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

Abstract This article includes a literature review and a case study of artificial intelligence (AI) heart murmur detection models to analyse the opportunities and challenges in deploying AI in cardiovascular healthcare in low- or medium-income countries (LMICs). This study has two parallel components: The literature review assesses the capacity of AI to aid in addressing the observed disparity in healthcare between high- and low-income countries. Reasons for the limited deployment of machine learning models are discussed, as well as model generalisation. Moreover, the literature review discusses how emerging human-centred deployment research is a promising avenue for overcoming deployment barriers. A predictive AI screening model is developed and tested in a case study on 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. This is followed by a discussion of the model’s limitations, its robustness, and the obstacles preventing its practical application. The difficulty with which this model, and other state-of-the-art models, generalise to out-of-distribution data is also discussed. By integrating the results of the case study with those of the literature review, the NASSS framework was applied to evaluate the key challenges in deploying AI-supported heart murmur detection in low-income settings. The research accentuates the transformative potential of AI-enabled healthcare, particularly for affordable point-of-care screening systems in low-income settings. It also emphasises the necessity of effective implementation and integration strategies to guarantee the successful deployment of these technologies. Author Summary This study explores the potential and limitations of artificial intelligence (AI) in healthcare, focusing on its role in addressing global health inequities. Non-communicable diseases, especially cardiovascular disorders, are a leading global cause of death, exacerbated in low-income settings due to restricted healthcare access. This research has two components: a narrative literature summary that discusses the gap between AI research and real-world applications, and a case study on heart murmur detection in rural Brazil. The case study introduces an AI model tailored for low-income environments, which efficiently analyses heart sound recordings for diagnostic insights. Both parts highlight the challenges of model generalisation to out-of-distribution data. The findings accentuate the capacity of AI to revolutionise point-of-care screening in resource-limited settings. However, they also highlight the critical importance of effective implementation and conscientious design for the successful deployment of these technologies. By leveraging AI, this work contributes to the broader objective of fostering global health equity, while emphasising the need for thoughtful application and integration strategies.

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