The sky is not the limit when thinking of solutions to keep the astronauts safe and healthy in the hazardous environment of space.
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The projects for 2025-2026 are Complete but expect updates to these projects
All of these projects are based on needs for our future exploration missions and part of our Human Research Program (HRP). Here are the Risk assessments, Risk evidence, and GAP assessments
Student Template
Use this template to help guide you through the solution process if you like. This is not a requirement
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New projects and continuing projects
There are currently several new projects that are being developed with our flight surgeons, exploration medical and HRP folks. A few of them will include AI aspects.
If there are projects listed below that you worked on last year, you may continue to work on those projects or choose a new one.
If there are projects listed below that you worked on last year, you may continue to work on those projects or choose a new one.
Mars Transit Habitat Food Storage
| mars_transit_habitat_food_storage.pdf |
PACKAGING DESIGNS FOR MEDICATIONS
| packaging_for_medications_on_iss_and_future_space.pptx |
Both the packaging for medications and the Medical Inventory system may like to consider each others specifications since both projects may be used together
Medical Inventory system- With ease of use and safety- This project also in the Software program
Requested by NASA HH&P
Sponsored by TBD (Pharmaceutical Companies, Systesm Companies, etc. . )
The Challenge: Our Space-based Medical Inventory System is the perfect solution for any space exploration mission. It is designed to monitor and manage every aspect of medical supplies, from ordering and receiving to storage and usage. This system is critical to ensure the health and safety of your astronauts, as it enables you to keep track of medical supplies in case of an emergency. With our advanced features and tailored solutions, it is the perfect tool for any space exploration mission.
Increased Scope 2025-26 - Ease of Use plus safety, consider Facial Recognition, Pill Recognition, Voice Prompts.
With the importance of prescription and non-prescription medicines and supplements, it becomes very important that the inventory management system is very easy to use and is time-efficient for the astronauts. Some students last year used AI-based facial recognition to ensure that both earth-based staff and astronauts had an easy way to verify what medication was being taken, and who took this medication. We would like to continue on this type of theme and ensure very easy and efficient methods are used to dispense the different types of individual doses, track and update the inventory, taking into account the updated tracking, looking at edible tracking marking and validation of medication, especially prescription medication, plus the forecasting of when specific meds could be exhausted, and potential rationing recommendations, based on usage.
The next-generation medication management system for space missions should seamlessly integrate AI-driven facial recognition, pill recognition, and voice prompts to maximize ease of use, safety, and operational efficiency. The system must support secure dispensing, real-time inventory tracking, advanced forecasting, and robust validation—including edible tracking—while being intuitive and reliable for astronauts in isolated, high-stakes environments.]
Increased Scope 2025-26 - Ease of Use plus safety, consider Facial Recognition, Pill Recognition, Voice Prompts.
The Inventory System should use the additional scope for PACKAGING DESIGNS FOR MEDICATIONS
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Both the packaging for medications and the Medical Inventory system may like to consider each others specifications since both projects may be used together
Increased Scope 2025-26 - Ease of Use plus safety, consider Facial Recognition, Pill Recognition, Voice Prompts.
With the importance of prescription and non-prescription medicines and supplements, it becomes very important that the inventory management system is very easy to use and is time-efficient for the astronauts. Some students last year used AI-based facial recognition to ensure that both earth-based staff and astronauts had an easy way to verify what medication was being taken, and who took this medication. We would like to continue on this type of theme and ensure very easy and efficient methods are used to dispense the different types of individual doses, track and update the inventory, taking into account the updated tracking, looking at edible tracking marking and validation of medication, especially prescription medication, plus the forecasting of when specific meds could be exhausted, and potential rationing recommendations, based on usage.
The next-generation medication management system for space missions should seamlessly integrate AI-driven facial recognition, pill recognition, and voice prompts to maximize ease of use, safety, and operational efficiency. The system must support secure dispensing, real-time inventory tracking, advanced forecasting, and robust validation—including edible tracking—while being intuitive and reliable for astronauts in isolated, high-stakes environments.]
