ReviewJournal of personalized medicine2023
Personalized Care in Eye Health: Exploring Opportunities, Challenges, and the Road Ahead for Chatbots.
Review in Journal of personalized medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Integrating ChatGPT to support children and adolescents with diabetes mellitus in South African healthcare: Perspectives of healthcare professionals and academics.African journal of primary health care & family medicine · 2026Article
- AI-Augmented Teach-Back in Dentistry: From Patient Education to Verified Clinical Understanding.Cureus · 2026Review
- Unseen Insights: An AI-Powered Exploration of Secure Patient Messages in Ophthalmology.medRxiv : the preprint server for health sciences · 2026Article
- Bridging agronomic science and context specific farm-level advisory through generative AI for rice systems in India.Frontiers in artificial intelligence · 2026Review
- Benchmark analysis of myopia-related issues using large language models: a comparison of ChatGPT-4o and deepseek.BMC ophthalmology · 2025Article
- Review
- Comparison of artificial intelligence systems in answering prosthodontics questions from the dental specialty exam in Turkey.Journal of dental sciences · 2025Article
- Article
- Article
- Evaluation and comparison of large language models' responses to questions related optic neuritis.Frontiers in medicine · 2025Article
- Review
- Joint Expedition: Exploring the Intersection of Digital Health and AI in Precision Medicine with Team Integration.Journal of personalized medicine · 2024Article
- Large language models and eye education: A narrative review.Oman journal of ophthalmologyReview
- Information security and confidentiality in health chatbots: A scoping review and development of a conceptual model.Digital healthReview
- Artificial intelligence and big data integration in anterior segment imaging for glaucoma.Taiwan journal of ophthalmologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In modern eye care, the adoption of ophthalmology chatbots stands out as a pivotal technological progression. These digital assistants present numerous benefits, such as better access to vital information, heightened patient interaction, and streamlined triaging. Recent evaluations have highlighted their performance in both the triage of ophthalmology conditions and ophthalmology knowledge assessment, underscoring their potential and areas for improvement. However, assimilating these chatbots into the prevailing healthcare infrastructures brings challenges. These encompass ethical dilemmas, legal compliance, seamless integration with electronic health records (EHR), and fostering effective dialogue with medical professionals. Addressing these challenges necessitates the creation of bespoke standards and protocols for ophthalmology chatbots. The horizon for these chatbots is illuminated by advancements and anticipated innovations, poised to redefine the delivery of eye care. The synergy of artificial intelligence (AI) and machine learning (ML) with chatbots amplifies their diagnostic prowess. Additionally, their capability to adapt linguistically and culturally ensures they can cater to a global patient demographic. In this article, we explore in detail the utilization of chatbots in ophthalmology, examining their accuracy, reliability, data protection, security, transparency, potential algorithmic biases, and ethical considerations. We provide a comprehensive review of their roles in the triage of ophthalmology conditions and knowledge assessment, emphasizing their significance and future potential in the field.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.