The Contribution of Artificial Intelligence in Achieving the Sustainable Development Goals (SDGs): What Can Eye Health Can Learn From Commercial Industry and Early Lessons From the Application of Machine Learning in Eye Health Programmes.

Nicholas Sawers,Andrew Bastawrous,Nigel Bolster

doi:10.3389/fpubh.2021.752049

Nicholas Sawers, Andrew Bastawrous + Show 1 more

Open Access

https://doi.org/10.3389/fpubh.2021.752049

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Abstract

Achieving The United Nations sustainable developments goals by 2030 will be a challenge. Researchers around the world are working toward this aim across the breadth of healthcare. Technology, and more especially artificial intelligence, has the ability to propel us forwards and support these goals but requires careful application. Artificial intelligence shows promise within healthcare and there has been fast development in ophthalmology, cardiology, diabetes, and oncology. Healthcare is starting to learn from commercial industry leaders who utilize fast and continuous testing algorithms to gain efficiency and find the optimum solutions. This article provides examples of how commercial industry is benefitting from utilizing AI and improving service delivery. The article then provides a specific example in eye health on how machine learning algorithms can be purposed to drive service delivery in a resource-limited setting by utilizing the novel study designs in response adaptive randomization. We then aim to provide six key considerations for researchers who wish to begin working with AI technology which include collaboration, adopting a fast-fail culture and developing a capacity in ethics and data science.

Highlights

We aim to look in detail at how artificial intelligence (AI) can be used in testing strategies and provide a case study within eye health where Machine Learning (ML) could be employed, and detail a set of considerations for researchers that are looking to expand into a novel way of optimizing their study designs
The Covid-19 pandemic has caused a slowdown in the advancements on public health, we can take this opportunity to take stock and evaluate where the biggest gains will be made in the 10 years
This article argues that the integration of ML alongside traditional study designs into public health can propel testing and sustained incremental gains championed by improvement science

Summary

INTRODUCTION

We aim to look in detail at how AI can be used in testing strategies and provide a case study within eye health where ML could be employed, and detail a set of considerations for researchers that are looking to expand into a novel way of optimizing their study designs These considerations focus on the initial stages of working with ML and beginning to plan to use study designs or a testing strategy that utilizes the technology. The interplay between patients, healthcare workers and support staff in a variety of different environments ensure outcomes are unpredictable This is especially true for large, complex systems that deal with a variety of health conditions, locations and demographics that require a deep understanding of the underlying frameworks to develop effective research approaches [45]. Can we apply industry-standard testing designs such as ML algorithms to improve service delivery of already proven interventions in our health systems as well as optimization of those systems?. Advocacy will be an important factor in the journey so we recommend bringing ML to team research meetings, topics of interest, and research conversations to involve the wider community and normalize the field, especially around the people centered advantages of technology, not just technological advancement itself

CONCLUSIONS

Findings

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