Health intelligence: how artificial intelligence transforms population and personalized health
Open accessAbstract
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.
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AI has become increasingly embedded in medical practice over the past decade, enhancing care capabilities from personalized health libraries and patient education to disease screening, diagnostics, and treatment planning.
“Artificial intelligence (AI) has become increasingly embedded in medical practice over the past decade, enhancing care capabilities from the development of personalized health libraries and innovative patient education strategies to disease screening, diagnostics, and treatment planning [1-4].”
From Empathic AI for Patient-Centered Cancer Care: A Scoping Review of Patient Navigation, Support, and Clinical Practice (2026) · cites this paper as [2]
The Abstract (page unavailable) describes AI applications in patient education, medical imaging, and decision support; Personalized health intelligence describes tailoring therapy plans and connecting patients with information. The copy does not establish a trend specifically over the past decade or describe the development of personalized health libraries. It discusses diagnosis and surveillance, but does not clearly identify disease screening as an application. (LLM, conf=0.85)
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Chokkakula, S., Chong, S., Yang, B., Jiang, H., Yu, J., Han, R., Attitalla, I. H., Yin, C., & Zhang, S. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1517981 · Cited by 12 · see citing works
Data Classification Model for COVID-19 Pandemic
Folorunso, S. O., Awotunde, J. B., Adeboye, N. O., & Matiluko, O. E. (2021) · Studies in systems, decision and control · doi:10.1007/978-3-030-77302-1_6 · Cited by 13 · see citing works
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Artificial Intelligence in Health care and its application in Saudi Arabia
Aljerian, N., Arafat, M., Aldhubib, A., Almohaimeed, I., Alsultan, A. K., Alhosaini, A., Aloqayli, A. I., Alrabiah, B., AlBanyan, S., Ashry, L., et al. (2022) · International Journal of Innovative Research in Medical Science · doi:10.23958/ijirms/vol07-i11/1558 · Cited by 14 · see citing works
Geospatial Artificial Intelligence (GeoAI): Applications in Health Care
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Understanding Self-Tracked Data from Bounded Situational Contexts
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The West Africa Ebola outbreak (2014-2016): a Health Intelligence failure?
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Artificial Intelligence and Human Rights
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The future of artificial intelligence in nursing
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Internet of medical things for enhanced smart healthcare systems
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- Authors
- Shaban-Nejad, A., Michalowski, M., & Buckeridge, D. L.
- Affiliations
- University of Minnesota (US)
- McGill University (CA)
- Venue
- npj Digital Medicine
- Published
- 2018-10-02
- Volume
- 1
- Issue
- 1
- Pages
- 53-53
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC6550150
- OpenAlex
- W2892370045
- Cited by
- 252
- References
- 12
- Citations by year
- 2026: 17 · 2025: 32 · 2024: 28 · 2023: 81 · 2022: 34 · 2021: 22 · 2020: 24 · 2019: 14
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- health intelligence, artificial intelligence in healthcare, population health, personalized healthcare, health information systems, patient education, social media analytics, disease surveillance, syndromic surveillance, predictive modeling, clinical decision support, mobile health, diagnostic imaging, radiology, retinal image analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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