## 7 Telemedicine and AI Support Systems
### AI-Driven Decision Support  
AI-driven tools enhance autonomous ocular diagnostics by detecting conditions such as optic nerve edema, corneal ulcers, and retinal detachment. Machine learning models help astronauts prioritize treatments, ensuring timely interventions without Earth-based support [10,32,34].  
For managing spaceflight-associated neuro-ocular syndrome (SANS), these technologies are indispensable. Continuous monitoring of metrics like intraocular pressure and optic disc morphology enables early detection of risks such as tear film instability or optic nerve changes before they become severe. Non-invasive imaging tools, including optical coherence tomography (OCT) and ultrasound, integrated with automated systems, enhance the proactive management of ocular complications [46,89].  
In addition to diagnostics, AI-driven tools excel in providing actionable recommendations. For example, convolutional neural networks (CNNs) have been applied to optical coherence tomography (OCT) and fundus photography to detect and classify retinal and optic nerve conditions with high accuracy. These AI-assisted imaging techniques are particularly valuable for deep-space missions where communication delays of up to 21 min limit Earth-based consultation [81,82].  
These systems are also critical for autonomous medical interventions. Predictive models can identify patterns in ocular health metrics to suggest countermeasures, such as targeted exercises or pharmacological treatments, mitigating SANS progression [90]. They facilitate remote surgical guidance and robotic interventions, addressing emergencies like corneal abrasions or foreign body removal without requiring onboard specialists [89]. By enhancing tele-ultrasound capabilities, AI tools guide astronauts in capturing optimal diagnostic images and interpreting them onboard, bridging gaps created by delayed communication with Earth [91].  
Beyond real-time care, these platforms offer cognitive support during high-stress scenarios. Decision-making tools tailored to resource-limited environments ensure astronauts can independently manage ocular emergencies, even in the isolating conditions of deep space [91,92]. Future advancements, such as generative adversarial networks (GANs), promise to simulate rare conditions, enhancing diagnostic accuracy and model adaptability. Additionally, AI-driven self-experimentation frameworks could accelerate research into corneal adaptation to microgravity, bioprinted tissue viability, and fluid dynamics affecting tear film stability. These systems could autonomously analyze in-flight data, optimizing diagnostic and treatment protocols for space-based ocular health [93; 94].  
Beyond diagnostic applications, AI-driven chatbots could serve as auxiliary tools for astronauts by providing real-time guidance on technical troubleshooting, medical protocols, and translation support for international crews. These models can process extensive medical databases, offering decision support during emergencies or research activities where Earth-based consultation is delayed. However, the reliability of AI-generated outputs can vary, and there is a risk of generating inaccurate or misleading information. Therefore, rigorous validation and testing are essential before integrating these systems into critical mission operations [95].  
By seamlessly integrating diagnostics, predictive analytics, and autonomous interventions, AI platforms empower crews to manage ocular health effectively. These advancements reduce reliance on Earth-based support, paving the way for enhanced medical autonomy during long-duration space exploration.