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Artificial Intelligence Transforms the Future of Health Care
Open accessAbstract
Life sciences researchers using artificial intelligence (AI) are under pressure to innovate faster than ever. Large, multilevel, and integrated data sets offer the promise of unlocking novel insights and accelerating breakthroughs. Although more data are available than ever, only a fraction is being curated, integrated, understood, and analyzed. AI focuses on how computers learn from data and mimic human thought processes. AI increases learning capacity and provides decision support system at scales that are transforming the future of health care. This article is a review of applications for machine learning in health care with a focus on clinical, translational, and public health applications with an overview of the important role of privacy, data sharing, and genetic information.
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“ML algorithms, which analyze extensive clinical data and identify underlying patterns and trends, offer robust support for disease prediction and risk assessment [8, 9].”
From Machine learning with the body roundness index and associated indicators: a new approach to predicting metabolic syndrome (2025) · cites this paper as [8]
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“If patients are well informed about the way their information will be collected and used during their medical treatments, they will be able to decide their preferences regarding the privacy protection (Noorbakhsh-Sabet et al., 2019).”
From Responsible AI for Digital Health: a Synthesis and a Research Agenda (2023) · cites this paper as Noorbakhsh-Sabet et al., 2019
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Metadata
- Authors
- Noorbakhsh-Sabet, N., Zand, R., Zhang, Y., & Abedi, V.
- Affiliations
- St. Jude Children's Research Hospital (US)
- University of Tennessee Health Science Center (US)
- Geisinger Medical Center (US)
- Geisinger Health System (US)
- Corresponding author
- Abedi, Vida
- Venue
- The American Journal of Medicine
- Publisher
- Elsevier BV
- Published
- 2019-07
- Volume
- 132
- Issue
- 7
- Pages
- 795-801
- ISSN
- 0002-9343 (ISSN-L)
- PMC
- PMC6669105
- OpenAlex
- W2915053252
- Cited by
- 626
- References
- 68
- Citations by year
- 2026: 44 · 2025: 130 · 2024: 138 · 2023: 137 · 2022: 68 · 2021: 54 · 2020: 44 · 2019: 10
- FWCI
- 2.16
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- The American Journal of Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Heart, Lung, and Blood Institute (R56HL116832)
- 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
- artificial intelligence in healthcare, machine learning applications, clinical application, translational research, decision support system, data privacy, data sharing, genetic information
- MeSH terms
- Machine Learning, Artificial Intelligence, Delivery of Health Care, Forecasting, Humans, Decision Support Systems, Clinical, Drug Discovery, Translational Research, Biomedical, Epidemics
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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