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Visual heuristics and basal cell carcinoma: pitfalls and strategies for clinical vigilance

  • Helena M. Nammour , T. Austin Black , Mary E. Dyson , Maryam Niazi and Rashid M. Rashid EMAIL logo
Published/Copyright: January 7, 2026
Diagnosis
From the journal Diagnosis

Abstract

Objectives

To examine how visual heuristics contribute to the clinical misdiagnosis of basal cell carcinoma (BCC) and to identify common heuristic patterns that may lead to diagnostic error in dermatologic practice.

Methods

A review of published case reports and observational studies was conducted to identify instances in which BCC was misdiagnosed or mischaracterized prior to histopathologic confirmation. Articles were analyzed for recurring clinical features that influenced diagnostic reasoning. Heuristic patterns contributing to misdiagnosis were categorized thematically.

Results

Heuristics, or mental shortcuts used to streamline clinical decision-making, are widely used in dermatology due to their highly visual and pattern-based nature. While often efficient, they can introduce cognitive bias when lesions deviate from expected presentations. Analysis of misdiagnosed BCC cases revealed five key heuristic categories: pigmentation, lesion location, morphology or texture, patient age, and skin type. Misdiagnosis was most common in pigmented lesions, uncommon anatomical sites, and in patients with skin of color or younger age.

Conclusions

Visual heuristics play a central role in dermatologic diagnosis but may undermine accuracy when atypical features are present. Increased awareness of these diagnostic pitfalls, together with systematic dermoscopic evaluation and a low threshold for biopsy, can help reduce error and improve outcomes.


Corresponding author: Dr. Rashid M. Rashid, Mosaic Dermatology, 2211 Norfolk St #405, Houston, TX, 77098, USA, E-mail:

Acknowledgments

The authors would like to thank Dr. Rashid M. Rashid for his mentorship throughout this project and during preparation of this manuscript.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission. Author contributions: Writing, editing, literature Review, table production, formatting (HMN); writing, editing, figure Production (TAB); literature Review, editing (MED, MN).

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Received: 2025-08-10
Accepted: 2025-11-18
Published Online: 2026-01-07

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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