Results for “support” — Page 28

NASA NTRS2018

Collaborative Data Curation to Support the Multi-Mission Algorithm and Analysis Platform (MAAP)

Upcoming space-borne missions will offer unprecedented data about Earth but will also feature exponentially high data volumes. These high data volumes will change the way the scientific community works with data and will also create a unique need for improved data sharing and collaboration. NASA and ESA are working together to address these issues by collaboratively developing the Multi-Mission Algorithm and Analysis Platform (MAAP) to improve the understanding of global aboveground terrestrial carbon dynamics. The MAAP will support ESA’s BIOMASS mission, NASA’s GEDI mission and NASA/ISRO’s NISAR mission. The MAAP will be developed in two phases: a pilot phase and a full production phase. The pilot phase will demonstrate collaboration and basic capabilities. The pilot phase will focus on biomass relevant airborne and field campaign data. Two NASA teams are supporting the development of the MAAP. The MAAP engineering team is responsible for the development, maintenance and operations of the MAAP system while the MAAP data team ensures the ongoing quality of the data, metadata and other information provided in the MAAP. The MAAP data team also supports the ingest and archive of identified data to the MAAP platform. This poster describes the use case development process for the pilot MAAP and the data curated in support of those use cases. Additionally, this presentation will outline the pilot MAAP data ingest process and metadata curation effort along with efforts to ensure interoperability between ESA and NASA data and metadata.

Bugbee, Kaylin↗

NASA NTRS2019

International Space Station as a Development Testbed for Advanced Environmental Control and Life Support Systems

Since the beginning of human spaceflight, mission durations have steadily increased. Current mission durations onboard the International Space Station are multiple months, but future exploration missions to cislunar space and beyond will require multiple year durations. In addition, missions to cislunar or deep space will encounter a much harsher environment than the current ISS low-earth orbit missions, with relation to radiation, isolation, and lack of timely available support from Earth. To meet the challenges of deep space, so-called "exploration missions" will require Environmental Control and Life Support systems with higher performance, lower mass and logistics requirements, and more endurance than are possible with current operational systems on board the International Space Station. As a currently operational human-occupied platform, the International Space Station presents a unique opportunity to act as a testbed for development of advanced next-generation Environmental Control and Life Support Systems, such that these systems may be tested, proven, and refined for eventual deployment on deep space human exploration missions. This paper will outline the history, progress to date, and future plans for efforts to design, select, build, test and fly Advanced Environmental Control and Life Support Systems on the ISS.

Shaw, Laura A.↗

NASA NTRS2017

How Should Life Support Be Modeled and Simulated?

Why do most space life support research groups build and investigate large models for systems simulation? The need for them seems accepted, but are we asking the right questions and solving the real problems? The modeling results leave many questions unanswered. How then should space life support be modeled and simulated? Life support system research and development uses modeling and simulation to study dynamic behavior as part of systems engineering and analysis. It is used to size material flows and buffers and plan contingent operations. A DoD sponsored study used the systems engineering approach to define a set of best practices for modeling and simulation. These best practices describe a systems engineering process of developing and validating requirements, defining and analyzing the model concept, and designing and testing the model. Other general principles for modeling and simulation are presented. Some specific additional advice includes performing a static analysis before developing a dynamic simulation, applying the mass and energy conservation laws, modeling on the appropriate system level, using simplified subsystem representations, designing the model to solve a specific problem, and testing the model on several different problems. Modeling and simulation is necessary in life support design but many problems are outside its scope.

Jones, Harry W.↗

NASA NTRSDate not supplied

International Space Station as a Testbed for Exploration Environmental Control and Life Support Systems - 2021 Status

Human exploration missions beyond low earth orbit, such as NASA’s Artemis Program, present significant challenges to spacecraft system design and supportability. A particularly challenging area is the Environmental Control and Life Support System (ECLSS) that maintains a habitable and life-sustaining environment for crewmembers. NASA is utilizing the experience gained from its current and prior spaceflight programs to mature life support technologies for exploration missions to deep space. The intent is to establish a portfolio of life support system capabilities with proven performance and reliability to enable human exploration missions and reduce risk to success of those missions. As a fully operational human-occupied platform in microgravity, the International Space Station (ISS) presents a unique opportunity to act as a testbed for exploration-class ECLSS, such that these systems may be tested, proven, and refined for eventual deployment on deep space human exploration missions. This paper will provide an updated status on the testbed development including hardware and ISS vehicle integration progress to date as well as future plans for efforts to design, select, build, test and fly Exploration ECLSS on the ISS.

