Precision health monitoring in spaceflight with integration of lower body negative pressure and advanced large language model artificial intelligence

Oct 24, 2025Life sciences in space research

Accurate health monitoring during spaceflight using lower body pressure and advanced AI

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Circadian Biology on OpenScience ↗PubMed ↗DOI ↗

Abstract

Long-term exposure to microgravity is associated with increased risks of orthopedic injuries due to influences on musculoskeletal health.

Current countermeasures like resistive exercise and Lower Body Negative Pressure may not sufficiently ensure astronaut safety and health.
A new vision-language large language model, DeepSeek-VL, could autonomously monitor musculoskeletal health by analyzing radiographic and biomechanical data.
DeepSeek-VL aims to provide real-time feedback on bone density, joint stability, and loading patterns, focusing on personalized astronaut care.
This approach allows for immediate interpretation of musculoskeletal metrics, potentially improving the effectiveness of countermeasures in microgravity.
The development of DeepSeek-VL could support systematic monitoring of health during extended space missions and inform terrestrial healthcare models.

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Long-term exposure to microgravity influences musculoskeletal health and enhances the likelihood of sustaining orthopedic injuries while on a microgravity mission and upon return to Earth. Although countermeasures are being investigated to alleviate some risks of injury, such as resistive (or weight) exercise and Lower Body Negative Pressure (LBNP), evidence is accumulating that current paradigms do not ensure the safety or health of astronauts because of a lack of in-flight diagnostic methods, in which load/diagnostic metrics can be assessed over time. Here, we suggest the integration of a new vision-language large language model (DeepSeek-VL) as a potential autonomous diagnostic agent for monitoring musculoskeletal health in a microgravity environment. DeepSeek-VL will autonomously analyze radiographic data and biomechanical data streamed from a LBNP device. Determinations will be made based on lost or compromised density in bone, lost joint-centered stability, or ineffective loading patterns - providing personalized and specific feedback regarding musculoskeletal health with the astronaut as the primary user. Unlike conventional reporting approaches that rely on cross-institutional analysis by household experts, DeepSeek-VL allows for real-time, and autonomous interpretation of musculoskeletal imaging metrics (and physiological metrics) for on-time personalized countermeasure development. Here, we review architectural adaptations including microgravity specific samplings of data, training protocols and implications of deployment in the ISS. We anticipate DeepSeek's timely development of flight-ready diagnostic reporting will facilitate in-flight/systematic monitoring of musculoskeletal health and safety, especially for astronauts undergoing load management training (e.g., LBNP) and ensure effectiveness of countermeasures, their outputs. We will address methods to circumvent limitations and barriers to risk, and establish the importance of a federated, adaptive, and resilient AI-based platform to mitigate risk for astronaut musculoskeletal health during extended missions. Finally, we address some considerations for terrestrial model and a healthcare authority within a current context of growing importance for effective orthopedic healthcare.

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Precision health monitoring in spaceflight with integration of lower body negative pressure and advanced large language model artificial intelligence

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