ArticleJournal of personalized medicine2022
The Validity of Machine Learning Procedures in Orthodontics: What Is Still Missing?
Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Development of Leading Enhancement Assistive Planning: A three-dimensional malocclusion classification system for orthodontic treatment with clear aligners.Korean journal of orthodontics · 2026Article
- Artificial intelligence in revolutionizing orthodontic practice.World journal of methodology · 2025Review
- Challenges and opportunities for digital twins in precision medicine from a complex systems perspective.NPJ digital medicine · 2025Review
- Development and validation of predictive models for skeletal malocclusion classification using airway and cephalometric landmarks.BMC oral health · 2024Article
- Comparison of deep learning models to detect crossbites on 2D intraoral photographs.Head & face medicine · 2024Article
- Evaluation of deep learning and convolutional neural network algorithms accuracy for detecting and predicting anatomical landmarks on 2D lateral cephalometric images: A systematic review andThe Saudi dental journal · 2023Review
- Identification and verification of the temozolomide resistance feature gene DACH1 in gliomas.Frontiers in oncology · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) models and procedures hold remarkable predictive efficiency in the medical domain through their ability to discover hidden, non-obvious clinical patterns in data. However, due to the sparsity, noise, and time-dependency of medical data, AI procedures are raising unprecedented issues related to the mismatch between doctors' mentalreasoning and the statistical answers provided by algorithms. Electronic systems can reproduce or even amplify noise hidden in the data, especially when the diagnosis of the subjects in the training data set is inaccurate or incomplete. In this paper we describe the conditions that need to be met for AI instruments to be truly useful in the orthodontic domain. We report some examples of computational procedures that are capable of extracting orthodontic knowledge through ever deeper patient representation. To have confidence in these procedures, orthodontic practitioners should recognize the benefits, shortcomings, and unintended consequences of AI models, as algorithms that learn from human decisions likewise learn mistakes and biases.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.