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To fuel artificial intelligence (AI) potential in clinical practice in otolaryngology, researchers must understand its epistemic limitations, which are tightly linked to ethical dilemmas requiring careful consideration. AI tools are fundamentally opaque systems, though there are methods to increase explainability and transparency. Reproducibility and replicability limitations can be overcomed by sharing computing code, raw data, and data processing methodology. The risk of bias can be mitigated via algorithmic auditing, careful consideration of the training data, and advocating for a diverse AI workforce to promote algorithmic pluralism, reflecting our population's diverse values and preferences., Competing Interests: Disclosure A. Rameau owns equity of Perceptron Health Inc. A. Rameau is a medical advisor for Sound Health Systems, Inc., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Objective: To investigate the accuracy of artificial intelligence-assisted growth prediction using a convolutional neural network (CNN) algorithm and longitudinal lateral cephalograms (Lat-cephs)., Materials and Methods: A total of 198 Japanese preadolescent children, who had skeletal Class I malocclusion and whose Lat-cephs were available at age 8 years (T0) and 10 years (T1), were allocated into the training, validation, and test phases (n = 161, n = 17, n = 20). Orthodontists and the CNN model identified 28 hard-tissue landmarks (HTL) and 19 soft-tissue landmarks (STL). The mean prediction error values were defined as 'excellent,' 'very good,' 'good,' 'acceptable,' and 'unsatisfactory' (criteria: 0.5 mm, 1.0 mm, 1.5 mm, and 2.0 mm, respectively). The degree of accurate prediction percentage (APP) was defined as 'very high,' 'high,' 'medium,' and 'low' (criteria: 90%, 70%, and 50%, respectively) according to the percentage of subjects that showed the error range within 1.5 mm., Results: All HTLs showed acceptable-to-excellent mean PE values, while the STLs Pog', Gn', and Me' showed unsatisfactory values, and the rest showed good-to-acceptable values. Regarding the degree of APP, HTLs Ba, ramus posterior, Pm, Pog, B-point, Me, and mandibular first molar root apex exhibited low APPs. The STLs labrale superius, lower embrasure, lower lip, point of lower profile, B', Pog,' Gn' and Me' also exhibited low APPs. The remainder of HTLs and STLs showed medium-to-very high APPs., Conclusion: Despite the possibility of using the CNN model to predict growth, further studies are needed to improve the prediction accuracy in HTLs and STLs of the chin area., (© 2024 The Authors. Orthodontics & Craniofacial Research published by John Wiley & Sons Ltd.)

A novel computational approach is proposed to investigate the shear modulus of graphene nanostructures. In this approach, the factors that affect the shear modulus of graphene structures are analysed using an integrated artificial intelligence (AI) cluster comprising molecular dynamics (MD) and gene expression programming. The MD-based-AI approach has the ability to formulate the explicit relationship of shear modulus graphene nanostructure with respect to aspect ratio, temperature, number of atomic planes and vacancy defects. In addition, the shear modulus of graphene predicted using an integrated MD-based-AI model is in good agreement with that of experimental results obtained from the literature. The sensitivity and parametric analysis were further conducted to find out specific influence and variation of each of the input system parameters on the shear modulus of two graphene structures. It was found that the number of defects has the most dominating influence on the shear modulus of graphene nanostructure. [ABSTRACT FROM PUBLISHER]

Computational modeling tools such as molecular dynamics (MD), ab initio, finite element modeling or continuum mechanics models have been extensively applied to study the properties of carbon nanotubes (CNTs) based on given input variables such as temperature, geometry and defects. Artificial intelligence techniques can be used to further complement the application of numerical methods in characterizing the properties of CNTs. In this paper, we have introduced the application of multi-gene genetic programming (MGGP) and support vector regression to formulate the mathematical relationship between the compressive strength of CNTs and input variables such as temperature and diameter. The predictions of compressive strength of CNTs made by these models are compared to those generated using MD simulations. The results indicate that MGGP method can be deployed as a powerful method for predicting the compressive strength of the carbon nanotubes. [ABSTRACT FROM AUTHOR]

An evolutionary approach of multi-gene genetic programming (GP) is used to study the effects of aspect ratio, temperature, number of atomic planes and vacancy defects on the engineering moduli viz. tensile and shear modulus of single layer graphene sheet. MD simulation based on REBO potential is used to obtain the engineering moduli. This data is then fed into the paradigm of a GP cluster comprising of genetic programming, which was specifically designed to formulate the explicit relationship of engineering moduli of graphene sheets loaded in armchair and zigzag directions with respect to aspect ratio, temperature, number of atomic planes and vacancy defects. We find that our MGGP model is able to model the engineering moduli of armchair and zigzag oriented graphene sheets well in agreement with that of experimental results. We also conducted sensitivity and parametric analysis to find out specific influence and variation of each of the input system parameters on the engineering moduli of armchair and zigzag graphene sheets. It was found that the number of defects has the most dominating influence on the engineering moduli of graphene sheets. [ABSTRACT FROM AUTHOR]

A molecular dynamics (MD)-based-artificial intelligence (AI) simulation approach is proposed to investigate thermal transport of carbon nanotubes (CNTs). In this approach, the effect of size, chirality and vacancy defects on the thermal conductivity of CNTs is first analyzed using MD simulation. The data obtained using the MD simulation is then fed into the paradigm of an AI cluster comprising multi-gene genetic programming, which was specifically designed to formulate the explicit relationship of thermal transport of CNT with respect to system size, chirality and vacancy defect concentration. Performance of the proposed model is evaluated against the actual results. We find that our proposed MD-based-AI model is able to model the phenomenon of thermal conductivity of CNTs very well, which can be then used to complement the analytical solution developed by MD simulation. Based on sensitivity and parametric analysis, it was found that length has most dominating influence on thermal conductivity of CNTs. [ABSTRACT FROM AUTHOR]

Highlights: [•] Three artificial intelligence (AI) methods for modeling output voltage of microbial fuel cell (MFC) system is discussed. [•] AI models efficiently establish the relationship between output voltage and input factors of MFC. [•] Out of three methods, MGGP evolves a model with better generalization ability. [•] MGGP shows excellent potential to predict performance of MFC and can be used to gain better insights into MFC system. [Copyright &y& Elsevier]