Diagnostic Accuracy and Pitfalls of Publicly Available Artificial Intelligence Models for Nail Disorders

Tanya Boghosian; Haroutyun Joulfayan; Nazar Boghosian; Jacob Beer; Aaron J. Russell

doi:10.36849/jdd.9734

March 1, 2026Journal of Drugs in Dermatology

Diagnostic Accuracy and Pitfalls of Publicly Available Artificial Intelligence Models for Nail Disorders

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Key Points

This research aims to evaluate the diagnostic accuracy of publicly available AI models for identifying nail disorders.
Evaluated publicly available AI models
Compared AI diagnostic accuracy to clinical assessments
Investigated the implications of accuracy on patient outcomes
AI models support pattern recognition but have diagnostic limitations
Clinical judgment is critical for ensuring accurate diagnoses
Inaccuracies can lead to significant impacts on patient care

Implication

Observational analysis assesses AI model accuracy for diagnosing nail disorders, suggesting clinical judgment is vital.

Abstract

These findings support the potential role of VLMs as adjunctive tools for pattern recognition but reaffirm that clinical judgment remains essential, especially in cases where diagnostic accuracy has critical implications for patient outcomes. .

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Cite This Study

Boghosian et al. (2026) studied this question.

synapsesocial.com/papers/69d34dd49c07852e0af97695 https://doi.org/10.36849/jdd.9734

View at Publisher

Ask AI

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Diagnostic Accuracy and Pitfalls of Publicly Available Artificial Intelligence Models for Nail Disorders

Key Points

Implication

Abstract

Discussion

Cite This Study

Diagnostic Accuracy and Pitfalls of Publicly Available Artificial Intelligence Models for Nail Disorders

Key Points

Implication

Abstract

Discussion

Cite This Study

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