Increased Scope 2025-26 - Ease of Use plus safety, consider Facial Recognition, Pill Recognition, Voice Prompts.
- Define an overall methodology
- Look for Reputable Training Data – Research Centers - University Medical Research
- After Training define test cases that identify a:
- Suspected Illness or Malady
- Underlying patient issues
- Neutral Patients
- Test these groups, capture testing
- Also try sensitivity testing, change single parameters, and see the impact.
- Provide patient reports and overall Data Reports
The Inventory System should use the additional scope for PACKAGING DESIGNS FOR MEDICATIONS
______________________________________________________________________________________
Both the packaging for medications and the Medical Inventory system may like to consider each others specifications since both projects may be used together
| modified_and_enhanced_prescription_and_non-prescription_plus_medical_supplies_inventory_management_system.pdf |
| medical_inventory_system.pdf |
| mct_system_eb3ee91089.pdf |
Diagnostic Medical Tool Design- Updated from last year
This project is to look at the Tempus Pro (a multi-diagnostic tool) and help create a solution that will allow the Exploration medical team to use this diagnostic tool as an otoscope as one of it's features.
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Please review the PowerPoint carefully as some wording and specifications have changed since last year
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| laryngoscopedimensions_10_3_24.png |
| additional_tempus_pro_information.pdf |
Image of end of fiber optic camera
| img_6758.jpeg |
Fiber optic camera turned on
| img_7287.jpeg |
Tempus Pro Unit image
| img_7264.jpeg |
Machine Learning & Artificial Intelligence Medical Diagnostic System
tHIS PROJECT ALSO IN THE sOFTWARE pROGRAM
Executive Summary
The AI-Driven Medical Diagnosis System for Long-Duration Space Missions (2025–2026) project aims to develop and deploy an autonomous, artificial intelligence (AI)-based medical assistant to support astronaut health and mission safety during Moon and Mars expeditions. As communication delays (4 to 40 minutes) and long communication blackouts make real-time medical support from Earth infeasible, this system will serve as the first line of medical diagnosis and guidance for crews with limited medical training and no on-site physician.
The system will integrate narrow AI (task-specific, rule-based or machine-learned diagnostic modules) and generative AI (large language models capable of nuanced conversation and multimodal reasoning). Running on a robust, single-server partition within the spacecraft’s computing environment, the AI will gather data from biosensors, imaging devices, and crew input, delivering accurate, actionable medical guidance for a range of space-relevant conditions.
By leveraging proven Earth-based medical AI, adapting it for space constraints (power, memory, radiation, and autonomy), and synthesizing real-time data from multiple sources, this initiative will enable timely, effective medical interventions even in complete communication blackout scenarios. This project is crucial for advancing the autonomy of future missions, such as GATEWAY, Axiom, and Starlab, and directly supports NASA’s Moon to Mars strategy.
Project Background
Space Exploration Context:
With NASA, SpaceX, and international partners targeting long-duration lunar and Martian surface missions in the mid-2020s, the health and safety of astronauts are paramount. Unlike aboard the International Space Station (ISS), where near-instant ground support is available, deep space missions face unavoidable communication latency and potential blackouts. Medical emergencies in these extreme environments are compounded by microgravity, radiation exposure, and the lack of advanced hospital facilities.
Challenge:
Astronauts will often have only basic medical or paramedic training. In the event of illness or injury, rapid, accurate diagnosis and intervention can be lifesaving. Traditional telemedicine fails under blackout conditions and is hampered by latency delays. There is thus an urgent need for onboard, autonomous medical decision support systems—intelligent enough to interpret symptoms, analyze physiological data, and provide step-by-step guidance, yet resource-efficient and robust enough for space deployment.
Technological Foundation:The project builds upon recent advances in:
Narrow AI: Specialized diagnostic algorithms used in hospitals (e.g., sepsis detection, radiology triage).
Generative AI: Large language models (e.g., OpenAI GPT-4, Google Med-PaLM, AMIE) with proven performance in medical question answering and dialog.
Multimodal AI: Systems capable of integrating imaging, sensor data, and natural language to improve diagnostic accuracy.
