Abstract
Human exploration of deep space will involve missions of substantial distance and duration. To effectively mitigate health hazards, paradigm shifts in astronaut health systems are necessary to enable Earth-independent healthcare, rather than Earth-reliant. Here we present a summary of decadal recommendations from a workshop organized by NASA on artificial intelligence, machine learning and modelling applications that offer key solutions toward these space health challenges. The workshop recommended various biomonitoring approaches, biomarker science, spacecraft/habitat hardware, intelligent software and streamlined data management tools in need of development and integration to enable humanity to thrive in deep space. Participants recommended that these components culminate in a maximally automated, autonomous and intelligent Precision Space Health system, to monitor, aggregate and assess biomedical statuses.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Afshinnekoo, E. et al. Fundamental biological features of spaceflight: advancing the field to enable deep-space exploration. Cell 183, 1162–1184 (2020).
Loftus, D. J., Rask, J. C., McCrossin, C. G. & Tranfield, E. M. The chemical reactivity of lunar dust: from toxicity to astrobiology. Earth Moon Planets 107, 95–105 (2010).
Pohlen, M., Carroll, D., Prisk, G. K. & Sawyer, A. J. Overview of lunar dust toxicity risk. NPJ Microgravity 8, 55 (2022).
Paul, A.-L. & Ferl, R. J. The biology of low atmospheric pressure—implications for exploration mission design and advanced life support. Gravit. Space Res. 19, 3–17 (2005).
Council, N. R. Recapturing a Future for Space Exploration: Life and Physical Sciences Research for a New Era (National Academies Press, 2011).
Goswami, N. et al. Maximizing information from space data resources: a case for expanding integration across research disciplines. Eur. J. Appl. Physiol. 113, 1645–1654 (2013).
McGuire, K. et al. Using systems engineering to develop an integrated crew health and performance system to mitigate risk for human exploration missions. In Proc. 50th International Conference on Environmental Systems, 298, 1–11 (2021).
Antonsen, E., Hanson, A., Shah, R., Reed, R. D. & Canga, M. A. Conceptual drivers for an exploration medical system. In Proc. 67th International Astronautical Congress 1–10 (NASA Technical Reports Server, 2016).
Zhao, K. & Zhang, Q. Network protocol architectures for future deep-space internetworking. Sci. China Inf. Sci. 61, 040303 (2018).
Beaton, K. H. et al. Extravehicular activity operations concepts under communication latency and bandwidth constraints. In Proc. 2017 IEEE Aerospace Conference 1–20 (IEEE, 2017)
Ball, J. R. & Evans, C. H. Jr. Safe Passage: Astronaut Care for Exploration Missions (National Academies Press, 2014).
Antonsen, E. L. et al. Estimating medical risk in human spaceflight. NPJ Microgravity 8, 8 (2022).
McNulty, M. J. et al. Evaluating the cost of pharmaceutical purification for a long-duration space exploration medical foundry. Front. Microbiol. 12, 700863 (2021).
Blue, R. S. et al. Challenges in clinical management of radiation-induced illnesses during exploration spaceflight. Aerosp. Med. Hum. Perform. 90, 966–977 (2019).
Chancellor, J. C. et al. Limitations in predicting the space radiation health risk for exploration astronauts. NPJ Microgravity 4, 8 (2018).
Patel, Z. S. et al. Red risks for a journey to the red planet: the highest priority human health risks for a mission to Mars. NPJ Microgravity 6, 33 (2020).
Jordan, M. I. & Mitchell, T. M. Machine learning: trends, perspectives and prospects. Science 349, 255–260 (2015).
Costes, S. V., Sanders, L. M. & Scott, R. T. Workshop on Artificial Intelligence & Modeling for Space Biology https://zenodo.org/record/7508535#.Y9LwQITP23A (2023).
Hood, L. & Flores, M. A personal view on systems medicine and the emergence of proactive P4 medicine: predictive, preventive, personalized and participatory. N. Biotechnol. 29, 613–624 (2012).
Zitnik, M. et al. Machine learning for integrating data in biology and medicine: principles, practice and opportunities. Inf. Fusion 50, 71–91 (2019).
Sanders, L. M. et al. Biological research and self-driving labs in deep space supported by artificial intelligence. Nat. Mach. Intell. https://doi.org/10.1038/s42256-023-00618-4 (2023).
