Applications of natural language processing in ophthalmology: present and future

Abstract

Advances in technology, including novel ophthalmic imaging devices and adoption of the electronic health record (EHR), have resulted in significantly increased data available for both clinical use and research in ophthalmology. While artificial intelligence (AI) algorithms have the potential to utilize these data to transform clinical care, current applications of AI in ophthalmology have focused mostly on image-based deep learning. Unstructured free-text in the EHR represents a tremendous amount of underutilized data in big data analyses and predictive AI. Natural language processing (NLP) is a type of AI involved in processing human language that can be used to develop automated algorithms using these vast quantities of available text data. The purpose of this review was to introduce ophthalmologists to NLP by (1) reviewing current applications of NLP in ophthalmology and (2) exploring potential applications of NLP. We reviewed current literature published in Pubmed and Google Scholar for articles related to NLP and ophthalmology, and used ancestor search to expand our references. Overall, we found 19 published studies of NLP in ophthalmology. The majority of these publications (16) focused on extracting specific text such as visual acuity from free-text notes for the purposes of quantitative analysis. Other applications included: domain embedding, predictive modeling, and topic modeling. Future ophthalmic applications of NLP may also focus on developing search engines for data within free-text notes, cleaning notes, automated question-answering, and translating ophthalmology notes for other specialties or for patients, especially with a growing interest in open notes. As medicine becomes more data-oriented, NLP offers increasing opportunities to augment our ability to harness free-text data and drive innovations in healthcare delivery and treatment of ophthalmic conditions.

Utility of artificial intelligence‐based large language models in ophthalmic care

“…The two subfields of AI are machine learning and natural language processing (NLP). 3 Machine learning requires 'supervised learning' where experts label and grade individual features and severity from images to develop the AI. A subset of machine learning is deep learning that shows promise in disease screening, diagnosis, risk stratification, treatment monitoring and improved patient care for eyes with myopia, 4 optic disc abnormalities (e.g., glaucoma, papilledema), [5][6][7] retinal diseases (e.g., age-related macular degeneration, diabetic retinopathy), 2 cataract 2 and corneal disorders.…”

“…NLP could transform human language (free text) or image into code that computers understand, and has been primarily used to date for information retrieval and text extraction. 3 However, NLP is susceptible to error due to the variable nature of human-generated natural text and limited by the requirement of a huge data set for training NLP models which may or may not utilise deep learning or machine learning. Moreover, NLPs are often trained in specific domains, that impact how word embeddings, which is a method of extracting features out of text, based on the distance between two words, interpret relationships between words in different contexts.…”

“…Current applications of AI in ophthalmology are mostly focused on image‐based techniques used for image analysis, recognition and diagnosis of ophthalmic diseases using ophthalmic data from fundus photography and optical coherence tomography images. The two subfields of AI are machine learning and natural language processing (NLP) 3 . Machine learning requires ‘supervised learning’ where experts label and grade individual features and severity from images to develop the AI.…”

“…NLP, the other subset of AI, is focused on extracting and processing text data that include written and spoken words. NLP could transform human language (free text) or image into code that computers understand, and has been primarily used to date for information retrieval and text extraction 3 . However, NLP is susceptible to error due to the variable nature of human‐generated natural text and limited by the requirement of a huge data set for training NLP models which may or may not utilise deep learning or machine learning.…”

Optimizing the Clinical Direction of Artificial Intelligence With Health Policy: A Narrative Review of the Literature

“…Natural language processing (NLP) is a field in AI that focuses on analyzing and processing text data, including written and spoken words. In simple terms, NLP allows computers to understand speech and text data and enables them to perform tasks such as language translation, speech recognition, text summarization, question answering, and many more [ 11 ]. Big data refers to large and complex data sets that exceed the scope of traditional data processing methods [ 12 ].…”