Mission and Spacecraft Constraints
Autonomy: The system must operate without external support for up to two weeks.
Reliability: Must withstand the harsh space environment (radiation, limited power, hardware constraints).
User-Centered Design: Interface and recommendations must be accessible to non-experts under stress.
Strategic Importance: This project directly supports the Moon to Mars initiative, as well as commercial and international space station programs (GATEWAY, Axiom, Starlab), by closing a critical gap in astronaut medical autonomy. It positions the US and its partners at the forefront of AI-enabled crewed space exploration, paving the way for safe, sustainable presence beyond low Earth orbit.
Reference SummaryThe text proposes an AI-based system to support medical officers and flight surgeons during long-duration space missions. This system would be pre-trained on Earth's clinical knowledge, then continuously refined in space using data from spacecraft sensors, astronaut health data, and human feedback. The AI would provide personalized health recommendations, adapting to the extreme space environment and operating with low power and bandwidth through edge computing and dimensionality reduction.
The long-term vision requires developing pre-trained models, adapting them to diverse spaceflight data, and integrating them with small, robust processors. A short-term goal is to focus on the data and model software structure. This involves evaluating existing biomedical LLMs (like BioMedLLM, Med-PaLM) for accuracy and selecting the best for a proof-of-concept. Additionally, a synthetic database mimicking various space mission data (environmental, microbial, astronaut physiological) needs to be developed, along with data standardization methods, to refine the chosen LLM. Finally, plausible space mission scenarios with data aberrations should be created to test the model's predictive and recommendation capabilities.
The AI-Driven Medical Diagnosis System for Long-Duration Space Missions (2025–2026) project aims to develop and deploy an autonomous, artificial intelligence (AI)-based medical assistant to support astronaut health and mission safety during Moon and Mars expeditions. As communication delays (4 to 40 minutes) and long communication blackouts make real-time medical support from Earth infeasible, this system will serve as the first line of medical diagnosis and guidance for crews with limited medical training and no on-site physician.
The system will integrate narrow AI (task-specific, rule-based or machine-learned diagnostic modules) and generative AI (large language models capable of nuanced conversation and multimodal reasoning). Running on a robust, single-server partition within the spacecraft’s computing environment, the AI will gather data from biosensors, imaging devices, and crew input, delivering accurate, actionable medical guidance for a range of space-relevant conditions.
By leveraging proven Earth-based medical AI, adapting it for space constraints (power, memory, radiation, and autonomy), and synthesizing real-time data from multiple sources, this initiative will enable timely, effective medical interventions even in complete communication blackout scenarios. This project is crucial for advancing the autonomy of future missions, such as GATEWAY, Axiom, and Starlab, and directly supports NASA’s Moon to Mars strategy.
Project Background
Space Exploration Context:
With NASA, SpaceX, and international partners targeting long-duration lunar and Martian surface missions in the mid-2020s, the health and safety of astronauts are paramount. Unlike aboard the International Space Station (ISS), where near-instant ground support is available, deep space missions face unavoidable communication latency and potential blackouts. Medical emergencies in these extreme environments are compounded by microgravity, radiation exposure, and the lack of advanced hospital facilities.
Challenge:
Astronauts will often have only basic medical or paramedic training. In the event of illness or injury, rapid, accurate diagnosis and intervention can be lifesaving. Traditional telemedicine fails under blackout conditions and is hampered by latency delays. There is thus an urgent need for onboard, autonomous medical decision support systems—intelligent enough to interpret symptoms, analyze physiological data, and provide step-by-step guidance, yet resource-efficient and robust enough for space deployment.
Technological Foundation:The project builds upon recent advances in:
Narrow AI: Specialized diagnostic algorithms used in hospitals (e.g., sepsis detection, radiology triage).
Generative AI: Large language models (e.g., OpenAI GPT-4, Google Med-PaLM, AMIE) with proven performance in medical question answering and dialog.
Multimodal AI: Systems capable of integrating imaging, sensor data, and natural language to improve diagnostic accuracy.