Laura A Shaw↗

NASA NTRS2020

Detecting Juno’s ‘Heartbeat’: Communications Support during Critical Events of the Juno Mission

The first section of this paper summarizes the radio systems of the spacecraft, the antennas of the DSN, and signal processing performed in real-time on the ground to support the critical activities. The second section goes into detail on each of the critical events with details of the radio science support and results of each event. Finally, the paper is concluded with a discussion of future planned supports and capabilities of radio science support during critical events.

Jongeling, Andre↗

NASA NTRSDate not supplied

NASA Environmental Control and Life Support Technology Development for Exploration: 2020 to 2021 Overview

This paper provides an overview of NASA supported activities developing Environmental Control and Life Support (ECLSS) technologies in the following capability areas: life support, environmental monitoring, fire safety, and logistics. NASA has been refining technology needs for deep space missions including Gateway, lunar surface, Mars transit, and Mars surface missions. Validating technologies in relevant environments, both in low earth orbit (LEO) and ground tests is critical in understanding technology performance and long duration performance. On-orbit and ground tests inform NASA’s technology decisions to fill exploration gaps. NASA has multiple technology projects across the technology readiness spectrum with potential to fill or partially fill exploration gaps. For each capability area, this paper will describe select capability gaps, NASA technology project maturation over the past year, and key performance parameters (KPPs). KPPs are evolving but they still provide a useful measure in communicating progress and identifying development needs to fill exploration gaps. The intent is to provide a very high-level overview describing the projects that are supporting gap closure and provide references to additional technical details, progress, and KPPs.

James Lee Broyan↗

NASA NTRSDate not supplied

Updated Human Mars Ascent Vehicle Concept in Support of NASA’s Strategic Analysis Cycle 2021

The NASA Artemis program has brought significant change to the agency’s human exploration strategy over the last several years. The human exploration of Mars remains an ultimate objective of the overall human exploration strategy. However, how the agency intends to execute initial human exploration of Mars has shifted to better align with current policy. These changes find their way into the design of key architecture elements, such as the Mars Ascent Vehicle (MAV). Of the numerous changes in the conceptual human Mars architecture, two in particular have had significant impact on the design of the human MAV: the desire for minimal surface infrastructure for initial human Mars missions, and technology investment timelines in support of initial human missions to Mars. The first leads to a surface mission architecture targeting a 30-day surface duration with two crewmembers. The second drives out in-situ resource utilization (ISRU) from the initial sortie. As a result, the reference MAV design for initial human Mars missions in the Human Exploration and Operations Mission Directorate’s Strategic Analysis Cycle 2021 (SAC21) is based around supporting the return of 2 crew from the surface of Mars, without surface ISRU. This has led to several design changes since 2019, when details of a reference human MAV were last published. The MAV concept for SAC21 consists of a two-stage vehicle capable of supporting two crew for 84 hours. This duration is derived from a baseline ascent trajectory targeting an apoapsis rendezvous with the habitation element in a nominal 5-sol Mars aggregation orbit. With the goal of minimizing technology investments for the initial surface sortie removing ISRU from the trade space, the MAV must now be landed either fully loaded with propellant, or with some portion of the propellant off-loaded. Landing a partially-fueled MAV will require additional surface assets to perform robotic propellant transfer on the surface of Mars from a pre-emplaced propellant depot. Furthermore, cryogenic fluid management and storage for extended duration with no losses represents additional technology investment that would have to be made to support cryogenic-based propellant concepts that have been the baseline in past architectures. As a result, the SAC21 architecture utilizes a storable propellant-based MAV propulsion system to minimize these potential technology investments incurred by cryogenic-based propulsion systems. Recent efforts also focused on evaluating the sensitivity of key design parameters, such as landing site latitude, elevation, and local atmospheric conditions. All play a large role in determining the predicted propellant requirements of the MAV. Understanding their potential impacts is important because of the rippling effects of changes to the MAV design on other architecture elements, such as entry, descent, landing, and transportation systems. Results indicate up to +2% to -4% wet mass variation due to landing site latitude, with higher latitudes greater than 70 degrees North resulting in greater than 6% wet mass increases. The MAV saw reduced impact due to atmospheric design parameters, which only accounting for -0.6% to +0.4% wet mass variation.