Kahn, J., Liverman, C. T. & McCoy, M. A. Health Standards for Long Duration and Exploration Spaceflight: Ethics Principles, Responsibilities and Decision Framework (National Academies Press, 2014).
Schmidt, M. A., Schmidt, C. M., Hubbard, R. M. & Mason, C. E. Why personalized medicine is the frontier of medicine and performance for humans in space. New Space 8, 63–76 (2020).
National Research Council (US) Committee on A Framework for Developing a New Taxonomy of Disease. Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease (National Academies Press, 2012).
Park, S.-M., Ge, T. J., Won, D. D., Lee, J. K. & Liao, J. C. Digital biomarkers in human excreta. Nat. Rev. Gastroenterol. Hepatol. 18, 521–522 (2021).
Gambhir, S. S., Ge, T. J., Vermesh, O. & Spitler, R. Toward achieving precision health. Sci. Transl. Med. 10, eaao3612 (2018).
Gambhir, S. S., Ge, T. J., Vermesh, O., Spitler, R. & Gold, G. E. Continuous health monitoring: an opportunity for precision health. Sci. Transl. Med. 13, eabe5383 (2021).
Antonsen, E. L. & Reed, R. D. Policy considerations for precision medicine in human spaceflight. Hous. J. Health L. Policy 19, 1–37 (2020).
Schork, N. J. Personalized medicine: time for one-person trials. Nature 520, 609–611 (2015).
Arges, K. et al. The Project Baseline Health Study: a step towards a broader mission to map human health. NPJ Digit. Med. 3, 84 (2020).
Chen, R. et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 148, 1293–1307 (2012).
Li, X. et al. Digital health: tracking physiomes and activity using wearable biosensors reveals useful health-related information. PLoS Biol. 15, e2001402 (2017).
Zhou, W. et al. Longitudinal multi-omics of host-microbe dynamics in prediabetes. Nature 569, 663–671 (2019).
Mias, G. I. et al. Longitudinal saliva omics responses to immune perturbation: a case study. Sci. Rep. 11, 710 (2021).
Haney, N. M., Urman, A., Waseem, T., Cagle, Y. & Morey, J. M. AI’s role in deep space. J. Med. Artif. Intell. 3, 11 (2020).
Yu, K.-H., Beam, A. L. & Kohane, I. S. Artificial intelligence in healthcare. Nat. Biomed. Eng. 2, 719–731 (2018).
Topol, E. J. Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again (Basic Books, 2019).
Topol, E. J. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25, 44–56 (2019).
Garrett-Bakelman, F. E. et al. The NASA Twins Study: a multidimensional analysis of a year-long human spaceflight. Science 364, eaau8650 (2019).
Thompson, D. E. Space Technology—Game Changing Development NASA Facts: Autonomous Medical Operations. NASA Technology Reports Server (NASA, 2018).
Walton, M. E. & Kerstman, E. L. Quantification of medical risk on the International Space Station using the Integrated Medical Model. Aerosp. Med. Hum. Perform. 91, 332–342 (2020).
Sipes, W., Holland, A. & Beven, G. in Handbook of Bioastronautics (eds Young, L. R. & Sutton, J. P.) 425–436 (Springer, 2021).
McGregor, C. A platform for real-time space health analytics as a service utilizing space data relays. In Proc. 2021 IEEE Aerospace Conference (50100) 1–14 (IEEE, 2021).
McGregor, C. A platform for real-time online health analytics during spaceflight. In Proc. 2013 IEEE Aerospace Conference 1–8 (IEEE, 2013).
Mindock, J. et al. Systems engineering for space exploration medical capabilities. In Proc. AIAA SPACE and Astronautics Forum and Exposition 139, 306–312 (American Institute of Aeronautics and Astronautics, 2017).
Schneider, W. F. et al. NASA environmental control and life support technology development and maturation for exploration: 2019 to 2020 overview. In Proc. International Conference on Environmental Systems 200, 1–12 (2021).
Broyan, J. L., Shaw, L., Mc Kinley, M., Meyer, C. & Ewert, M. K. NASA environmental control and life support technology development for exploration: 2020 to 2021 overview. In Proc. 50th International Conference on Environmental Systems 384, 1–12 (NASA, 2021).
Williams-Byrd, J. A. et al. Implementing NASA’s capability-driven approach: insight into NASA’s processes for maturing exploration systems. In AIAA SPACE 2015 Conference and Exposition (American Institute of Aeronautics and Astronautics, 2015).
