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.
“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]
The Artificial intelligence section says AI recognizes sophisticated patterns in images and describes a breast-cancer algorithm that combines mammograms with health records to help detect cancer. A separate passage on nongenomic risk prediction discusses detecting disease at an earlier state using wearable data, but the paper does not establish that AI analysis of medical images and patient data detects diseases at their earliest stages generally. Page numbers are not available in our copy. (LLM, conf=0.85)
Experimental — accuracy not yet measuredEarly detection by AI-powered diagnostic tools can lead to better outcomes and lower treatment costs.
“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]
The Summary (page unavailable) says that prevention and early detection could decrease disease burden and the cost of preventable health care, supporting the claim’s possible benefit to outcomes and costs. It does not specifically say that early detection lowers treatment costs. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThe 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 potential applications to improve diagnosis and clinical decisions.
“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]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredThe use of a fairness-aware algorithm improves the performance of the model and provides a fairer diagnosis for all patients.
“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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Microsystem Advances through Integration with Artificial Intelligence
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- 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.
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