AI Medical Compendium Journal:
Clinical & experimental optometry

Showing 1 to 5 of 5 articles

Evaluating the reliability of the responses of large language models to keratoconus-related questions.

Clinical & experimental optometry
CLINICAL RELEVANCE: Artificial intelligence has undergone a rapid evolution and large language models (LLMs) have become promising tools for healthcare, with the ability of providing human-like responses to questions. The capabilities of these tools ...

Talking technology: exploring chatbots as a tool for cataract patient education.

Clinical & experimental optometry
CLINICAL RELEVANCE: Worldwide, millions suffer from cataracts, which impair vision and quality of life. Cataract education improves outcomes, satisfaction, and treatment adherence. Lack of health literacy, language and cultural barriers, personal pre...

Accuracy of a Large Language Model as a new tool for optometry education.

Clinical & experimental optometry
CLINICAL RELEVANCE: The unsupervised introduction of certain Artificial Intelligence tools in optometry education may challenge the proper acquisition of accurate clinical knowledge and skills proficiency.

What do individuals with visual impairment need and want from a dialogue-based digital assistant?

Clinical & experimental optometry
CLINICAL SIGNIFICANCE: Optometrists are well-placed to provide helpful advice and guidance to patients with visual impairment but may not know how best to do this. The availability of a reliable and comprehensive conversational agent to which patient...

Deep learning: applications in retinal and optic nerve diseases.

Clinical & experimental optometry
Deep learning (DL) represents a paradigm-shifting, burgeoning field of research with emerging clinical applications in optometry. Unlike traditional programming, which relies on human-set specific rules, DL works by exposing the algorithm to a large ...