Goel, N. & Dinges, D. F. Predicting risk in space: genetic markers for differential vulnerability to sleep restriction. Acta Astronaut. 77, 207–213 (2012).
Limkakeng, A. T. Jr. et al. Systematic molecular phenotyping: a path toward precision emergency medicine? Acad. Emerg. Med. 23, 1097–1106 (2016).
Clément, G. R. et al. Challenges to the central nervous system during human spaceflight missions to Mars. J. Neurophysiol. 123, 2037–2063 (2020).
Fitzgerald, J. et al. Future of biomarker evaluation in the realm of artificial intelligence algorithms: application in improved therapeutic stratification of patients with breast and prostate cancer. J. Clin. Pathol. 74, 429–434 (2021).
Weiss, J., Hoffmann, U. & Aerts, H. J. W. L. Artificial intelligence-derived imaging biomarkers to improve population health. Lancet Digit. Health 2, e154–e155 (2020).
Strangman, G. E. et al. Deep-space applications for point-of-care technologies. Curr. Opin. Biomed. Eng. 11, 45–50 (2019).
Budd, S. et al. Prototyping CRISP: a Causal Relation and Inference Search Platform applied to colorectal cancer data. In Proc. IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech) 517–521 (IEEE, 2021).
Schmidt, M. A. & Goodwin, T. J. Personalized medicine in human space flight: using Omics based analyses to develop individualized countermeasures that enhance astronaut safety and performance. Metabolomics 9, 1134–1156 (2013).
Low, L. A., Mummery, C., Berridge, B. R., Austin, C. P. & Tagle, D. A. Organs-on-chips: into the next decade. Nat. Rev. Drug Discov. 20, 345–361 (2021).
Tissue Chips in Space https://ncats.nih.gov/tissuechip/projects/space (NIH, 2016).
Yeung, C. K. et al. Tissue chips in space—challenges and opportunities. Clin. Transl. Sci. 13, 8–10 (2020).
Papalexi, E. & Satija, R. Single-cell RNA sequencing to explore immune cell heterogeneity. Nat. Rev. Immunol. 18, 35–45 (2018).
Gertz, M. L. et al. Multi-omic, single-cell, and biochemical profiles of astronauts guide pharmacological strategies for returning to gravity. Cell Rep. 33, 108429 (2020).
Spitzer, M. H. & Nolan, G. P. Mass cytometry: single cells, many features. Cell 165, 780–791 (2016).
Lakshmikanth, T. et al. Human immune system variation during 1 year. Cell Rep. 32, 107923 (2020).
Hartmann, F. J. et al. Comprehensive immune monitoring of clinical trials to advance human immunotherapy. Cell Rep. 28, 819–831 (2019).
Emerson, R. O. et al. Immunosequencing identifies signatures of cytomegalovirus exposure history and HLA-mediated effects on the T cell repertoire. Nat. Genet. 49, 659–665 (2017).
Tesei, D., Jewczynko, A., Lynch, A. M. & Urbaniak, C. Understanding the complexities and changes of the astronaut microbiome for successful long-duration space missions. Life 12, 495 (2022).
Cervantes, J. L. & Hong, B.-Y. Dysbiosis and immune dysregulation in outer space. Int. Rev. Immunol. 35, 67–82 (2016).
Malkani, S. et al. Circulating miRNA spaceflight signature reveals targets for countermeasure development. Cell Rep. 33, 108448 (2020).
Bezdan, D. et al. Cell-free DNA (cfDNA) and exosome profiling from a year-long human spaceflight reveals circulating biomarkers. iScience 23, 101844 (2020).
Mencia-Trinchant, N. et al. Clonal hematopoiesis before, during and after human spaceflight. Cell Rep. 33, 108458 (2020).
Pariset, E. et al. DNA damage baseline predicts resilience to space radiation and radiotherapy. Cell Rep. 33, 108434 (2020).
Bruce-Keller, A. J., Salbaum, J. M. & Berthoud, H.-R. Harnessing gut microbes for mental health: getting from here to there. Biol. Psychiatry 83, 214–223 (2018).
Obermeyer, Z., Samra, J. K. & Mullainathan, S. Individual differences in normal body temperature: longitudinal big data analysis of patient records. Brit. Med. J. 359, j5468 (2017).
