Natural Language Processing in medicine and ophthalmology: A review for the 21st-century clinician

Abstract

Natural Language Processing (NLP) is a subfield of artificial intelligence that focuses on the interaction between computers and human language, enabling computers to understand, generate, and derive meaning from human language. NLP's potential applications in the medical field are extensive and vary from extracting data from Electronic Health Records -one of its most well-known and frequently exploited uses- to investigating relationships among genetics, biomarkers, drugs, and diseases for the proposal of new medications. NLP can be useful for clinical decision support, patient monitoring, or medical image analysis. Despite its vast potential, the real-world application of NLP is still limited due to various challenges and constraints, meaning that its evolution predominantly continues within the research domain. However, with the increasingly widespread use of NLP, particularly with the availability of large language models, such as ChatGPT, it is crucial for medical professionals to be aware of the status, uses, and limitations of these technologies.

Keywords: Artificial Intelligence; ChatGPT; Clinical Application; Large Language Models; Natural Language Processing; Ophthalmology.

Fig. 1.. Artificial Intelligence hierarchy.

While we can depict them in a hierarchical structure, such as AI > ML > Deep Learning > NLP/Generative AI > LLM, it is important to note that advancements in these fields continually reshape their relationships and boundaries.

Fig. 2.

Techniques of text representation in NLP.

Fig. 3.

Evolution of NLP techniques: from Bag of Words to Large Language Models.

Fig. 4.

Flow of Large Language Models’ training to generate Chatbots.

Fig. 5.

Applications of NLP in the clinical practice.