Artificial intelligence for home monitoring devices

Abstract

Purpose of review Home monitoring in ophthalmology is appropriate for disease stages requiring frequent monitoring or rapid intervention, for example, neovascular age-related macular degeneration (AMD) and glaucoma, where the balance between frequent hospital attendance versus risk of late detection is a constant challenge. Artificial intelligence approaches are well suited to address some challenges of home monitoring. Recent findings Ophthalmic data collected at home have included functional (e.g. perimetry), biometric (e.g. intraocular pressure), and imaging [e.g. optical coherence tomography (OCT)] data. Potential advantages include early detection/intervention, convenience, cost, and visual outcomes. Artificial intelligence can assist with home monitoring workflows by handling large data volumes from frequent testing, compensating for test quality, and extracting useful metrics from complex data. Important use cases include machine learning applied to hyperacuity self-testing for detecting neovascular AMD and deep learning applied to OCT data for quantifying retinal fluid. Summary Home monitoring of health conditions is useful for chronic diseases requiring rapid intervention or frequent data sampling to decrease risk of irreversible vision loss. Artificial intelligence may facilitate accurate, frequent, large-scale home monitoring, if algorithms are integrated safely into workflows. Clinical trials and economic evaluations are important to demonstrate the value of artificial intelligence-based home monitoring, towards improved visual outcomes.

AI-based support for optical coherence tomography in age-related macular degeneration

“…Home monitoring OCT has emerged as an innovative technological paradigm aimed at optimizing the surveillance of individuals affected by chronic sight-threatening pathologies, notably those requiring recurrent monitoring and fast intervention, such as nAMD [ 74 ]. Home screening tests for nAMD are not a novelty.…”

“…Home screening tests for nAMD are not a novelty. Amsler grid and preferential hyperacuity perimetry has been previously proposed to detect metamorphopsia as an early sign of choroidal neovascularization in AMD patients [ 74 , 75 ]. The current development of an AI-based fluid monitoring algorithm implemented on a home OCT device (The Notal Vision Home OCT system) showed promising results with feasible self-scan rates.…”

Accuracy of an Artificial Intelligence Chatbot’s Interpretation of Clinical Ophthalmic Images

“…Artificial intelligence (AI) chatbots may have transformative potential in ophthalmology, given their capability to reshape patient engagement and health care provision . Previous literature has highlighted their ability to offer high-diagnostic accuracy, contribute to patient education, enable remote monitoring of chronic eye conditions, and ease burden on health care professionals . Nevertheless, addressing regulatory compliance, privacy concerns, and the seamless integration of AI chatbots within healthcare systems necessitates further exploration.…”

“…1 Previous literature has highlighted their ability to offer high-diagnostic accuracy, contribute to patient education, enable remote monitoring of chronic eye conditions, and ease burden on health care professionals. [2][3][4] Nevertheless, addressing regulatory compliance, privacy concerns, and the seamless integration of AI chatbots within healthcare systems necessitates further exploration. There has been immense interest in cutting-edge large language models (LLMs), particularly ChatGPT-4 (OpenAI), given its capacity for real-time analysis of medical prompts.…”