Deep learning for healthcare: review, opportunities and challenges
Open accessAbstract
Gaining knowledge and actionable insights from complex, high-dimensional and heterogeneous biomedical data remains a key challenge in transforming health care. Various types of data have been emerging in modern biomedical research, including electronic health records, imaging, -omics, sensor data and text, which are complex, heterogeneous, poorly annotated and generally unstructured. Traditional data mining and statistical learning approaches typically need to first perform feature engineering to obtain effective and more robust features from those data, and then build prediction or clustering models on top of them. There are lots of challenges on both steps in a scenario of complicated data and lacking of sufficient domain knowledge. The latest advances in deep learning technologies provide new effective paradigms to obtain end-to-end learning models from complex data. In this article, we review the recent literature on applying deep learning technologies to advance the health care domain. Based on the analyzed work, we suggest that deep learning approaches could be the vehicle for translating big biomedical data into improved human health. However, we also note limitations and needs for improved methods development and applications, especially in terms of ease-of-understanding for domain experts and citizen scientists. We discuss such challenges and suggest developing holistic and meaningful interpretable architectures to bridge deep learning models and human interpretability.
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Multimodal DL approaches face the same challenges that DL in the biomedical field face generally, including data volume, quality, interpretability and temporality.
“Multimodal DL approaches face the same challenges that DL in the biomedical field face generally, including data volume, quality, interpretability and temporality as outlined by e.g. Miotto et al. [100].”
From Multimodal deep learning for biomedical data fusion: a review (2022) · cites this paper as [100]
Machine learning and deep learning models are attracting much attention and are proving to be very practical in the healthcare industry.
“The intelligent models of machine learning and deep learning are attracting much attention and are proving to be very practical [130,131] in the healthcare industry.”
From Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review (2023) · cites this paper as [130]
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Self-citation
- Authors
- Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T.
- Affiliations
- Icahn School of Medicine at Mount Sinai (US)
- Cornell University (US)
- University of California San Diego (US)
- Corresponding author
- Wang, Fei
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-05-06
- Volume
- 19
- Issue
- 6
- Pages
- 1236-1246
- ISSN
- 1467-5463 (ISSN-L)
- 1477-4054
- PMC
- PMC6455466
- OpenAlex
- W2610332124
- Cited by
- 3,076
- References
- 114
- Citations by year
- 2026: 353 · 2025: 538 · 2024: 422 · 2023: 393 · 2022: 371 · 2021: 345 · 2020: 310 · 2019: 216 · 2018: 111 · 2017: 15
- FWCI
- 26.29
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Briefings in Bioinformatics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (R01GM118609, 1650723)
- National Institutes of Health (U01TR002062, UL1TR000067)
- National Human Genome Research Institute (R00HG008175, R01HG007078)
- National Cancer Institute (U54-CA189201-02, U54CA189201)
- National Institute of General Medical Sciences (R01GM118609, R01GM114612)
- National Institute of Diabetes and Digestive and Kidney Diseases (R01-DK098242-03, R01DK098242)
- National Institute of Biomedical Imaging and Bioengineering (U01EB023685)
- U.S. National Library of Medicine (R00LM011392, R21LM012060)
- Division of Information and Intelligent Systems
- National Center for Advancing Translational Sciences
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- deep learning in healthcare, biomedical data, electronic health records, diagnostic imaging, omics data, sensor data, text mining, end-to-end learning, feature engineering, model interpretability, explainable AI, citizen science, unstructured data
- MeSH terms
- Deep Learning, Delivery of Health Care, Diagnostic Imaging, Humans, Telemedicine, Computational Biology, Genomics, Data Mining, Electronic Health Records
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 ›
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