Douglas J. Trent↗

NASA NTRS2022

Novel Crash Sled with a Translating Support Mass

Background: A novel crash sled has been developed with a translating support, incorporating transducers that allow multiple methods of measuring energy absorption to fully characterize the dynamic crush response of composite components. Objective: The main goal of the current investigation was to demonstrate functionality, repeatability, and accuracy of crush testing using a crash sled with a translating support mass. Methods: A semi-automated algorithm for data reduction was developed based on impact mechanics principles. A preliminary set of tests was initially conducted using aluminum honeycomb specimens with a specified stable crushing force to quantify the accuracy and repeatability of the crush data. Following the success of these tests, triaxially-braided fiber-reinforced polymer (FRP) specimens were evaluated. Results: Crush tests with the aluminum honeycomb specimens showed excellent outcomes for all three specimens. These data provided close agreement with cumulative energy absorption between individual instruments and stable crushing forces at expected values. For the FRP specimens, specific energy absorption (SEA) and force-displacement curves were successfully measured; however, data from the translating support mass accelerometer was excluded from the dataset due to clipping. The SEA of the corrugated specimens was greater than the SEA for the C-channel specimens at both test speeds. Conclusions: The crash sled functionality was verified, the specimen geometry was found to contribute more to SEA than the impact speed in the speed range tested, and the support mass accelerometer will be upgraded to prevent clipping in future tests.

Dynamic Crush↗

NASA NTRSDate not supplied

International Space Station as a Testbed for Exploration Environmental Control and Life Support Systems - 2022 Status

Human exploration missions beyond low earth orbit, such as NASA’s Artemis Program, present significant challenges to spacecraft system design and supportability. A particularly challenging area is the Environmental Control and Life Support System (ECLSS) that maintains a habitable and life-sustaining environment for crewmembers. NASA is utilizing the experience gained from its current and prior spaceflight programs to mature life support technologies for exploration missions to deep space. The intent is to establish a portfolio of life support system capabilities with proven performance and reliability to enable human exploration missions and reduce risk to success of those missions. As a fully operational human-occupied platform in microgravity, the International Space Station (ISS) presents a unique opportunity to act as a testbed for exploration-class ECLSS, such that these systems may be tested, proven, and refined for eventual deployment on deep space human exploration missions. This paper will provide an updated status on the testbed development including hardware and ISS vehicle integration progress to date as well as future plans for efforts to design, select, build, test and fly Exploration ECLSS on the ISS.

ECLSS↗

NASA NTRSDate not supplied

NASA Airborne Observations in Support of Wildfire Science and Applications

The NASA Science Mission Directorate Earth Science Division Airborne Science Program enables access to modified aircraft in support of NASA projects. The Program supports researchers and engineers by enabling testing of new instruments, calibration of on-orbit satellite instruments with airborne sensors, and to support process studies and model development. This talk will provide an overview of the Program capabilities, review past NASA flight projects that have focused on wildfires, and discuss how the Program is poised to support the FireSense project and interagency efforts to incorporate new technologies and observations into the wildfire management community. Past missions including the Western States Fire Mission and FIREX-AQ will be discussed, an overview of relevant NASA sensors will be provided, and an introduction to upcoming flight activity will be presented. Data telemetry, communications, and data standards will also be discussed towards improving timely distribution of data products to land managers.

Matthew M Fladeland↗

NASA NTRSDate not supplied

International Space Station as a Testbed for Exploration Environmental Control and Life Support Systems - 2023 Status

Human exploration missions beyond low earth orbit, such as NASA’s Artemis Program, present significant challenges to spacecraft system design and supportability. A particularly challenging area is the Environmental Control and Life Support System (ECLSS) that maintains a habitable and life-sustaining environment for crewmembers. NASA is utilizing the experience gained from its current and prior spaceflight programs to mature life support technologies for exploration missions to deep space. The intent is to establish a portfolio of life support system capabilities with proven performance and reliability to enable human exploration missions and reduce risk to success of those missions. As a fully operational human-occupied platform in microgravity, the International Space Station (ISS) presents a unique opportunity to act as a testbed for exploration-class ECLSS, such that these systems may be tested, proven, and refined for eventual deployment on deep space human exploration missions. This paper will provide an updated status on the testbed development including hardware and ISS vehicle integration progress to date as well as future plans for efforts to design, select, build, test and fly Exploration ECLSS on the ISS.

ECLSS↗

NASA NTRSDate not supplied

International Space Station (ISS) as A Testbed for Exploration Environmental Control and Life Support Systems (ECLSS)- 2023 Status

Human exploration missions beyond low earth orbit, such as NASA’s Artemis Program, present significant challenges to spacecraft system design and supportability. A particularly challenging area is the Environmental Control and Life Support System (ECLSS) that maintains a habitable and life-sustaining environment for crewmembers. NASA is utilizing the experience gained from its current and prior spaceflight programs to mature life support technologies for exploration missions to deep space. The intent is to establish a portfolio of life support system capabilities with proven performance and reliability to enable human exploration missions and reduce risk to success of those missions. As a fully operational human-occupied platform in microgravity, the International Space Station (ISS) presents a unique opportunity to act as a testbed for exploration-class ECLSS, such that these systems may be tested, proven, and refined for eventual deployment on deep space human exploration missions. This paper will provide an updated status on the testbed development including hardware and ISS vehicle integration progress to date as well as future plans for efforts to design, select, build, test and fly Exploration ECLSS on the ISS.

