Clinical Photography's Computational Blind Spot

The smartphone has become dermatology's primary clinical camera, yet the photo it saves is assembled by software rather than being a faithful unedited photograph: several frames are merged, grain is removed, and texture is altered before the image file exists, so no unedited original survives. This processing is hidden, inconsistent between shots, recorded only in undocumented vendor-specific form, and cannot be fully disabled in the camera app. Smartphone photographs still serve to locate a lesion and support a referral, but the processing pipeline is ill-suited and potentially misleading for serial monitoring, research, and training of artificial intelligence models. We propose a per-image record of capture and processing, practical measures clinicians can take now, and smartphone camera processing pipelines built for clinical fidelity rather than aesthetic ends.

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Publication Details

Journal
Australasian Journal of Dermatology
Published
2026-11-02
DOI
https://doi.org/10.1111/ajd.70225
Primary Topic
Digital Imaging in Medicine
Type
article
Field-Weighted Citation Impact
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article

Clinical Photography's Computational Blind Spot

Philip L. Tong, Harrison A. Edwards
Australasian Journal of Dermatology
Digital Imaging in Medicine
article

Clinical Photography's Computational Blind Spot

Philip L. Tong, Harrison A. Edwards
article en

Abstract

The smartphone has become dermatology's primary clinical camera, yet the photo it saves is assembled by software rather than being a faithful unedited photograph: several frames are merged, grain is removed, and texture is altered before the image file exists, so no unedited original survives. This processing is hidden, inconsistent between shots, recorded only in undocumented vendor-specific form, and cannot be fully disabled in the camera app. Smartphone photographs still serve to locate a lesion and support a referral, but the processing pipeline is ill-suited and potentially misleading for serial monitoring, research, and training of artificial intelligence models. We propose a per-image record of capture and processing, practical measures clinicians can take now, and smartphone camera processing pipelines built for clinical fidelity rather than aesthetic ends.

Australasian Journal of Dermatology
Garvan Institute of Medical Research (AU), Royal Brisbane and Women's Hospital (AU)
Openalex Percentile: Top 12%
Digital Imaging in Medicine
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Clinical Photography's Computational Blind Spot — Philip L. Tong, Harrison A. Edwards · Australasian Journal of Dermatology (2026) | TGRS Research Map | TGRS