Bias and Non-Diversity of Big Data in Artificial Intelligence: Focus on Retinal Diseases

Abstract

Artificial intelligence (AI) applications in healthcare will have a potentially far-reaching impact on patient care, however issues regarding algorithmic bias and fairness have recently surfaced. There is a recognized lack of diversity in the available ophthalmic datasets, with 45% of the global population having no readily accessible representative images, leading to potential misrepresentations of their unique anatomic features and ocular pathology. AI applications in retinal disease may show less accuracy with underrepresented populations that may further widen the gap of health inequality if left unaddressed. Beyond disease symptomatology, social determinants of health must be integrated into our current paradigms of disease understanding, with the goal of more personalized care. AI has the potential to decrease global healthcare inequality, but it will need to be based on a more diverse, transparent and responsible use of healthcare data.

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