Human data interactions in digital modes of eye care

In eye care, work on developing algorithms to improve the early detection and diagnosis of diseases is underway. The use of artificial intelligence in clinical decision making has transformed the landscape of Big Data, inevitably changing the scope of practice of ophthalmic providers, while also impacting student learning at the pre-professional level. This data needs to be used in meaningful and ethical ways. These innovations intend to revolutionize medicine through combining ancestry, demographic, environmental, image based and genetic data to enable accurate classification of eye health and disease by accounting for the diversity of the human population, including efforts to develop individualized data driven care. This chapter will review new models that utilize human data interactions and provide specific eye conditions as examples. Further framework advances in which teleophthalmology services are delivered will be presented. The need for monitoring of patient safety, privacy, traceability, accountability, and security, with plans in place to address any breaches will be highlighted. Challenges for validation, clinical implementation, postdigital adaptation, and recommendations on future directions, gaps, and unmet clinical needs will also be discussed, as they relate to eye care.