Sep 2026· Current Opinion in Ophthalmology· 0 citations· 33 references
Medicine
TL;DR
This review examines vibe coding through an ophthalmology-specific lens, identifying where it adds value and how it can be used responsibly, and how it can be used responsibly while preserving safety and accountability.
Abstract
Purpose
OF REVIEW
Recent advances in large language models (LLMs) have given rise to 'vibe coding', a conversational approach to software development in which users describe desired functionality in natural language and collaborate with an LLM agent to generate functional code. This review examines vibe coding through an ophthalmology-specific lens, identifying where it adds value and how it can be used responsibly.
RECENT
Findings
Vibe coding platforms now enable nonprogrammers to create fully functional applications, including user authentication and database integration, from natural language prompts alone. In ophthalmology, clinicians may apply this approach to build clinical workflow, research and medical education tools. These applications are most effective when directed at niche, subspecialty-specific problems that are too small or too specialized to attract commercial development.
SUMMARY
Vibe coding provides ophthalmologists with a pragmatic pathway to translate unmet needs into executable prototypes. For low-risk applications that do not directly influence clinical decision-making, clinicians may in certain settings be able to deploy vibe-coded tools independently. For higher risk applications, vibe coding does not replace professional software development, but it enables experimentation, clarifies user needs, and makes ideas more tangible. With appropriate guardrails, vibe coding can accelerate clinician-led innovation while preserving safety and accountability.
This review highlights the promise of LLMs in enhancing decision support, workflow efficiency, research productivity, and patient communication in spine care, while emphasizing the need for interdisciplinary collaboration, robust evaluation metrics, and governance frameworks that prioritize patient safety and equity.
Fabio Galbusera, Andrea Cina· European spine journal· 0 citations
In the current environment, LLMs have the potential to augment cytopathology practice, but responsible adoption requires rigorous validation and sustained collaboration among cytopathologists, AI researchers, and regulatory bodies.
K. Bilal, Joanna A Gibson, David Kim et al.· Cancer Cytopathology· 1 citation
Objective: To assess the accuracy and efficiency of a large language model (LLM) using two prompt strategies to extract structured data from ophthalmic diagnostic PDF reports. Methods: Twenty deidentified reports across four types (Visual Field, OCT Glaucoma Overview, OCT retinal nerve fiber layer Single Exam, and OCT...
Umair A. Zaidi, An-Lun Wu, Wei-Chun Lin et al.· 0 citations
Current frontier LLMs show strong recommendation generation but substantial limitations in audiometric numerical interpretation that are shared across models and that an automated adjudicator partially miscalibrated at the per-item level.
Lin-Kai Li, Chang-Geng Mo, Hao-Shuai Zhou et al.· Journal of Medical Internet...· 1 citation
A fully on-device AI pipeline using open-source Automatic Speech Recognition and Large Language Models that transcribes breast cancer MDT discussions, structures clinical information, and generates treatment recommendations using retrieval-augmented generation (RAG) grounded in National Institute for Health and Care Ex...
Aarzoo Dhiman, F. Haque, I. Muazzam et al.· 0 citations
BACKGROUND
Clinical documentation accounts for a substantial proportion of ophthalmologists' working time and increasingly consumes scarce resources. Ambient scribe systems, which automatically transcribe clinical consultations and generate structured clinical notes, could reduce the documentation burden. To date, data...
J. Terheyden, Simon Haas, Corinna Schmalohr et al.· Die Ophthalmologie· 0 citations
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