ECLSS↗

NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗

NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗

NASA NTRS2024

International Space Station as a Testbed for Exploration Environmental Control and Life Support Systems - 2024 Status

Human exploration missions beyond low earth orbit, such as NASA’s Artemis Program, present significant challenges to spacecraft system design and supportability. A particularly challenging area is the Environmental Control and Life Support System (ECLSS) that maintains a habitable and life-sustaining environment for crewmembers. NASA is utilizing the experience gained from its current and prior spaceflight programs to mature life support technologies for exploration missions to deep space. The intent is to establish a portfolio of life support system capabilities with proven performance and reliability to enable human exploration missions and reduce risk to success of those missions. As a fully operational human-occupied platform in microgravity, the International Space Station (ISS) presents a unique opportunity to act as a testbed for exploration-class ECLSS, such that these systems may be tested, proven, and refined for eventual deployment on deep space human exploration missions. This paper will provide an updated status on the testbed development including hardware and ISS vehicle integration progress to date as well as future plans for efforts to design, select, build, test and fly Exploration ECLSS on the ISS.

ECLSS↗

NASA NTRSDate not supplied

Defining A Modelling Language to Support Functional Hazard Assessment

Functional Hazard Assessment (FHA) is a key early-stage engineering process that supports the incorporation of safety in design by identifying the high-level functional hazards the system may encounter. While many FHA-like methodologies have been proposed in the design engineering literature, many of these methodologies have had difficulty becoming accepted industry practice. Industry standards, on the other hand, either provide too little recommendation on how to represent the function of the system to perform FHA, or rely on existing design artefacts which insufficiently support the goals of the process. This paper presents some of the problems with current modeling languages (both proposed and used) for FHA which limit the scope, expressiveness, flexibility, and precision of the analysis. It then outlines desirable principles an FHA-supporting analysis language should embody, and introduces the Functional Reasoning Design Language (FRDL), a formal modeling language for describing the functional elements of a system and their interactions, which aims to satisfy these principles. To demonstrate the use of this language, the modeling and hazard analysis of a disaster response drone is presented. While this case study is limited in scope, it highlights how FRDL can represent system function while reducing the ambiguity present in typical FHA-supporting functional modeling languages

Hazard Assessment↗

NASA NTRSDate not supplied

Developing Mars-Based Clinical Scenarios for an Earth Independent Medical Operations (EIMO) – Based Decision Support Service

As crewed missions move beyond Low-Earth Orbit, pre-mission planning cannot fully buy down the medical risks of exploration-class missions. Martian missions, where increased hazards exist, (such as long-duration spaceflight, surface-level EVA operations, and communications delays) will require a paradigm shift in the structure of a medical system. An Earth-Independent Medical Operations-based Medical System (EIMO-MS) will need to optimize four critical domains to help provide medical care: utilization of Pre-Mission Planning, augmentation of Acute and Prolonged Medical Decision Making, automated tracking of Resource Management, and assistance in Task Load Balance. The ideal EIMO-MS will be able to accomplish this goal by having an interactive, adaptable interface that will be able to provide real-time medical services. It must respond based on the level of crewmember training, medical situation, and available medical and non-medical resources. To showcase the capabilities and requirements of such a sophisticated automated MS, a series of clinical scenarios of escalating complexity were developed with clinical and systems engineering input. These scenarios describe in clinical detail what a theoretical future medical system, enhanced with multiple information streams (such as a medical database, an AI-based Decision Support System, real-time monitoring, enhanced in-situ laboratory imaging, etc.) can achieve in conjunction with a trained and experienced crew. Scenarios are comprised of: a context section including objectives and applicable spaceflight environment, a highlighted assumptions section, a clinical narrative section, and a systems engineering activity diagram demonstrating the integrated Medical System (MS). The “swim lanes” of the activity diagram act as the logistical core of each scenario and show how the MS will interact with the crew, ground support, and other in-flight systems. The Design Reference Mission that is used for the scenarios is based on existing reference mission profiles [1] with a projected 30-sol stay on the Martian surface. Scenarios span the spectrum from planned evaluations, minor medical care, urgent care, surgical guidance, critical and expectant management, and behavioral health care. Mission complexity will exponentially increase during deep space and Mars exploration-class missions, and medical support for these missions will likewise need to increase in autonomy and adaptability. The integrated system that will support these missions will need to provide assistance in a variety of anticipated and unforeseen scenarios. These medical scenarios, guided by clinician input, are initial steps in crafting the requirements for an EIMO-based medical system. By working in a systems engineering framework, requirements and capabilities can be extracted and mapped while maintaining a clinical core.

