Harnessing Artificial Intelligence for Medical Diagnosis and Treatment During Space Exploration Missions

From May 8th to June 9th, 2023, I had the opportunity to participate in an experiential learning experience at Johnson Space
Center in Houston, TX with Exploration Medical Capability (ExMC), an element of the NASA Human Research Program.
During this research experience, I was not only able to work on the above titled research project, but also gain an immense
exposure to the field of aerospace medicine, make numerous connections within the field, tour NASA facilities, as well as
travel to the Aerospace Medical Association Annual Conference (AsMA) in New Orleans.

To briefly introduce my project, it is well understood that the medical capabilities available to crew medical officers (CMOs)
on the International Space Station will be different than the capabilities available and needed during deep space exploration
missions to the Moon, Mars, and beyond. Ground support is particularly limited due to distance, communication delays (or
lack of communication), and lack of resupply. Therefore, to support medical care by CMOs on these missions, robust clinical
decision support systems (CDSSs) must be designed. The recent publication and public launch of generative artificial
intelligence (AI) tools based upon large language models (LLM) such as ChatGPT provides the opportunity to create a
smart assistant for onboard triage, diagnosis, and treatment of medical conditions. Ultimately, the overall purpose of the
project was to research what AI tools currently exist or are in development, and to see how they might be implemented
onboard during exploration class spaceflights of the future.

The ExMC element is actively developing several tools to be used in preparation for and during deep space exploration
missions. One of those tools, known as IMPACT, is a probabilistic risk assessment model which can be used to propose a
desired medical system (based on mass and volume) and suggest the clinical outcomes likely to occur for a design reference
mission (DRM). The group recently presented the IMPACT model and a DRM of interest titled “Modified Long Duration
Lunar Orbital and Lunar Surface” (mLDLOLS) at the recent AsMA conference. The mLDLOLS mock mission is a 9 month
and 6-day deep space exploration mission consisting of time in Moon’s orbit (3 months on the Gateway space station), on
the lunar surface (3 months within habitat), and another 3 months on Gateway before return to Earth. For this DRM, IMPACT
ultimately outlined a preferred medical system that was then associated with medical conditions considered to be most likely
based on frequency, most likely to cause astronaut task time loss (TTL), most likely to cause return to definitive care (RTDC),
and most likely cause loss of crew life (LOCL). IMPACT also highlighted the medical capabilities/skills that would be required
to care for those medical conditions, such as performing a history of present illness or musculoskeletal exam with ultrasound.

The primary objective of the project was to perform a survey of the AI tools and systems applicable to the conditions outlined
for the proposed mLDLOLS mission. Using PubMed (including most relevant MeSH terms) and Google Scholar, we then
created a robust annotated bibliography organized by condition. The 56-page and over 500 reference annotated
bibliography was subsequently used to create a review outline that would become the basis for drafting of a future
publication. For the review outline, we took those medical conditions researched within the annotated bibliography
(condition-based approach) and deployed a systems-based approach, combining those medical conditions and related tools
into ten categories. These categories included general/all-purpose CDSSs, tools to diagnose or manage respiratory,
dermatologic, neurologic, auditory and vestibular, ophthalmic, musculoskeletal, infection-associated, and gynecologic
conditions, as well as tools that could be deployed in the setting of trauma/emergency. With the completion of the 30-page
outline, we then began drafting the review paper. To conclude the research experience, I presented the findings from our
survey to the ExMC Clinical and Science team. With these objectives, I ultimately learned about the number of AI tools that
exist today to assist medical professionals with the triage, diagnosis, and management of several medical conditions. These
tools can span from chatbot assistants to help triage knee pain to vision transformer models that can identify ophthalmic
conditions based on ocular surface images captured with a cell phone. We also highlighted the current gaps that exist in
the literature alongside the advancements that are needed to make the desired CDSS for deep space exploration missions.

With this experience, I certainly confirmed an existing career goal and identified several additional skills needed to become
an aerospace medical doctor including knowledge of critical care in an extreme medicine setting, aerospace engineering
and human integration systems, artificial intelligence, machine learning, and risk models. I also identified numerous
transferable skills for this career goal including the basic knowledge of medicine (MD), deployment of the scientific method
for critical thought about new scientific questions (PhD), review of published literature, including creating an annotated
bibliography (PhD), as well as detailed scientific writing (PhD). The results of my research will likely guide the design of an
all-encompassing onboard medical assistant for use during deep space exploration missions of the future. I plan on sharing
the outcomes from this experience with my peers at a student seminar in the Fall semester on August 30th. During the
seminar, I will detail the project, my experience at NASA and AsMA, as well as offer best practice guidelines for students
entertaining similar experiences or careers. In conclusion, I would like to thank the WVU School of Medicine, Research and
Graduate Education office, as well as NASA ExMC for the unwavering support of this life-changing experience.