise performance. We discuss the model’s limitations, its robustness, and the obstacles preventing its practical application. We especially highlight how our model, and other state-of-the-art models, struggle to generalise to out-of-distribution data. The research accentuates the transformative potential of AI-enabled healthcare, particularly affordable point-of-care monitoring systems, in low-income settings. It also emphasises the necessity for effective implementation and integration strategies to guarantee the successful deployment of these technologies.

Ai-Driven Environmental Health Disease Modeling: A Review of Techniques and Their Impact on Public Health in the Usa and African Contexts

“…The study found the system valuable and feasible, but also identified implementation issues, highlighting the challenges in integrating digital health solutions into existing healthcare models. Krones and Walker (2023) discuss the deployment of AI in healthcare, particularly in lowincome settings, through a case study on heart murmur detection in rural Brazil. Their Binary Bayesian ResNet model, which integrates patient heart sound recordings with demographic data, faced challenges in generalizing to out-of-distribution data.…”

“…AI in healthcare presents a landscape of opportunities and challenges. The case studies by Perry et al (2021), Krones andWalker (2023), andFirouzi et al (2022) provide valuable insights into the practical applications of AI in healthcare, highlighting both its transformative potential and the hurdles that need to be overcome. As AI continues to evolve, addressing these challenges will be crucial for maximizing its benefits in improving healthcare outcomes.…”