Development and validation of a peripheral blood smear assessment tool using cloud-based full-field digital microscopy
Peripheral blood smear review is a fundamental skill that aids in the timely diagnosis of morbid conditions in hematology. There is a lack of existing, evidence-based assessment tools to evaluate learners and guide curricular development both in peripheral blood smear review and analogous visuospatial skills across medical specialties. We collected validity evidence for a novel assessment tool for use in hematology education. We recruited attending hematologists, hematology fellows, and internal medicine residents to participate in the development of this assessment tool. The attending cohort participated in iterative revision of a digital microscopy–based assessment tool encompassing classical and malignant hematology. The validated tool then compared performance across attending, clinical fellow, and resident cohorts. Attending accuracy was poor with peripheral blood smear images alone and improved significantly with the addition of clinical context. Performance increased across training levels with attending and fellow performance significantly outpacing resident performance. Characteristics of morphologic descriptions varied significantly across cohorts. Digital microscopy image quality was considered to be equivalent to light microscopy with minor limitations. This tool offers hematology and pathology educators a validated method to assess learners and evaluate microscopy-based curricular developments. Further investment is required to broaden applications within hematology and pathology education.
Authors
- Jason A. Freed (ORCID: https://orcid.org/0000-0002-9123-8107)
- Matthew Lewis Chase (ORCID: https://orcid.org/0000-0002-6998-2212)
- Kevin Y. Yang (ORCID: https://orcid.org/0009-0005-5282-9683)
Institutions
- University of Hawaiʻi at Mānoa (US)
- Beth Israel Deaconess Medical Center (US)
- Kaiser Permanente (US)
- Hawaii Permanente Medical Group (US)
Publication Details
- Journal
- Academic Pathology
- Published
- 2026-09-25
- DOI
- https://doi.org/10.1016/j.acpath.2026.100296
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00