What is artificial intelligence (AI)?
Disruptive technology is defined as an innovation that drastically disrupts the way consumers think and businesses operate. Disruptive technologies include automobiles, telephones, computers, and the internet. Artificial Intelligence (AI) is rapidly progressing from a future technology to a disruptive technology.
In our daily lives, we are surrounded with AI. Artificial intelligence (AI) has been ingrained in our daily lives thanks to our smartphones. AI algorithms are used by social media to improve our online experiences and recommend personalised content and adverts. Similarly, AI algorithms are used in various technologies such as smart assistants (Siri, Alexa), self-driving cars, phone cameras, and spam filters.
Machine learning (ML) and Deep learning (DL)
AI is an umbrella phrase that refers to a computer algorithm that can simulate human intelligence by performing cognitive functions such as learning and problem-solving. Machine Learning (ML) and Deep Learning (DL) are two further subcategories of AI that are frequently interchangeably utilised, which is incorrect. As a result, a greater understanding of their differences is required. Simply described, machine learning (ML) is a set of specialised statistical algorithms that learn patterns from given data and generate predictions based on unknown data. DL is a more advanced variant of ML that is based on the human brain (Fig. 1).
Artificial intelligence (AI) is a broad phrase that refers to a computer algorithm that can execute cognitive processes in the same way that humans do.
DL is built on an elementary unit known as artificial neurons (perceptron), which are layered in three layers (input, hidden, and output) to form an Artificial Neural Network (ANN). The phrase 'Deep Learning' refers to an ANN with multiple hidden layers, as well as an input and output layer (Fig. 2).
Recent AI research has shifted its focus from machine learning to deep learning, owing to DL's numerous advantages. DL algorithms can learn features and recognise patterns on their own, whereas ML algorithms require manual input from subject experts, which is an expensive and time-consuming process. In addition, if an ML algorithm produces an incorrect forecast, a topic expert must make changes to enhance it. The algorithm in DL can measure its accuracy and, as a result, change and improve itself at the next iteration. DL can attain extremely high accuracy levels as a result of these characteristics, making it more helpful in real-life applications.
Computer vision (the capacity to recognise and identify images), natural language processing (the ability to understand and contextualise words), and audio signal processing are all using deep learning (speech recognition). Computer vision, which uses Convolution Neural Network (CNN) as a special form of DL approach for image processing, has seen the most significant uptake of AI in dentistry.
"While the algorithm does a good job of recognising teeth, it is hampered by artefacts, disease, impacted teeth, and various types of dental implants."
A CNN is a more advanced artificial neural algorithm (ANN) that includes a specialised layer known as the convolutional layer. This convolutional layer works like a sliding window, looking for certain elements in an image such as borders, forms, edges, and colours in order to compress complex images into a feature map for faster processing.
In dentistry, AI has clinical applications.
Despite its acceptance in other areas, artificial intelligence (AI) has yet to make its way into medical and dental practise. Artificial intelligence (AI) research in dentistry is relatively new.
A few instances are shown below.
Charting software that is fully automated
To automate dental charting on radiographs, several machine learning and deep learning algorithms are being tested. The most common radiograph used for dental charting is the orthopantomogram (OPG). However, researchers have discovered that it is a difficult task. This is due to the OPG's overlapping anatomy (zygoma, sinuses), which causes the algorithm to become confused. While the algorithm does a good job of recognising teeth, it is hampered by artefacts, disease, impacted teeth, and various types of dental implants.
"It should be noted that dentists do not entirely rely on dental radiographs for caries detection, therefore not integrating patient history and clinical examination is a restriction of automated caries detection."
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