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

Purpose of review Artificial 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 findings The 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. Healthcare has always endeavored to be patient centric and personalized. For AI-based NLP systems to become an eventual reality in larger-scale applications, it is pertinent for key stakeholders to collaborate and address potential challenges in application. Ultimately, these would enable more equitable and generalizable use of NLP systems for the betterment of healthcare and society.

Automated summary

Artificial intelligence (AI) is viewed as the fourth industrial revolution, with natural language processing (NLP) being a type of AI that transforms human language for computer interpretation. In healthcare, NLP holds promise for various applications but also presents potential challenges. Collaboration, acceptance, and technology advancement will shape the future of NLP in healthcare and ophthalmology, aiming for more equitable and generalizable use for societal benefit.