Artificial intelligence (AI) has comparable accuracy to ophthalmologists for diabetic retinopathy (DR) screening, yet its cost-effectiveness is crucial for implementation. Our review of 18 health economic analyses of AI versus manual grading for DR found significant methodological variation, with cost-utility analysis and Markov modelling being the commonest evaluation and modelling approaches, respectively. We identified three key considerations when appraising health economic analyses of AI-enabled DR screening: the importance of contextualised parameters including subgroup analysis, real-world data on adherence to ophthalmology follow-up, and the trade-off between diagnostic accuracy and cost-effectiveness. 39% of studies followed standardised reporting guidelines, and most did not consider improved follow-up after AI screening, potentially underestimating its economic value. Future evaluations should incorporate contextualised parameters, including adherence and regional data, and recognise that the most accurate diagnostic screening may not reflect the most cost-effective. Studies should follow updated reporting guidelines such as CHEERS-AI or PICOTS-ComTeC to improve methodological transparency.