aceApplication of deep learning and explainable AI-supported medical decision-making for facial phenotyping in genetic syndromes
Objective To conduct a pilot study to assess whether saliency-based explainable artificial intelligence (XAI) affects the recognition of genetic conditions from facial images. Methods Forty-four medical geneticists, divided into AI-only and XAI-supported groups, assessed 18 images of individuals with or without genetic conditions. Diagnostic accuracy and confidence were recorded before and after viewing an AI classifier's prediction probability, with or without XAI explanations. Mediation analyses were conducted…
When the AI classifier was correct, showing its prediction probability raised geneticists' diagnostic accuracy and confidence in recognizing genetic conditions from facial images.
When the AI classifier was incorrect, geneticists' accuracy and confidence fell, and saliency-based explanations did not improve decisions, so the explanations were not integrated.
The work is described as a pilot study with 44 geneticists and 18 images, and the authors state the data may only help inform larger studies.
Evidence
- Peer-reviewedClinical Dysmorphology2026-10-08
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Truvace Impact Record TRV-2026-1607, v1: “Application of deep learning and explainable AI-supported medical decision-making for facial phenotyping in genetic syndromes.” Truvace, 2026-10-16. /record/TRV-2026-1607 (accessed at citation time). sha256 fd9089560e2160bc…
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