Deep learning-based natural language processing in ophthalmology: applications, challenges and future directions

Abstract

Purpose of reviewArtificial intelligence (AI) is the fourth industrial revolution in mankind's history. Natural language processing (NLP) is a type of AI that transforms human language, to one that computers can interpret and process. NLP is still in the formative stages of development in healthcare, with promising applications and potential challenges in its applications. This review provides an overview of AI-based NLP, its applications in healthcare and ophthalmology, next-generation use case, as well as potential challenges in deployment. Recent findingsThe integration of AI-based NLP systems into existing clinical care shows considerable promise in disease screening, risk stratification, and treatment monitoring, amongst others. Stakeholder collaboration, greater public acceptance, and advancing technologies will continue to shape the NLP landscape in healthcare and ophthalmology.

Etiology of Macular Edema Defined by Deep Learning in Optical Coherence Tomography Scans

“…As a comprehensive strategy to overcome these needs, the application of artificial intelligence (AI) arises to support the timely reading of diagnostic images, which usually exhibit several patterns that can be challenging to recognize even by expert evaluators. 13 , 14 A deep learning (DL) approach confers the inherent advantage of the optimized processing of a large amount of data in a very short time. 13 Abràmoff et al 15 conducted a pivotal study for automated diagnosis of diabetic retinopathy (DR), targeting superiority endpoints at sensitivity higher than 85% and specificity higher than 82.5%, becoming the first Food and Drug Administration–approved AI-based medical diagnostic algorithm.…”

“… 13 , 14 A deep learning (DL) approach confers the inherent advantage of the optimized processing of a large amount of data in a very short time. 13 Abràmoff et al 15 conducted a pivotal study for automated diagnosis of diabetic retinopathy (DR), targeting superiority endpoints at sensitivity higher than 85% and specificity higher than 82.5%, becoming the first Food and Drug Administration–approved AI-based medical diagnostic algorithm. These values serve as a reference to compare AI-based models with the performance of an expert ophthalmologist.…”

Orbital and eyelid diseases: The next breakthrough in artificial intelligence?

“…Artificial Intelligence (AI) simulates and extends human intelligence, and has been hailed as "the Future of Employment" (Yang et al, 2021). Long before the mid-twentieth century, the British scientist Alan Turing first predicted that machines could become intelligent , and in 1956, McCarthy introduced "AI" at the Dartmouth Conference (Dzobo et al, 2020).…”

Applications of natural language processing in ophthalmology: present and future

“…The majority of these applications within ophthalmology have focused on image-based AI including diagnosis of diabetic retinopathy ( 15 , 16 ), age-related macular degeneration ( 17 , 18 ), retinopathy of prematurity ( 19 , 20 ), and glaucoma ( 21 – 23 ), among others. Though structured datasets (such as extracted tabular data from EHRs) and large image datasets have been studied extensively in ophthalmic big data applications, far fewer AI studies in ophthalmology have utilized unstructured, or free-text, data such as EHR clinical notes from office visits ( 24 – 27 ). Because clinical notes represent the majority of provider documentation regarding each office visit, there remains a large amount of untapped free-text data (up to 80% of data in the EHR) that may be useful in predictive AI or analytics ( 28 ).…”

“…Within ML, modeling can be performed using neural networks, which have the ability to learn from large amounts of data without explicitly defined features, an area known as deep learning (DL) ( 33 , 36 ). While several NLP techniques that do not utilize modeling such as ML or DL exist, some NLP can be used to perform modeling with ML or DL, using free-text (raw or processed) as input rather than images or pre-defined features (i.e., tabular data, or data in tables) ( 27 , 37 ). This intersection of ML, DL, and NLP is shown in Figure 1 .…”