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Precision Medicine, AI, and the Future of Personalized Health Care
Open accessAbstract
The convergence of artificial intelligence (AI) and precision medicine promises to revolutionize health care. Precision medicine methods identify phenotypes of patients with less-common responses to treatment or unique healthcare needs. AI leverages sophisticated computation and inference to generate insights, enables the system to reason and learn, and empowers clinician decision making through augmented intelligence. Recent literature suggests that translational research exploring this convergence will help solve the most difficult challenges facing precision medicine, especially those in which nongenomic and genomic determinants, combined with information from patient symptoms, clinical history, and lifestyles, will facilitate personalized diagnosis and prognostication.
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“AI-powered diagnostic tools can detect diseases at their earliest stages by identifying subtle patterns and anomalies in medical images and patient data.”
From Ethical implications of AI and robotics in healthcare: A review (2023) · cites this paper as [12]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“This early detection can lead to better outcomes and lower treatment costs.[12]”
From Ethical implications of AI and robotics in healthcare: A review (2023) · cites this paper as [12]
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“The promising utility of AI in health care has been outlined previously with possible benefits in personalized medicine, drug discovery, and the analysis of large datasets aside from the potential applications to improve diagnosis and clinical decisions [17,18].”
From ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns (2023) · cites this paper as [17]
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“The use of a fairness-aware algorithm improves the performance of the model and provides a fairer diagnosis for all patients [279,280].”
From AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications (2023) · cites this paper as [280]
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1 retracted
RETRACTED ARTICLE: LSTM Model for Prediction of Heart Failure in Big Data
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The UK Biobank resource with deep phenotyping and genomic data
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BackgroundScalable and accurate deep learning with electronic health records
Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., et al. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0029-1 · Cited by 2,648 · cited 1×
“In the provider space, AI is used for evidence-based clinical decision support,(11) detection of adverse events, and the usage of electronic health record (EHR) data to predict patients at risk for readmission.(12) Healthcare policymakers and government use AI-based tools to control and predict infections and outbreak…”
MethodTing, D. S. W., Cheung, C. Y.-L., Lim, G., Tan, G. S. W., Quang, N. D., Gan, A., Hamzah, H., Garcia-Franco, R., San Yeo, I. Y., Lee, S. Y., et al. (2017) · JAMA · doi:10.1001/jama.2017.18152 · Cited by 2,402
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Scalable and accurate deep learning with electronic health records.
Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., et al. (2018) · eScholarship (California Digital Library) · Cited by 2,242
Flagged first, then by citations
Research works printed but not linked: 4
- Matheny M. E., 2019, AI in Health Care: The Hope, the Hype, the Promise, the Peril
- IBM and Partners to Transform Personal Health with Watson and Open Cloud. <https://www‐03.ibm.com/press/us/en/pressrelease/46580.wss>2015.
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Metadata
- Authors
- Johnson, K. B., Wei, W., Weeraratne, D., Frisse, M. E., Misulis, K., Rhee, K., Zhao, J., & Snowdon, J. L.
- Affiliations
- Vanderbilt University Medical Center (US)
- IBM (United States) (US)
- Corresponding author
- Johnson, Kevin B.
- ORCID
- Venue
- Clinical and Translational Science
- Publisher
- Wiley
- Published
- 2020-10-12
- Volume
- 14
- Issue
- 1
- Pages
- 86-93
- ISSN
- 1752-8054 (ISSN-L)
- 1752-8062
- PMC
- PMC7877825
- OpenAlex
- W3088017652
- Cited by
- 1,854
- References
- 90
- Citations by year
- 2026: 382 · 2025: 636 · 2024: 529 · 2023: 208 · 2022: 82 · 2021: 15 · 2020: 1
- FWCI
- 5.77
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Clinical and Translational Science (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Vanderbilt University
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- precision medicine, artificial intelligence, personalized healthcare, augmented intelligence, clinician decision-making, translational research, genomic determinants, personalized diagnosis, outcome prediction
- MeSH terms
- Artificial Intelligence, Delivery of Health Care, Forecasting, Humans, Risk Assessment, Genetic Predisposition to Disease, Translational Research, Biomedical, Precision Medicine, Patient-Specific Modeling
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