Text-to-image artificial intelligence to aid clinicians in perceiving unique neuro-ophthalmic visual phenomena

Generative artificial intelligence in ophthalmology: current innovations, future applications and challenges

The rapid advancements in generative artificial intelligence are set to significantly influence the medical sector, particularly ophthalmology. Generative adversarial networks and diffusion models enable the creation of synthetic images, aiding the development of deep learning models tailored for specific imaging tasks. Additionally, the advent of multimodal foundational models, capable of generating images, text and videos, presents a broad spectrum of applications within ophthalmology. These range from enhancing diagnostic accuracy to improving patient education and training healthcare professionals. Despite the promising potential, this area of technology is still in its infancy, and there are several challenges to be addressed, including data bias, safety concerns and the practical implementation of these technologies in clinical settings.

AI in Neuro-Ophthalmology: Current Practice and Future Opportunities

BACKGROUND

Neuro-ophthalmology frequently requires a complex and multi-faceted clinical assessment supported by sophisticated imaging techniques in order to assess disease status. The current approach to diagnosis requires substantial expertise and time. The emergence of AI has brought forth innovative solutions to streamline and enhance this diagnostic process, which is especially valuable given the shortage of neuro-ophthalmologists. Machine learning algorithms, in particular, have demonstrated significant potential in interpreting imaging data, identifying subtle patterns, and aiding clinicians in making more accurate and timely diagnosis while also supplementing nonspecialist evaluations of neuro-ophthalmic disease.

EVIDENCE ACQUISITION

Electronic searches of published literature were conducted using PubMed and Google Scholar. A comprehensive search of the following terms was conducted within the Journal of Neuro-Ophthalmology: AI, artificial intelligence, machine learning, deep learning, natural language processing, computer vision, large language models, and generative AI.

RESULTS

This review aims to provide a comprehensive overview of the evolving landscape of AI applications in neuro-ophthalmology. It will delve into the diverse applications of AI, optical coherence tomography (OCT), and fundus photography to the development of predictive models for disease progression. Additionally, the review will explore the integration of generative AI into neuro-ophthalmic education and clinical practice.

CONCLUSIONS

We review the current state of AI in neuro-ophthalmology and its potentially transformative impact. The inclusion of AI in neuro-ophthalmic practice and research not only holds promise for improving diagnostic accuracy but also opens avenues for novel therapeutic interventions. We emphasize its potential to improve access to scarce subspecialty resources while examining the current challenges associated with the integration of AI into clinical practice and research.

GPT-4: a new era of artificial intelligence in medicine

GPT-4 is the latest version of ChatGPT which is reported by OpenAI to have greater problem-solving abilities and an even broader knowledge base. We examined GPT-4's ability to inform us about the latest literature in a given area, and to write a discharge summary for a patient following an uncomplicated surgery and its latest image analysis feature which was reported to be able to identify objects in photos. All things considered, GPT-4 has the potential to help drive medical innovation, from aiding with patient discharge notes, summarizing recent clinical trials, providing information on ethical guidelines, and much more.

GPT-4 and Ophthalmology Operative Notes

Operative notes are an essential piece of documentation made by an ophthalmic team following ocular or ophthalmic surgery. While the advanced language processing capabilities of GPT-4 can be utilized to enhance the comprehension of natural language in healthcare applications, the utilization of GPT-4 in writing operative notes is not explicitly referenced in the current literature. In this paper, we describe the potential applications for GPT-4 to write ophthalmic operative notes. It is imperative to acknowledge that despite the impressive capabilities exhibited by GPT-4, it remains constrained by certain limitations, and therefore, ought to be employed in tandem with human expertise and subjected to critical evaluation.

Generative Pre-Trained Transformers (GPT) and Space Health: A Potential Frontier in Astronaut Health During Exploration Missions

In anticipation of space exploration where astronauts are traveling away from Earth, and for longer durations with an increasing communication lag, artificial intelligence (AI) frameworks such as large language learning models (LLMs) that can be trained on Earth can provide real-time answers. This emerging technology may be helpful for acute medical emergencies, particularly in austere and distant space environments. In this manuscript, we provide an overview of generative pre-trained transformer (GPT) technology, a rapidly emerging AI technology, and implications, considerations, and limitations of such technology for space health.