Mission and Spacecraft Constraints
Autonomy: The system must operate without external support for up to two weeks.
Reliability: Must withstand the harsh space environment (radiation, limited power, hardware constraints).
User-Centered Design: Interface and recommendations must be accessible to non-experts under stress.
Strategic Importance: This project directly supports the Moon to Mars initiative, as well as commercial and international space station programs (GATEWAY, Axiom, Starlab), by closing a critical gap in astronaut medical autonomy. It positions the US and its partners at the forefront of AI-enabled crewed space exploration, paving the way for safe, sustainable presence beyond low Earth orbit.
Reference SummaryThe text proposes an AI-based system to support medical officers and flight surgeons during long-duration space missions. This system would be pre-trained on Earth's clinical knowledge, then continuously refined in space using data from spacecraft sensors, astronaut health data, and human feedback. The AI would provide personalized health recommendations, adapting to the extreme space environment and operating with low power and bandwidth through edge computing and dimensionality reduction.
The long-term vision requires developing pre-trained models, adapting them to diverse spaceflight data, and integrating them with small, robust processors. A short-term goal is to focus on the data and model software structure. This involves evaluating existing biomedical LLMs (like BioMedLLM, Med-PaLM) for accuracy and selecting the best for a proof-of-concept. Additionally, a synthetic database mimicking various space mission data (environmental, microbial, astronaut physiological) needs to be developed, along with data standardization methods, to refine the chosen LLM. Finally, plausible space mission scenarios with data aberrations should be created to test the model's predictive and recommendation capabilities.
| building_a_medical_diagnostic_system.pdf |
Tips for Using AI to synthesize data
Lunar Smart Adaptive Habitat Design with AI Psychological Monitoring for Crew Adaptive Positive Mental Health
This project uses a variety of areas including architecture, interior design, software/AI, Psychology. You may choose to have several groups working on this project that are working on those various areas. Goal is to have all aspects intersect to have a final project.
Do not be afraid of trying out AI even if you have not tried before. The document below has some very easy ways to get into AI so that you can completely do the project. Please reach out with any questions.
| getting_started_with_artificial_intelligence___machine_learning_v0.1.pdf |
This proposal presents a next-generation lunar habitat concept that merges NASA’s latest architectural and operational requirements with advanced artificial intelligence (AI) for real-time psychological health monitoring and adaptive crew support. The project is developed for the NASA HUNCH program, empowering high school students to innovate at the intersection of software, biomedical engineering, and space architecture.
| lunar_smart_adaptive_habitat_design_with_ai_psychological_monitoring_for_crew_adaptive_positive_mental_health.pdf |
| psychological_evaluation_using_ai.pdf |
Click the software program website button to get the complete project details.
Portable IV Fluid
| portable_iv_fluid_generation.pdf |
Since we no longer have the IV fluid administration project, we do want this project to add including IV fluid administration once the IV fluid has been generated. This project will not have to worry about microgravity, but rather reduced gravity on moon and mars. There would still be an issue of getting rid of bubbles, although not like it would have been in the microgravity environment.
| ivgen_mini_asma_2023_final.pdf |
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Attached is a document I have put together summarizing the United States Pharmacopeia (USP) standards for IV fluid recognized for the IVGEN and IVGEN Mini IV fluid testing. There is a table that lays out each USP standard requirement for .9% IV fluid within the document. Because each standard is mandatory to produce safe IV fluid, I cannot provide a list of the "most important" standards because they are all equally important. To help with understanding the water purification and sterilization processes, I have also included information about several water purification methods with a process flow chart that shows the steps to achieve sterile water for injection from potable water.
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Attached is the paper about the water purification and sterilization device.. Towards the end of the paper, it details all the tests the product water goes through to meet the required standards.
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Innovative Nutrition to Prevent illness in space
| innovative_nutrition_to_prevent_illness_in_space.pdf |
Document below gives detail on how the research needs to be done and how it is documented.
| innovative_nutrition_to_prevent_illness_in_space_v0.2__1_.pdf |
Neuro Occular Issues
Kidney Issues in Space
Radiation issues on Physiology