AI-assisted Diagnosis of Nonmelanoma Skin Cancer in Resource-Limited Settings
Pathology FMs offer a promising pathway to improve early and precise NMSC diagnosis, especially in resource-limited environments. These tools could also facilitate patient stratification and recruitment for prospective clinical trials aimed at improving NMSC management.
Authors
- Renyu Zhang (ORCID: https://orcid.org/0000-0002-5285-2729)
- Habibul Ahsan (ORCID: https://orcid.org/0000-0001-9808-377X)
- Aly A. Khan (ORCID: https://orcid.org/0000-0003-3933-8538)
- Christopher R. Shea (ORCID: https://orcid.org/0000-0002-3780-8113)
- Maria Argos (ORCID: https://orcid.org/0000-0003-4234-252X)
- Hasan Shahriar (ORCID: https://orcid.org/0000-0002-1625-0229)
- Robert L. Grossman (ORCID: https://orcid.org/0000-0003-3741-5739)
- Spencer Ellis
- Derek Reiman (ORCID: https://orcid.org/0000-0002-7955-3980)
- Steven Song (ORCID: https://orcid.org/0000-0001-5868-3877)
- Xuan Hui (ORCID: https://orcid.org/0000-0001-6426-0723)
- Mohammed Kamal
Institutions
- Toyota Technological Institute at Chicago (US)
- Chicago Department of Public Health (US)
- Bangladesh Medical University (BD)
- University of Illinois Chicago (US)
- University of Chicago (US)
- Chan Zuckerberg Initiative (United States) (US)
Publication Details
- Journal
- Cancer Epidemiology Biomarkers & Prevention
- Published
- 2025-04-25
- DOI
- https://doi.org/10.1158/1055-9965.epi-25-0132
- Primary Topic
- Cutaneous Melanoma Detection and Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Chan Zuckerberg Initiative
- National Institutes of Health
- National Cancer Institute
- National Institute of General Medical Sciences
- National Institute of Allergy and Infectious Diseases
- National Institute of Environmental Health Sciences