The neuro-ocular effects of long-duration spaceflight have been termed Spaceflight Associated Neuro-Ocular Syndrome (SANS) and are a potential challenge for future, human space exploration. The underlying pathogenesis of SANS remains ill-defined, but several emerging translational applications of terrestrial head-mounted, visual assessment technology and machine learning frameworks are being studied for potential use in SANS. To develop such technology requires close consideration of the spaceflight environment which is limited in medical resources and imaging modalities. This austere environment necessitates the utilization of low mass, low footprint technology to build a visual assessment system that is comprehensive, accessible, and efficient. In this paper, we discuss the unique considerations for developing this technology for SANS and translational applications on Earth. Several key limitations observed in the austere spaceflight environment share similarities to barriers to care for underserved areas on Earth. We discuss common terrestrial ophthalmic diseases and how machine learning and visual assessment technology for SANS can help increase screening for early intervention. The foundational developments with this novel system may help protect the visual health of both astronauts and individuals on Earth.

The human body undergoes many changes during long-duration spaceflight including musculoskeletal, visual, and behavioral changes. Several of these microgravity-induced effects serve as potential barriers to future exploration missions. The advent of artificial intelligence (AI) in medicine has progressed rapidly and has many promising applications for maintaining and monitoring astronaut health during spaceflight. However, the austere environment and unique nature of spaceflight present with challenges in successfully training and deploying successful systems for upholding astronaut health and mission performance. In this article, the dynamic barriers facing AI development in space medicine are explored. These diverse challenges range from limited astronaut data for algorithm training to ethical/legal considerations in deploying automated diagnostic systems in the setting of the medically limited space environment. How to address these challenges is then discussed and future directions for this emerging field of research.

ChatGPT (Generative Pre-trained Transformer) is an artificial intelligence (AI) language model developed by OpenAI. GPT-4 is the newest version of ChatGPT released on March 14, 2023 and has been reported to have a broader knowledge base as well as improved problem-solving ability. GPT-4 has also been reported to be less easy to fool, and is capable of processing 8 times more words. The usages for ChatGPT continue to grow, and new applications of the langue learning model continue to be found. Due to the black box nature of AI models, interpretation of GPT-4 outputs must be made with caution to ensure that no errors have been made. Particularly in healthcare delivery and medicine, where policies and procedures are frequently revised, GPT-4 algorithms comments may be out-of-date or incorrect. Out of the new features introduced in GPT-4, the most important feature may be its new ability to analyze images. This could potentially help doctors to diagnose and treat patients quickly and accurately, especially in areas where access to medical professionals may be limited. To examine GPT-4’s image diagnostic ability, we provided it with a variety of common medical imaging modalities: from chest X-rays, magnetic resonance images (MRI), to optical coherence tomography (OCT) images. All in all, although significant advancements and further research is still required, the future of automated medical image analysis is highly promising.