Manor, O. et al. Health and disease markers correlate with gut microbiome composition across thousands of people. Nat. Commun. 11, 5206 (2020).
Price, N. D. et al. A wellness study of 108 individuals using personal, dense, dynamic data clouds. Nat. Biotechnol. 35, 747–756 (2017).
Masison, J. et al. A modular computational framework for medical digital twins. Proc. Natl Acad. Sci. USA 118, e2024287118 (2021).
Penninckx, S. et al. Dose, LET and strain dependence of radiation-induced 53BP1 foci in 15 mouse strains ex vivo introducing novel DNA damage metrics. Radiat. Res. 192, 1–12 (2019).
Pariset, E. et al. 53BP1 repair kinetics for prediction of in vivo radiation susceptibility in 15 mouse strains. Radiat. Res. 194, 485–499 (2020).
Or, F., Torous, J. & Onnela, J.-P. High potential but limited evidence: using voice data from smartphones to monitor and diagnose mood disorders. Psychiatr. Rehabil. J. 40, 320–324 (2017).
Gratzer, D. & Goldbloom, D. Therapy and E-therapy-preparing future psychiatrists in the era of apps and chatbots. Acad. Psychiatry 44, 231–234 (2020).
Gaffney, H., Mansell, W. & Tai, S. Conversational agents in the treatment of mental health problems: mixed-method systematic review. JMIR Ment. Health 6, e14166 (2019).
Gulshan, V. et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316, 2402–2410 (2016).
Lee, A. G. et al. Spaceflight associated neuro-ocular syndrome (SANS) and the neuro-ophthalmologic effects of microgravity: a review and an update. NPJ Microgravity 6, 7 (2020).
De Fauw, J. et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat. Med. 24, 1342–1350 (2018).
Poplin, R. et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat. Biomed. Eng. 2, 158–164 (2018).
Taibbi, G. et al. Opposite response of blood vessels in the retina to 6° head-down tilt and long-duration microgravity. NPJ Microgravity 7, 38 (2021).
Wang, Y. & Schork, N. J. Power and design issues in crossover-based N-of-1 clinical trials with fixed data collection periods. Healthcare (Basel) 7, 84 (2019).
Schork, N. J. & Goetz, L. H. Single-subject studies in translational nutrition research. Annu. Rev. Nutr. 37, 395–422 (2017).
Ray, S. et al. GeneLab: omics database for spaceflight experiments. Bioinformatics 35, 1753–1759 (2019).
Berrios, D. C., Galazka, J., Grigorev, K., Gebre, S. & Costes, S. V. NASA GeneLab: interfaces for the exploration of space omics data. Nucleic Acids Res. 49, D1515–D1522 (2021).
Scott, R. T. et al. Advancing the integration of biosciences data sharing to further enable space exploration. Cell Rep. 33, 108441 (2020).
Scott, R. T. et al. Open science for the next decade of life and physical sciences research for deep space exploration. A White Paper Submitted on 23 December 2021 to the Committee on Biological and Physical Sciences in Space for the 2023–2032 Decadal Survey (National Academies Press, 2021).
Schimmerling, W. Space Radiation Dosimetry https://three.jsc.nasa.gov/articles/dosimetryposted3.pdf (NASA, 2009).
Yasuda, H. Effective dose measured with a life size human phantom in a low Earth orbit mission. J. Radiat. Res. 50, 89–96 (2009).
Kroupa, M. et al. A semiconductor radiation imaging pixel detector for space radiation dosimetry. Life Sci. Space Res. 6, 69–78 (2015).
Lassmann, M. & Eberlein, U. The relevance of dosimetry in precision medicine. J. Nucl. Med. 59, 1494–1499 (2018).
Blue, R. S. et al. Limitations in predicting radiation-induced pharmaceutical instability during long-duration spaceflight. NPJ Microgravity 5, 15 (2019).
Horneck, G. Biological monitoring of radiation exposure. Adv. Space Res. 22, 1631–1641 (1998).
Wang, A., Nguyen, D., Sridhar, A. R. & Gollakota, S. Using smart speakers to contactlessly monitor heart rhythms. Commun. Biol. 4, 319 (2021).
Park, S.-M. et al. A mountable toilet system for personalized health monitoring via the analysis of excreta. Nat. Biomed. Eng. 4, 624–635 (2020).
Ge, T. J., Chan, C. T., Lee, B. J., Liao, J. C. & Park, S.-M. Smart toilets for monitoring COVID-19 surges: passive diagnostics and public health. NPJ Digit. Med. 5, 39 (2022).
