The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
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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“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]
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Cites
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Collins, F. S., & Varmus, H. (2015) · New England Journal of Medicine · doi:10.1056/nejmp1500523 · Cited by 5,202
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Shaban‐Nejad, A., Lavigne, M., Okhmatovskaia, A., & Buckeridge, D. L. (2016) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.13271 · Cited by 96
Self-citationShin, E. K., Mahajan, R., Akbilgiç, O., & Shaban‐Nejad, A. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0056-y · Cited by 66
Self-citationA principled machine learning framework improves accuracy of stage II colorectal cancer prognosis
Dimitriou, N., Arandjelović, O., Harrison, D. J., & Caie, P. D. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0057-x · Cited by 59
Wilk, S., Michalowski, M., Michalowski, W., Rosu, D., Carrier, M., & Kezadri-Hamiaz, M. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.12.002 · Cited by 53
Self-citationTracking health seeking behavior during an Ebola outbreak via mobile phones and SMS
Feng, S., Grépin, K. A., & Chunara, R. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0055-z · Cited by 24
Online Public Health Intelligence: Ethical Considerations at the Big Data Era
Mamiya, H., Shaban‐Nejad, A., & Buckeridge, D. L. (2017) · Lecture notes in social networks · doi:10.1007/978-3-319-68604-2_8 · Cited by 14
Self-citationPublic Health Intelligence and the Internet
Buckeridge, D. L., Brownstein, J. S., & Shaban‐Nejad, A. (2017) · Lecture notes in social networks · doi:10.1007/978-3-319-68604-2 · Cited by 5
Self-citationWilk, S., O’Sullivan, D. M., Michalowski, M., Bonaccio, S., Michalowski, W. J., Mor Peleg, B., & Carrier, M. (2017) · NORMA · Cited by 4
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Applications of artificial intelligence and machine learning in smart cities
Ullah, Z., Al-Turjman, F., Mostarda, L., & Gagliardi, R. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.02.069 · Cited by 756
The “inconvenient truth” about AI in healthcare
Panch, T., Mattie, H., & Celi, L. A. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0155-4 · Cited by 473
Global evolution of research in artificial intelligence in health and medicine: a bibliometric study
Tran, B. X., Vu, G. T., Ha, G. H., Vuong, Q.-H., Ho, M.-T., Vuong, T.-T., La, V.-P., Ho, M.-T., Nghiem, K.-C. P., Nguyen, H. L. T., et al. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8030360 · Cited by 472
Mukhamediev, R. I., Popova, Y., Kuchin, Y. I., Zaitseva, E., Kalimoldayev, A. M., Сымагулов, А., Levashenko, V., Abdoldina, F., Gopejenko, V. I., Yakunin, K., et al. (2022) · Mathematics · doi:10.3390/math10152552 · Cited by 415
Current status and applications of Artificial Intelligence (AI) in medical field: An overview
Haleem, A., Javaid, M., & Khan, I. H. (2019) · Current Medicine Research and Practice · doi:10.1016/j.cmrp.2019.11.005 · Cited by 285
Metadata
- 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
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