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Shaban-Nejad A, Michalowski M, Buckeridge DL. Health intelligence: how artificial intelligence transforms population and personalized health. NPJ Digit Med. 2018;1:53doi: 10.1038/s41746-018-0058-9.
Shaban-Nejad, A., Michalowski, M., & Buckeridge, D. L. (2018). Health intelligence: how artificial intelligence transforms population and personalized health. NPJ digital medicine, 153. https://doi.org/10.1038/s41746-018-0058-9
Shaban-Nejad, Arash, et al. "Health intelligence: how artificial intelligence transforms population and personalized health." NPJ digital medicine vol. 1 (2018): 53. doi: https://doi.org/10.1038/s41746-018-0058-9
Shaban-Nejad A, Michalowski M, Buckeridge DL. Health intelligence: how artificial intelligence transforms population and personalized health. NPJ Digit Med. 2018 Oct 02;1:53. doi: 10.1038/s41746-018-0058-9. eCollection 2018. PMID: 31304332; PMCID: PMC6550150.
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NPJ Digit Med. 2018 Oct 02;1:53. doi: 10.1038/s41746-018-0058-9. eCollection 2018.

Health intelligence: how artificial intelligence transforms population and personalized health.

NPJ digital medicine

Arash Shaban-Nejad, Martin Michalowski, David L Buckeridge

Affiliations

  1. 1Department of Pediatrics, University of Tennessee Health Science Center - Oak Ridge National Laboratory (UTHSC-ORNL) Center for Biomedical Informatics, Memphis, TN USA.
  2. 2School of Nursing, University of Minnesota, Minneapolis, MN USA.
  3. Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC USA.

PMID: 31304332 PMCID: PMC6550150 DOI: 10.1038/s41746-018-0058-9

Abstract

Advances in computational and data sciences for data management, integration, mining, classification, filtering, visualization along with engineering innovations in medical devices have prompted demands for more comprehensive and coherent strategies to address the most fundamental questions in health care and medicine. Theory, methods, and models from artificial intelligence (AI) are changing the health care landscape in clinical and community settings and have already shown promising results in multiple applications in healthcare including, integrated health information systems, patient education, geocoding health data, social media analytics, epidemic and syndromic surveillance, predictive modeling and decision support, mobile health, and medical imaging (e.g. radiology and retinal image analyses). Health intelligence uses tools and methods from artificial intelligence and data science to provide better insights, reduce waste and wait time, and increase speed, service efficiencies, level of accuracy, and productivity in health care and medicine.

Keywords: Population screening; Research management

Conflict of interest statement

Competing interestsThe authors declare no competing interests.

References

  1. N Engl J Med. 2015 Feb 26;372(9):793-5 - PubMed
  2. Ann N Y Acad Sci. 2017 Jan;1387(1):44-53 - PubMed
  3. J Biomed Inform. 2017 Feb;66:52-71 - PubMed

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