Hook, J. V. et al. Nebulae: a proposed concept of operation for deep space computing clouds. In Proc. 2020 IEEE Aerospace Conference 1–14 (IEEE, 2020).
Keys, A., Adams, J., Cressler, J., Johnson, M. & Patrick, M. A review of NASA’s radiation-hardened electronics for space environments project. In Proc. AIAA SPACE 2008 Conference & Exposition 1–7 (American Institute of Aeronautics and Astronautics, 2008).
Fernandez, M. HPC in space: an update on spaceborne computer after 1+ year on the ISS. In Proc. The International Conference for High Performance Computing, Networking, Storage, and Analysis (2018).
McIntyre, A. B. R. et al. Single-molecule sequencing detection of N6-methyladenine in microbial reference materials. Nat. Commun. 10, 579 (2019).
Azar, J., Makhoul, A., Barhamgi, M. & Couturier, R. An energy efficient IoT data compression approach for edge machine learning. Future Gener. Comput. Syst. 96, 168–175 (2019).
Monarch, R. (Munro). Human-in-the-Loop Machine Learning: Active Learning and Annotation for Human-centered AI (Manning, 2021).
Nangle, S. N. et al. The case for biotech on Mars. Nat. Biotechnol. 38, 401–407 (2020).
SBIR. Deep Neural Net and Neuromorphic Processors for In-Space Autonomy and Cognition (NASA Small Business Innovation Research, 2020).
Nelson, C. A. et al. Knowledge network embedding of transcriptomic data from spaceflown mice uncovers signs and symptoms associated with terrestrial diseases. Life 11, 42 (2021).
O’Donoghue, O. et al. Invariant risk minimisation for cross-organism inference: substituting mouse data for human data in human risk factor discovery. Preprint at https://arxiv.org/abs/2111.07348 (2021).
Adler-Milstein, J., Chen, J. H. & Dhaliwal, G. Next-generation artificial intelligence for diagnosis: from predicting diagnostic labels to ‘wayfinding’. JAMA 326, 2467–2468 (2021).
Erdemir, A. et al. Credible practice of modeling and simulation in healthcare: ten rules from a multidisciplinary perspective. J. Transl. Med. 18, 369 (2020).
IEEE Standards Association. IEEE Standard Model Process for Addressing Ethical Concerns during System Design. IEEE 1–82 (IEEE, 2021).
Joly, Y., Saulnier, K. M., Osien, G. & Knoppers, B. M. The ethical framing of personalized medicine. Curr. Opin. Allergy Clin. Immunol. 14, 404–408 (2014).
Li, T., Sahu, A. K., Talwalkar, A. & Smith, V. Federated learning: challenges, methods and future directions. IEEE Signal Process. Mag. 37, 50–60 (2020).
Green, R. C., Lautenbach, D. & McGuire, A. L. GINA, genetic discrimination and genomic medicine. N. Engl. J. Med. 372, 397–399 (2015).
Lavin, A. et al. Technology readiness levels for machine learning systems. Nat. Commun. 13, 6039 (2022).
Reynolds, R. J. & Shelhamer, M. In Beyond LEO (ed. Reynolds, R. J.) 1–7 (IntechOpen, 2020).
Hanson, A. et al. A model-based systems engineering approach to exploration medical system development. In Proc. 2019 IEEE Aerospace Conference 1–19 (IEEE, 2019).
Auñón-Chancellor, S. M., Pattarini, J. M., Moll, S. & Sargsyan, A. Venous thrombosis during spaceflight. N. Engl. J. Med. 382, 89–90 (2020).
Marshall-Goebel, K. et al. Assessment of jugular venous blood flow stasis and thrombosis during spaceflight. JAMA Netw. Open 2, e1915011 (2019).
Vyas, R. J. et al. Decreased vascular patterning in the retinas of astronaut crew members as new measure of ocular damage in spaceflight-associated neuro-ocular syndrome. Invest. Ophthalmol. Vis. Sci. 61, 34 (2020).
Lagatuz, M. et al. Vascular patterning as integrative readout of complex molecular and physiological signaling by VESsel GENeration Analysis. J. Vasc. Res. 58, 207–230 (2021).
Weng, S. F., Reps, J., Kai, J., Garibaldi, J. M. & Qureshi, N. Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS ONE 12, e0174944 (2017).
