Machine learning for precision dermatology: Advances, opportunities, and outlook
Open accessThe 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
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?
ML and AI techniques have been applied to predict cancer prognosis and estimate treatment response based on genomic and clinical data.
“These techniques have been applied to predict cancer prognosis and estimate treatment response based on genomic and clinical data [8–14].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [11]
Unlike certain health specialties such as dermatology or ophthalmology, where ML and AI have been successfully adopted due to their reliance on visual evaluation and pattern recognition, the application of ML and AI in epidemiology presents unique challenges.
“Unlike certain health specialties such as dermatology or ophthalmology, where ML and AI have been successfully adopted due to their reliance on visual evaluation and pattern recognition [11], the application of ML and AI in epidemiology presents unique challenges.”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [11]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970 · see citing works
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162 · see citing works
Rajkomar, A., Dean, J., & Kohane, I. (2019) · New England Journal of Medicine · doi:10.1056/nejmra1814259 · Cited by 4,579 · see citing works
Multimodal skin lesion classification using deep learning
Yap, J., Yolland, W., & Tschandl, P. (2018) · Experimental Dermatology · doi:10.1111/exd.13777 · Cited by 326 · see citing works
Brinker, T. J., Hekler, A., Enk, A. H., Klode, J., Hauschild, A., Berking, C., Schilling, B., Haferkamp, S., Schadendorf, D., Fröhling, S., et al. (2019) · European Journal of Cancer · doi:10.1016/j.ejca.2019.02.005 · Cited by 304 · see citing works
Rajpara, S. M., Botello, A. P., Townend, J., & Ormerod, A. D. (2009) · British Journal of Dermatology · doi:10.1111/j.1365-2133.2009.09093.x · Cited by 186 · see citing works
Brinker, T. J., Hekler, A., Hauschild, A., Berking, C., Schilling, B., Enk, A. H., Haferkamp, S., Karoglan, A., von Kalle, C., Weichenthal, M., et al. (2019) · European Journal of Cancer · doi:10.1016/j.ejca.2018.12.016 · Cited by 157 · see citing works
Automated detection of nonmelanoma skin cancer using digital images: a systematic review
Marka, A., Carter, J. B., Toto, E., & Hassanpour, S. (2019) · BMC Medical Imaging · doi:10.1186/s12880-019-0307-7 · Cited by 90 · see citing works
Diagnostic accuracy of content‐based dermatoscopic image retrieval with deep classification features
Tschandl, P., Argenziano, G., Razmara, M., & Yap, J. (2018) · British Journal of Dermatology · doi:10.1111/bjd.17189 · Cited by 86 · see citing works
Patrick, M. T., Raja, K., Miller, K. L., Sotzen, J. R., Gudjonsson, J. E., Elder, J. T., & Tsoi, L. C. (2018) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2018.09.018 · Cited by 74 · see citing works
Can clinical decision making be enhanced by artificial intelligence?
Janda, M., & Soyer, H. P. (2019) · British Journal of Dermatology · doi:10.1111/bjd.17110 · Cited by 36 · see citing works
Circulating biomarkers predictive of tumor response to cancer immunotherapy
Lee, E. Y., & Kulkarni, R. P. (2019) · Expert Review of Molecular Diagnostics · doi:10.1080/14737159.2019.1659728 · Cited by 32 · see citing works
Self-citationTaroni, J., Martyanov, V., Mahoney, J. M., & Whitfield, M. L. (2016) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2016.12.007 · Cited by 28 · see citing works
Artificial intelligence in dermatology: are we there yet?
Lim, B. C. W., & Flaherty, G. T. (2019) · British Journal of Dermatology · doi:10.1111/bjd.17899 · Cited by 23 · see citing works
Embracing machine learning and digital health technology for precision dermatology
Wongvibulsin, S., Ho, B. K., & Kwatra, S. G. (2019) · Journal of Dermatological Treatment · doi:10.1080/09546634.2019.1623373 · Cited by 9 · see citing works
Advancing Health and Health Care Using Machine Learning
Rivara, F. P., Fihn, S. D., & Perlis, R. H. (2018) · JAMA Network Open · doi:10.1001/jamanetworkopen.2018.7176 · Cited by 5 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Chen, S., Yu, J., Chamouni, S., Wang, Y., & Li, Y. (2024) · BMC Medicine · doi:10.1186/s12916-024-03566-x · Cited by 73 · see citing works
“Unlike certain health specialties such as dermatology or ophthalmology, where ML and AI have been successfully adopted due to their reliance on visual evaluation and pattern recognition [11], the application of ML and AI in epidemiology presents unique challenges.”
BackgroundWongvibulsin, S., Frech, T. M., Chren, M.-M., & Tkaczyk, E. R. (2022) · JID Innovations · doi:10.1016/j.xjidi.2022.100105 · Cited by 29 · see citing works
- Authors
- Lee, E. Y., Maloney, N. J., Cheng, K., & Bach, D. Q.
- Affiliations
- California Institute of Technology (US)
- University of California, Los Angeles (US)
- Corresponding author
- Lee, Ernest Y.
- Venue
- Journal of the American Academy of Dermatology
- Publisher
- Elsevier BV
- Published
- 2021-05
- Volume
- 84
- Issue
- 5
- Pages
- 1458-1459
- ISSN
- 0190-9622 (ISSN-L)
- PMC
- PMC8023050
- OpenAlex
- W3038519274
- Cited by
- 14
- References
- 16
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 2 · 2023: 3 · 2022: 3 · 2021: 1
- FWCI
- 0.63
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of the American Academy of Dermatology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- Funders
- National Institutes of Health
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- machine learning, artificial intelligence, dermatological diagnosis, dermatological treatment, electronic health records, multi-omics, non-invasive imaging, clinical translation
- MeSH terms
- Machine Learning, Big Data, Dermatology, Humans, Image Processing, Computer-Assisted, Photography, Skin, Skin Diseases, Gene Expression Profiling, Genomics, Precision Medicine
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
