Using artificial intelligence for diabetic retinopathy screening: Policy implications

Artificial intelligence (AI) has evolved over the last few years; its use in DR screening has been demonstrated in multiple evidences across the globe. However, there are concerns right from the data acquisition, bias in data, difficulty in comparing between different algorithm, challenges in machine learning, its application in different group of population, and human barrier to AI adoption in health care. There are also legal and ethical concerns related to AI. The tension between risks and concerns on one hand versus potential and opportunity on the other have driven a need for authorities to implement policies for AI in DR screening to address these issues. The policy makers should support and facilitate research and development of AI in healthcare, but at the same time, it has to be ensured that the use of AI in healthcare aligns with recognized standards of safety, efficacy, and equity. It is essential to ensure that algorithms, datasets, and decisions are auditable and when applied to medical care (such as screening, diagnosis, or treatment) are clinically validated and explainable. Policy frameworks should require design of AI systems in health care that are informed by real-world workflow and human-centric design. Lastly, it should be ensured that healthcare AI solutions align with all relevant ethical obligations, from design to development to use and to be delivered properly in the real world.