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The promise of AI and technology to improve quality of life and care for older adults

Nature Aging volume 3, pages 629–631 (2023) Cite this article

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By 2060, the number of adults aged 65 or older living in the USA is projected to reach 94.7 million and comprise almost one-quarter of the US population1. Older adults often live with conditions that affect their physical health, psychosocial well-being, cognition and ability to live independently, resulting in complex medical and social needs. Managing these needs requires a multidimensional, integrative approach that addresses not only medical factors but also psychosocial and community factors that affect health and well-being; however, the special training, techniques, tools and expertise to support such an approach are not available in many communities and healthcare settings.

A potential solution to addressing the complex health and well-being needs of older adults comes from artificial intelligence (AI), which is defined in this paper as the science of creating machines and technologies that can reason, learn, plan and make predictions in ways that could surpass human capabilities. In recent years, developments in AI and digital technologies have been working rapidly to profoundly revolutionize healthcare delivery. For example, machine learning can interpret data from wearable devices with sensors (such as smartwatches and smartphones) to monitor patient health and facilitate community care, as well as predict the likely outcome of a given intervention for a given patient2. Voice-based virtual assistants can help patients to implement treatment plans, improve medication adherence and reduce feelings of loneliness and social isolation. Computer vision algorithms enable inference of health conditions in older adults using diagnostic images such as those from computed tomography, magnetic resonance imaging, ultrasound and X-rays. These capabilities can empower patients to proactively take control of their healthcare, which can increase patient engagement, improve the patient experience and enhance well-being and health outcomes.

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Fig. 1: The AITC program.
The alternative text for this image may have been generated using AI.

References

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Acknowledgements

We thank D. Berlyne, C. Garcia, K. Goggans and K. Harr for editorial assistance with the manuscript, and J. Fairman for producing the original figure illustration. This work was supported by grants P30AG073104 (R.C., P.M.A. and J.D.W.), P30AG073107 (N.C. and D.G.), P30AG073105 (G.D., J.K. and J.H.M.), and U24AG073094 (R.M.L.) from the National Institute on Aging, part of the National Institutes of Health.

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Authors and Affiliations

  1. Johns Hopkins Medicine, Johns Hopkins University, Baltimore, MD, USA

    Peter M. Abadir, Rama Chellappa, Jeremy D. Walston, Alicia I. Arbaje, Thomas K. M. Cudjoe, Esther Oh, Christopher G. Chute, Phillip Phan, Quincy Samus & Nancy L. Schoenborn

  2. Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA

    Peter M. Abadir, Rama Chellappa, Najim Dehak, Mathias Unberath & Suchi Saria

  3. Harvard Medical School, Harvard University, Boston, MA, USA

    Niteesh Choudhry

  4. Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA

    Niteesh Choudhry

  5. School of Nursing, University of Pennsylvania, Philadelphia, PA, USA

    George Demiris

  6. Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    George Demiris & Jason Karlawish

  7. Manning College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA, USA

    Deepak Ganesan & Benjamin Marlin

  8. Rose Li and Associates, Inc., Chevy Chase, MD, USA

    Rose M. Li

  9. Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Jason H. Moore

  10. American Geriatrics Society https://www.americangeriatrics.org/

    Nancy Lundebjerg

  11. Johns Hopkins Carey Business School, Johns Hopkins University, Baltimore, MD, USA

    Phillip Phan

Authors
  1. Peter M. Abadir
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  2. Rama Chellappa
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  7. Rose M. Li
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  9. Jeremy D. Walston
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Consortia

Directors & Advisors of the AITCs

  • Peter M. Abadir
  • , Rama Chellappa
  • , Niteesh Choudhry
  • , George Demiris
  • , Deepak Ganesan
  • , Jason Karlawish
  • , Benjamin Marlin
  • , Rose M. Li
  • , Najim Dehak
  • , Alicia I. Arbaje
  • , Mathias Unberath
  • , Thomas K. M. Cudjoe
  • , Suchi Saria
  • , Esther Oh
  • , Nancy Lundebjerg
  • , Christopher G. Chute
  • , Jason H. Moore
  • , Phillip Phan
  • , Quincy Samus
  • , Nancy L. Schoenborn
  •  & Jeremy D. Walston

Contributions

All authors contributed to preparing the manuscript. P.M.A. led the writing of the manuscript.

Corresponding author

Correspondence to Peter M. Abadir.

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Competing interests

The authors declare no competing interests.

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Abadir, P.M., Chellappa, R., Choudhry, N. et al. The promise of AI and technology to improve quality of life and care for older adults. Nat Aging 3, 629–631 (2023). https://doi.org/10.1038/s43587-023-00430-0

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  • DOI: https://doi.org/10.1038/s43587-023-00430-0

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