The DOI matches its Crossref record; 90 of the 90 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
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
“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]
Experimental — accuracy not yet measuredIn the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
1 retracted
RETRACTED ARTICLE: LSTM Model for Prediction of Heart Failure in Big Data
Maragatham, G., & Devi, S. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1243-3 · Cited by 153
RetractedHigh-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450
The UK Biobank resource with deep phenotyping and genomic data
Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., Motyer, A., Vukcevic, D., Delaneau, O., O’Connell, J., et al. (2018) · Nature · doi:10.1038/s41586-018-0579-z · Cited by 8,876
Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., & Lloyd, S. (2017) · Nature · doi:10.1038/nature23474 · Cited by 4,779
The potential for artificial intelligence in healthcare
Davenport, T., & Kalakota, R. (2019) · Future Healthcare Journal · doi:10.7861/futurehosp.6-2-94 · Cited by 3,800
Ehteshami Bejnordi, B., Veta, M., Johannes van Diest, P., van Ginneken, B., Karssemeijer, N., Litjens, G., van der Laak, J. A. W. M., and the CAMELYON16 Consortium, Hermsen, M., Manson, Q. F., et al. (2017) · JAMA · doi:10.1001/jama.2017.14585 · Cited by 3,293 · cited 2×
“87 AI-mediated image analysis aimed at improving disease risk prediction and diagnosis will likely continue to increase in use for detection of diabetic retinopathy 88 and metastasis in cancer, 89 as well as for identification of benign melanoma.”
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
Artificial intelligence in medicine
Hamet, P., & Tremblay, J. (2017) · Metabolism · doi:10.1016/j.metabol.2017.01.011 · Cited by 2,334
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.
- Davenport H.T., 2018, Using AI to improve electronic health records, Harvard Bus. Rev., 12, 1
- National Research Council, 2011, Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease
Cited by
Sorted by citations
Items 1,001–1,003 of 1,003
Perry, N., Boulton, K., Hankin, L., Roisenberg, B. B., & Guastella, A. J. (2026) · Digital Health · doi:10.1177/20552076251411228 · Cited by 2
Chronic Disease Monitoring Using Advanced Compliant Materials for Bioelectronics
Kim, H., Bartlett, M., Wang, L., Bates, K., He, M., Kim, M., Majidi, C., & Cohen‐Karni, T. (2026) · Advanced Electronic Materials · doi:10.1002/aelm.202500885 · Cited by 2
Porrogi, P. (2026) · Journal of Personalized Medicine · doi:10.3390/jpm16040183 · Cited by 2
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
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 ›
Are you an author, or do you see a problem with this page? Request a review
