Dear Editor, We thank the authors, Gurnani et al., for reading our published work on “Modeling and mitigating human annotations to design processing systems with human-in-the-loop machine learning for glaucomatous defects: The future in artificial intelligence” and stressing how data annotators are the unacclaimed heroes of the artificial intelligence (AI) revolution in ophthalmology.[12] We do agree with Gurnani et al. and would like to add the following important points on how data annotation is a game changer in ophthalmic AI and warrants further exploration. “Big data” has become the buzzword of the decade and has led to breakthroughs for major technology tycoons like Google. But unlike the technology giants, most ophthalmic set-ups are limited to “small data”. And for creating a successful ophthalmic AI model, it is imperative to have a huge dataset.[3] Small data makes the data comprehensible for humans and can be easily stored on local servers and laptops, whereas big data has an insane amount of structured and unstructured data that cannot be analyzed using conventional processing methods, which requires complex algorithms.[4] Considering how data annotation is an important armament in creating the AI algorithm, the majority of the ophthalmic practitioners, irrespective of the magnitude of the set-up, can make massive progress with small data as it makes it more feasible for annotations.[5] Moreover, there are ways in which you can deliver small data in a big way, by annotating every single clinical finding intricately, such as splinter hemorrhage, laminar dot sign, bayoneting sign, etc., for AI diagnostics. Also, certain techniques such as transfer learning and collective learning have helped transform small data into big data. Transfer learning encompasses the transfer of knowledge via open-sourced machine learning models from one domain to another domain with fewer data. Collective learning is the process by which multiple individual companies with small data, use a third-party AI and consolidate their data to create a sophisticated AI model.[346] To summarize, the lack of big data should not be a deterrent to venture into ophthalmic AI, and it is time to wield the tools at hand to create a successful AI model in ophthalmology with customized annotations. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.