The article "Applied Artificial Intelligence in Dentistry: Emerging Data Modalities and Modeling Approaches" by Balazs Feher, Camila Tussie, and William V. Giannobile provides a comprehensive overview of the application of artificial intelligence (AI) and machine learning (ML) in dental medicine. The authors highlight the increasing use of ML in oral health research and clinical practice, emphasizing its role in diagnostics, risk stratification, and decision support. The review covers the spectrum of available data modalities in dentistry, including image data, structured numerical data, and unstructured textual data, and discusses their compatibility with various AI methodologies. Key aspects of the article include: 1. **Artificial Intelligence in the Medical Context**: The authors explain the evolution of AI and ML in medicine, noting the importance of large datasets and advanced computing capabilities for deep learning. They categorize ML algorithms into supervised, unsupervised, and semi-supervised learning, and discuss their applications in regression and classification models. 2. **Data Modalities in Dental Medicine**: The article details the three major data modalities in dental medicine: image data (e.g., radiographs, photographs), structured numerical data (e.g., clinical parameters, billing records), and unstructured textual data (e.g., clinical notes). It highlights the unique advantages of each modality and their integration in comprehensive dental care. 3. **Diagnostic Modeling**: The focus is on computer vision, classification, and natural language processing (NLP) for diagnostic tasks. Computer vision is particularly relevant due to the large volume of radiographic data, while classification models are used for automated diagnostics based on structured numerical data. NLP is valuable for extracting information from clinical notes and other unstructured text. 4. **Prognostic Modeling**: Prognostic modeling uses ML to predict future events, such as disease onset or treatment outcomes. The article discusses the use of regression and classification models for this purpose, emphasizing the importance of longitudinal data and the challenges of model calibration. 5. **Generative Modeling**: Generative models create synthetic data, useful for research, educational purposes, and patient care. The article explores the potential of generative models in creating synthetic images and text, while also addressing the limitations, such as hallucinations and data privacy concerns. The authors conclude by discussing future considerations and directions, emphasizing the need for ethical, transparent, and equitable application of AI in dental medicine. They highlight the importance of diverse and high-quality datasets, regulatory compliance, and data privacy in ensuring the effective and safe use of AI in clinical practice.The article "Applied Artificial Intelligence in Dentistry: Emerging Data Modalities and Modeling Approaches" by Balazs Feher, Camila Tussie, and William V. Giannobile provides a comprehensive overview of the application of artificial intelligence (AI) and machine learning (ML) in dental medicine. The authors highlight the increasing use of ML in oral health research and clinical practice, emphasizing its role in diagnostics, risk stratification, and decision support. The review covers the spectrum of available data modalities in dentistry, including image data, structured numerical data, and unstructured textual data, and discusses their compatibility with various AI methodologies. Key aspects of the article include: 1. **Artificial Intelligence in the Medical Context**: The authors explain the evolution of AI and ML in medicine, noting the importance of large datasets and advanced computing capabilities for deep learning. They categorize ML algorithms into supervised, unsupervised, and semi-supervised learning, and discuss their applications in regression and classification models. 2. **Data Modalities in Dental Medicine**: The article details the three major data modalities in dental medicine: image data (e.g., radiographs, photographs), structured numerical data (e.g., clinical parameters, billing records), and unstructured textual data (e.g., clinical notes). It highlights the unique advantages of each modality and their integration in comprehensive dental care. 3. **Diagnostic Modeling**: The focus is on computer vision, classification, and natural language processing (NLP) for diagnostic tasks. Computer vision is particularly relevant due to the large volume of radiographic data, while classification models are used for automated diagnostics based on structured numerical data. NLP is valuable for extracting information from clinical notes and other unstructured text. 4. **Prognostic Modeling**: Prognostic modeling uses ML to predict future events, such as disease onset or treatment outcomes. The article discusses the use of regression and classification models for this purpose, emphasizing the importance of longitudinal data and the challenges of model calibration. 5. **Generative Modeling**: Generative models create synthetic data, useful for research, educational purposes, and patient care. The article explores the potential of generative models in creating synthetic images and text, while also addressing the limitations, such as hallucinations and data privacy concerns. The authors conclude by discussing future considerations and directions, emphasizing the need for ethical, transparent, and equitable application of AI in dental medicine. They highlight the importance of diverse and high-quality datasets, regulatory compliance, and data privacy in ensuring the effective and safe use of AI in clinical practice.