Reassessing the validity of AI cephalometric landmark identification: an analytical perspective on Ye et al. (2023)

To provide the public and non-specialist readers with clear insight, we re-evaluate a published study claiming that artificial intelligence (AI) software performs as accurately as orthodontist experts in identifying key points on dental X-ray images. Our analysis reveals flaws in the original research methods: inconsistent image processing led to unreliable measurements, and critical details about data sources were missing. Most importantly, when we repeated the experiment with new X-ray images and protocols, over 80% of the AI's landmark identifications showed significantly higher error rates than originally reported-contradicting the claim that AI matches expert accuracy. We urge methodological rigor in future studies on AI's role in dentistry.

Authors

Institutions

Publication Details

Journal
BMC Oral Health
Published
2026-08-03
DOI
https://doi.org/10.1186/s12903-026-08728-2
Primary Topic
Dental Radiography and Imaging
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Reassessing the validity of AI cephalometric landmark identification: an analytical perspective on Ye et al. (2023)

Fulin Jiang, Jiangwei Liao, Jinghong Yu
BMC Oral Health
Dental Radiography and Imaging
article

Reassessing the validity of AI cephalometric landmark identification: an analytical perspective on Ye et al. (2023)

Fulin Jiang, Jiangwei Liao, Jinghong Yu
article en

Abstract

To provide the public and non-specialist readers with clear insight, we re-evaluate a published study claiming that artificial intelligence (AI) software performs as accurately as orthodontist experts in identifying key points on dental X-ray images. Our analysis reveals flaws in the original research methods: inconsistent image processing led to unreliable measurements, and critical details about data sources were missing. Most importantly, when we repeated the experiment with new X-ray images and protocols, over 80% of the AI's landmark identifications showed significantly higher error rates than originally reported-contradicting the claim that AI matches expert accuracy. We urge methodological rigor in future studies on AI's role in dentistry.

BMC Oral HealthVol. 26(1)
Chongqing Three Gorges Central Hospital (CN), Chongqing Three Gorges University (CN), Air Force Medical University (CN)
Chongqing Municipal Education Commission
Openalex Percentile: Top 8%
Dental Radiography and Imaging
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.