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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.
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“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
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
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Son, N. K., Sangaiah, A. K., Paramarthika, L. K. M., Rajendran, V., Bian, G., & Alenazi, M. J. F. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2025.3572596 · Cited by 3
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Perenguez, M. N., Ramírez‐Montaño, D., Candelo, E., Echavarría, H. R., & de la Torre, A. (2024) · Current pharmacogenomics and personalized medicine (Online)/Current pharmacogenomics and personalized medicine · doi:10.2174/0118756921304274240819071740 · Cited by 3
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Chen, L., Zhao, Y., Hashemabad, S. K., Elze, T., Eslami, M., Wang, M., Wiggs, J. L., Segrè, A. V., & Zebardast, N. (2025) · medRxiv · doi:10.1101/2025.11.04.25339517 · Cited by 3
Callahan, P. (2023) · Delaware Journal of Public Health · doi:10.32481/djph.2023.11.003 · Cited by 2
Bjørklund, G., Bhat, R. S., & El-Ansary, A. K. (2024) · International journal for autism challenges & solution. · doi:10.54878/bgxvwf58 · Cited by 3
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Sharun, K., Banu, S. A., Mamachan, M., Abualigah, L., PAWDE, A. M., & Dhama, K. (2024) · Journal of Experimental Biology and Agricultural Sciences · doi:10.18006/2024.12(3).297.317 · Cited by 13
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Vandermeulen, M. D., Khaiwal, S., Rubio, G., Liti, G., & Cullen, P. J. (2024) · iScience · doi:10.1016/j.isci.2024.110860 · Cited by 2
Salve, M. J., Bais, V. R., Brahmane, P. V., & Shikalgar, N. F. K. (2024) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-4422-4.ch001 · Cited by 2
-, P. D. (2024) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2024.v06i06.33271 · Cited by 2
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Akyön, Ş. H., Akyön, F. Ç., Onur, G., & Arman, İ. H. (2024) · Eurasian Journal of Family Medicine · doi:10.33880/ejfm.2024130401 · Cited by 4
Dixit, R., Haque, S., Asif, F., & Jain, A. (2024) · Journal of Ayurvedic and Herbal Medicine · doi:10.4103/jahm.jahm_7_24 · Cited by 2
Pande, A., Kumar, A., & Anjankar, A. P. (2025) · Multidisciplinary Reviews · doi:10.31893/multirev.2025218 · Cited by 3
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Mishra, S. K., Chhetri, T., Balakrishnan, A., Roy, S., Gurung, K., Praba, J. J., & Georrge, J. J. (2025) · Information systems engineering and management · doi:10.1007/978-3-031-84404-1_5 · Cited by 2
Fan, X., Chen, L., Tang, W., Sun, L., Wang, J., Liu, S. M., Wang, S., Li, K., Wang, M., Cheng, Y., et al. (2025) · BMC Public Health · doi:10.1186/s12889-025-22430-y · Cited by 5
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Ahire, A. D., Mahajan, C. R., Girase, R. G., Pawar, A. R., & Patil, V. (2025) · Research Journal of Pharmacy and Technology · doi:10.52711/0974-4150.2025.00006 · Cited by 2
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Reddy, K. D., Anusha, S., & Ashalatha, N. (2025) · IgMin Research · doi:10.61927/igmin294 · Cited by 2
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Dhage, V. N., & Ponnusamy, S. (2025) · Advances in information security, privacy, and ethics book series · doi:10.4018/979-8-3373-0563-9.ch014 · Cited by 2
Guo, E., Sanguinetti, R. D., Boone, L., Deng, J., Shakil, H., & Gupta, M. (2025) · Brain and Spine · doi:10.1016/j.bas.2025.104271 · Cited by 2
Wang, D. D., & Huang, Y. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013074 · Cited by 2
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Usman, U. S., Ahsan, F., Alanjiro, M., Bataba, S. Y., Dallatu, J. A., Mahmood, T., Bano, S., Ansari, J. A., & Parveen, S. (2025) · Journal of intelligent medicine. · doi:10.1002/jim4.70003 · Cited by 3
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Ndlovu, K., Stein, N., Gaopelo, R., Monkge, M., Moen, L., & Molwantwa, M. C. (2025) · Oxford Open Digital Health · doi:10.1093/oodh/oqaf014 · Cited by 3
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Patel, R. H., Shukla, S., Patel, N. A., Sharma, P., Mevada, S., Singh, S., & Prajapati, B. G. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-43823-3.00009-1 · Cited by 2
Bhatia, N., Khan, M. M. U., Masih, D., & Srinivas, U. (2025) · Current Behavioral Neuroscience Reports · doi:10.1007/s40473-025-00318-w · Cited by 2
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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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