Paschalidis, Y. How machine learning is helping us predict heart disease and diabetes. Harvard Business Review (30 May 2017).
Lee, S. M. C., Stenger, M. B., Laurie, S. S. & Macias, B. R. Evidence report: risk of cardiac rhythm problems during spaceflight. NASA Human Research Roadmap (NASA, 2017).
Strodthoff, N. & Strodthoff, C. Detecting and interpreting myocardial infarction using fully convolutional neural networks. Physiol. Meas. 40, 015001 (2019).
Hannun, A. Y. et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat. Med. 25, 65–69 (2019).
Nasir, M., Baucom, B. R., Georgiou, P. & Narayanan, S. Predicting couple therapy outcomes based on speech acoustic features. PLoS ONE 12, e0185123 (2017).
Frankel, J. How artificial intelligence could help diagnose mental disorders. The Atlantic (23 August 2016).
Landon, L. B., Slack, K. J. & Barrett, J. D. Teamwork and collaboration in long-duration space missions: going to extremes. Am. Psychol. 73, 563–575 (2018).
Willams, R. S. & Davis, J. R. A critical strategy: ensuring behavioral health during extended-duration space missions. Aviat. Space Environ. Med. 76, B1–B2 (2005).
Nelson, C. A., Bove, R., Butte, A. J. & Baranzini, S. E. Embedding electronic health records onto a knowledge network recognizes prodromal features of multiple sclerosis and predicts diagnosis. J. Am. Med. Inform. Assoc. 29, 424–434 (2021).
Stingl, J. C., Welker, S., Hartmann, G., Damann, V. & Gerzer, R. Where failure is not an option—personalized medicine in astronauts. PLoS ONE 10, e0140764 (2015).
Blue, R. S. et al. Supplying a pharmacy for NASA exploration spaceflight: challenges and current understanding. NPJ Microgravity 5, 14 (2019).
Ashley, E. A. Towards precision medicine. Nat. Rev. Genet. 17, 507–522 (2016).
Wesseling, P. & Capper, D. WHO 2016 classification of gliomas. Neuropathol. Appl. Neurobiol. 44, 139–150 (2018).
Baribeau, Y. et al. Handheld point-of-care ultrasound probes: the new generation of POCUS. J. Cardiothorac. Vasc. Anesth. 34, 3139–3145 (2020).
Hashimoto, D. A., Rosman, G., Rus, D. & Meireles, O. R. Artificial intelligence in surgery: promises and perils. Ann. Surg. 268, 70–76 (2018).
Haidegger, T., Sándor, J. & Benyó, Z. Surgery in space: the future of robotic telesurgery. Surg. Endosc. 25, 681–690 (2011).
Akkus, Z. et al. A survey of deep-learning applications in ultrasound: artificial intelligence-powered ultrasound for improving clinical workflow. J. Am. Coll. Radiol. 16, 1318–1328 (2019).
Bowness, J., Varsou, O., Turbitt, L. & Burkett-St Laurent, D. Identifying anatomical structures on ultrasound: assistive artificial intelligence in ultrasound-guided regional anesthesia. Clin. Anat. 34, 802–809 (2021).
Mertens, C. J., Slaba, T. C. & Hu, S. Active dosimeter-based estimate of astronaut acute radiation risk for real-time solar energetic particle events. Space Weather 16, 1291–1316 (2018).
Toscano, W. et al. Wearable biosensor monitor to support autonomous crew health and readiness to perform. NASA Technology Reports Server (NASA, 2017).
Marković, D., Mizrahi, A., Querlioz, D. & Grollier, J. Physics for neuromorphic computing. Nat. Rev. Phys. 2, 499–510 (2020).
Liu, D., Yu, H. & Chai, Y. Low‐power computing with neuromorphic engineering. Adv. Intell. Syst. 3, 2000150 (2021).
Goecks, J., Jalili, V., Heiser, L. M. & Gray, J. W. How machine learning will transform biomedicine. Cell 181, 92–101 (2020).
Banbury, C. R. et al. Benchmarking TinyML systems: challenges and direction. Preprint at https://arxiv.org/abs/2003.04821 (2020).
Wang, Y., Yao, Q., Kwok, J. T. & Ni, L. M. Generalizing from a few examples: a survey on few-shot learning. ACM Comput. Surv. 53, 1–34 (2020).
