The Validity of Machine Learning Procedures in Orthodontics: What Is Still Missing?

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.

Complexity and data mining in dental research: A network medicine perspective on interceptive orthodontics.

Procedures and models of computerized data analysis are becoming researchers' and practitioners' thinking partners by transforming the reasoning underlying biomedicine. Complexity theory, Network analysis and Artificial Intelligence are already approaching this discipline, intending to provide support for patient's diagnosis, prognosis and treatments. At the same time, due to the sparsity, noisiness and time-dependency of medical data, such procedures are raising many unprecedented problems related to the mismatch between the human mind's reasoning and the outputs of computational models.

Prognostic approach to Class III malocclusion through case-based reasoning.

Objective: This investigation evaluates the evidence of case-based reasoning (CBR) in providing additional information on the prediction of future Class III craniofacial growth.

Settings And Sample Population: The craniofacial characteristics of 104 untreated Class III subjects (7-17 years of age), monitored with two lateral cephalograms obtained during the growth process, were evaluated.

Materials And Methods: Data were compared with the skeletal characteristics of subjects who showed a high degree of skeletal imbalance ('prototypes') obtained from a large data set of 1263 Class III cross-sectional subjects (7-17 years of age).

Computer-aided heuristics in orthodontics.

Introduction: During the decision-making process, physicians rely on heuristics that consist of simple, useful procedures for solving problems, intuitive shortcuts that produce reliable decisions based on limited information. In clinical situations characterized by a high degree of uncertainty such as those encountered in orthodontics, cognitive biases and judgment errors related to heuristics are not uncommon. This study aimed at promoting trust in the effective interface between the intuitive reasoning of the orthodontic practitioner and the computational heuristics emerging from simple statistical models.