Integrating multi-omics data with EHR for precision medicine using advanced artificial intelligence
Open accessAbstract
With the recent advancement of novel biomedical technologies such as high-throughput sequencing and wearable devices, multi-modal biomedical data ranging from multi-omics molecular data to real-time continuous bio-signals are generated at an unprecedented speed and scale every day. For the first time, these multi-modal biomedical data are able to make precision medicine close to a reality. However, due to data volume and the complexity, making good use of these multi-modal biomedical data requires major effort. Researchers and clinicians are actively developing artificial intelligence (AI) approaches for data-driven knowledge discovery and causal inference using a variety of biomedical data modalities. These AI-based approaches have demonstrated promising results in various biomedical and healthcare applications. In this review paper, we summarize the state-of-the-art AI models for integrating multi-omics data and electronic health records (EHRs) for precision medicine. We discuss the challenges and opportunities in integrating multi-omics data with EHRs and future directions. We hope this review can inspire future research and developing in integrating multi-omics data with EHRs for precision medicine.
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EHR data have been combined with multi-omics and imaging data to enhance diagnosis and prognosis for diseases such as Alzheimer's disease.
“EHR data have been combined with multi-omics and imaging data to enhance diagnosis and prognosis for a range of diseases, such as Alzheimer’s disease (AD) [104].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [104]
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BackgroundSelf-citation
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Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis
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- Authors
- Tong, L., Shi, W., Isgut, M., Zhong, Y., Lais, P., Gloster, L., Sun, J., Swain, A., Giuste, F., & Wang, M. D.
- Affiliations
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Georgia Institute of Technology (US)
- ORCID
- Venue
- IEEE Reviews in Biomedical Engineering
- Published
- 2024
- Volume
- 17
- Pages
- 80-97
- ISSN
- 1937-3333 (ISSN-L)
- 1941-1189
- OpenAlex
- W4387587504
- Cited by
- 130
- References
- 21
- Citations by year
- 2026: 48 · 2025: 61 · 2024: 21
- FWCI
- 2.71
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- IEEE Reviews in Biomedical Engineering (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Wallace H. Coulter Distinguished Faculty Fellowship
- Petit Institute Faculty Fellowship
- Amazon Faculty Fellowship
- Microsoft Research
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- multi-omics integration, electronic health records, precision medicine, artificial intelligence, wearable devices, next-generation sequencing, causal inference, physiological signals
- MeSH terms
- Multiomics, Artificial Intelligence, Delivery of Health Care, Humans, Precision Medicine, Electronic Health Records
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