Hu, F., Xie, D. & Shen, S. On the application of the Internet of Things in the field of medical and health care. In Proc. 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing 2053–2058 (IEEE, 2013).
Cheng, X. & Liu, H. A novel post-processing method based on a weighted composite filter for enhancing semantic segmentation results. Sensors (Basel) 20, 5500 (2020).
Jiang, H. & Nachum, O. Identifying and correcting label bias in machine learning. In Proc. 23rd Intl. Conf. on AISTATS 1–10 (2020).
Krueger, D. et al. Out-of-distribution generalization via risk extrapolation (REx). In Proc. 38th Intl. Conf. on MachineLearning 139, 5815–5826 (PMLR, 2021).
Nguyen, A.T., Tran, T., Gal, Y. & Baydin, A. G. Domain invariant representation learning with domain density transformations. Preprint at https://arxiv.org/pdf/2102.05082.pdf (2021).
Jin, Z., Sun, Y. & Cheng, A. C. Predicting cardiovascular disease from real-time electrocardiographic monitoring: an adaptive machine learning approach on a cell phone. In Proc. 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society 6889–6892 (IEEE, 2009).
Acknowledgements
We thank all June 2021 participants and speakers at the ‘NASA Workshop on Artificial Intelligence & Modeling for Space Biology’. Thanks go to the NASA Space Biology Program, part of the NASA Biological and Physical Sciences Division within the NASA Science Mission Directorate, as well as the NASA Human Research Program (HRP). We also thank the Space Biosciences Division and Space Biology at Ames Research Center (ARC), especially D. Ly, R. Vik and P. Vaishampayan. We are grateful for the support provided by NASA GeneLab and the NASA Ames Life Sciences Data Archive. Additional thanks go to S. Bhattacharya (NASA Space Biology Program Scientist), K. Martin (ARC Lead of Exploration Medical Capability (an Element of HRP)), as well as L. Lewis (ARC NASA HRP Lead). S.V.C. is funded by NASA Human Research Program grant NNJ16HP24I. S.E.B. holds the Heidrich Family and Friends Endowed Chair in Neurology at UCSF. S.E.B. also holds the Distinguished Professorship I in Neurology at UCSF. S.E.B. is funded by an NSF Convergence Accelerator award (2033569) and NIH/NCATS Translator award (1OT2TR003450). G.I.M. was supported by the Translational Research Institute for Space Health, through NASA NNX16AO69A (project no. T0412). E.L.A. was supported by the Translational Research Institute for Space Health, through NASA NNX16AO69A. C.E.M. acknowledges NASA grants NNX14AH50G and NNX17AB26G. This work was also part of the DOE Agile BioFoundry, supported by the US Department of Energy, Energy Efficiency and Renewable Energy, Bioenergy Technologies Office, and the DOE Joint BioEnergy Institute, supported by the Office of Science, Office of Biological and Environmental Research, through contract no. DE-AC02-05CH11231 between Lawrence Berkeley National Laboratory and the US Department of Energy. S.V.K. is funded by the Canadian Space Agency (19HLSRM04) and Natural Sciences and Engineering Research Council (NSERC, RGPIN-288253). J.H.Y. is funded by NIH grant no. R00 GM118907 and the Agilent Early Career Professor Award.
Author information
Authors and Affiliations
Contributions
All authors contributed ideas and discussion during the joint workshop writing session or were speakers at the ‘NASA Workshop on Artificial Intelligence & Modeling for Space Biology’. R.T.S., L.M.S. and S.V.C. prepared the manuscript. All authors provided input and feedback on the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Machine Intelligence thanks the anonymous reviewers for their contribution to the peer review of this work.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Scott, R.T., Sanders, L.M., Antonsen, E.L. et al. Biomonitoring and precision health in deep space supported by artificial intelligence. Nat Mach Intell 5, 196–207 (2023). https://doi.org/10.1038/s42256-023-00617-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s42256-023-00617-5
This article is cited by
-
Physiological evidence of stress reduction during a summer Antarctic expedition with a significant influence of previous experience and vigor
Scientific Reports (2024)
-
Augmented and Virtual Reality-Based Cyber Twin Model for Observing Infants in Intensive Care: 6G for Smart Healthcare 4.0 by Machine Learning Techniques
Wireless Personal Communications (2024)
-
Biological research and self-driving labs in deep space supported by artificial intelligence
Nature Machine Intelligence (2023)