Prashant Parmar↗

NASA NTRSDate not supplied

Objective Structured Clinical Evaluation (OSCE) of an Artificial Intelligence (AI) Clinical Decision Support System (CDSS) Tool

BACKGROUND Objective Structured Clinical Evaluations (OSCEs) have long been established as a robust methodology for summative assessment of clinical skills and decision-making during medical education. The recent integration of Artificial Intelligence (AI) into clinical decision-making processes has prompted the need for novel evaluation frameworks to assess the efficacy and reliability of AI clinical decision support system (CDSS) tools. This abstract outlines the process of quantitatively evaluating a novel CDSS (“Doc in a Box” Google 2024) trained on curated medical spaceflight data in the psychomotor domain as it interfaces with a human volunteer acting as the crew medical officer (CMO). PURPOSE The AI CDSS under review was developed as part of the Lunar Command and Control Interoperability (LuCCI) project, which is intended to address a gap in how Lunar Surface Systems (LSS) would interoperate across multiple programs, commercial partners, and international partners. The project objective is to define, prototype, integrate, and evaluate an interoperable lunar command, control, data, and software reference architecture to enable autonomy and informatics capability through common standards across LSS. A multi-modal AI-based CDSS compatible with Federated LSS will assist clinicians in diagnosing and managing complex medical conditions by providing evidence-based recommendations through predictive analytics. Given the critical role of decision-support as NASA continues to evolve its Earth-independent medical operations (EIMO), it is imperative to ensure that such AI tools perform reliably and align with clinical standards during progressive lunar and Martian exploration class missions. METHODS The OSCE framework, traditionally used for evaluating human clinicians, was adapted to assess the AI tool's decision-making capabilities in simulated clinical scenarios. In this adapted OSCE, the AI CDSS was tested across a series of structured clinical scenarios designed to mimic real-life spaceflight patient cases. These scenarios included a range of conditions and complexities, allowing for comprehensive assessment of the tool's performance. Key evaluation metrics included accuracy of diagnosis, timeliness of decision-making, and appropriate recommendations for therapies. The OSCE was scored by human physician evaluators who assessed the AI's recommendations in comparison with expert clinicians' medical decision making to ensure alignment with best practices and the standard of care. RESULTS Preliminary results indicate that the AI CDSS demonstrated high accuracy in diagnostic recommendations and decision support across various scenarios. However, certain limitations were noted, such as occasional discrepancies in handling complex or nuanced cases that required a more contextual understanding. Additionally, the tool scored higher on the diagnostic portion of the rubric, with lower scores in the therapeutic recommendations. These findings highlight the importance of continuous refinement and validation of AI tools through rigorous evaluation frameworks like the OSCE. The adaptation of OSCEs for AI tools presents several advantages, including a structured and reproducible approach to evaluation, the ability to test AI systems in diverse clinical scenarios, and the opportunity to benchmark AI performance against established clinical standards to permit charting of future progress as aerospace medicine evolves as a discipline. Remaining challenges include ensuring that these evaluations capture the full spectrum of clinical decision-making scenarios that will be confronted by CMOs during missions and adequately reflecting real-world variability of the austere spaceflight environment. CONCLUSION Employing OSCEs to evaluate AI clinical decision support tools offers a promising approach to validating their clinical utility and efficacy. This methodology not only provides insights into the tool's performance but also fosters ongoing improvement and alignment with standard of care practices. Future research should focus on refining these evaluation processes and addressing limitations to enhance the integration of AI tools in clinical spaceflight settings. REFERENCES Scott S, Hearns V, Barker MA. Testing Clinical Skills: A Look at the OSCE and USMLE Clinical Skills Exams. S D Med. 2019 Oct;72(10):451-453. Majumder MAA, Kumar A, Krishnamurthy K, Ojeh N, Adams OP, Sa B. An evaluative study of objective structured clinical examination (OSCE): students and examiners perspectives. Adv Med Educ Pract. 2019 Jun 5;10:387-397. Karam VY, Park YS, Tekian A, Youssef N. Evaluating the validity evidence of an OSCE: results from a new medical school. BMC Med Educ. 2018 Dec 20;18(1):313.

Ariana M Nelson↗