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Seeing the Gaps: Heatmaps, Metrics, and the Hidden Failures of Explainable AI in Dentomaxillofacial Radiology - A Systematic Review

Truvace record
TRV-2026-2447raw data (JSON)
Version
v1Published
First published
Evidence type
Systematic review or meta-analysis
Sources
1 sourcePeer-reviewed
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This systematic review aims to evaluate the techniques and reported patterns of heatmap-based visual explanations in deep learning (DL) systems for dental radiographic classification tasks. A literature search following PRISMA guidelines was conducted in PubMed, Scopus, IEEE Xplore Library, Oria.no and Google Scholar to retrieve studies using DL for dental classification tasks. Only articles with a visual explanation (heatmap) were selected. Risk of bias assessment was done with QUADAS-2, PROBAST-AI and CEBM. Th…

The problem

A systematic review of 136 dental radiograph AI studies found formal faithfulness testing of heatmap explanations was rarely done, so the heatmaps cannot be assumed to show how the model actually reasons.

What this doesn’t fix

The review evaluated studies that were not primarily designed to assess visual explanations, which limits how directly their heatmap findings can be judged.

Sources

1 on file
  1. Dentomaxillofacial Radiologydoi:10.1093/dmfr/twag074
    Peer-reviewed2026-11-07

Cite this record

v1

Truvace Impact Record TRV-2026-2447, v1: “Seeing the Gaps: Heatmaps, Metrics, and the Hidden Failures of Explainable AI in Dentomaxillofacial Radiology - A Systematic Review.” Truvace, 2026-11-10. /record/TRV-2026-2447 (accessed on the day you copy it). sha256 34764f89d5c2c92f…

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