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 Table of Contents  
EDITORIAL
Year : 2019  |  Volume : 31  |  Issue : 4  |  Page : 285

Artificial intelligence in oral medicine and radiology


Department of Oral Medicine and Radiology, Government College of Dentistry, Indore, Madhya Pradesh, India

Date of Submission13-Jan-2020
Date of Acceptance13-Jan-2020
Date of Web Publication03-Mar-2020

Correspondence Address:
Dr. Ajay Pratap Singh Parihar
Department of Oral Medicine and Radiology, Government College of Dentistry, Indore, Madhya Pradesh
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jiaomr.jiaomr_7_20

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How to cite this article:
Singh Parihar AP. Artificial intelligence in oral medicine and radiology. J Indian Acad Oral Med Radiol 2019;31:285

How to cite this URL:
Singh Parihar AP. Artificial intelligence in oral medicine and radiology. J Indian Acad Oral Med Radiol [serial online] 2019 [cited 2020 Jun 1];31:285. Available from: http://www.jiaomr.in/text.asp?2019/31/4/285/279865





“Intelligence is the ability to adapt to change.” – Stephen Hawking

Artificial intelligence (AI) is intelligence shown by machines. This term was coined by John MaCarthy in 1956 at Massachusetts Institute of Technology. AI is a branch of computer science dedicated to the development of computer algorithms to accomplish tasks traditionally associated with human intelligence, such as the ability to learn and solve problems. This includes machine learning (ML), representation learning, and deep learning.

Most popular AI analytic tool are used for image analysis inspired by the biological nervous system. This involves a network of highly interconnected computer processors that has the ability to learn from past examples, handle imprecise information, and generalize enabling application of the model to independent data has making it a very attractive analytical tool in the field of medicine. The various techniques of AI which are being applied in dentistry include artificial neural networks (ANN), genetic algorithms, and fuzzy logic.

ANNs have been used to interpret plain radiographs, ultrasound, computed tomography, magnetic resonance imaging (MRI), Cone Beam CT and radioisotope scans. ANN allows a computer to correctly generalize a setting by tuning of parameters within the algorithm to optimize the goodness of fit between the input (i.e. text, image, or video data fed into the algorithm) and output (i.e. classification). For example, an ML algorithm can detect a dentoalveolar pathology provided it is done by a trained radiologist by analysing thousands of such images which are labelled as normal or abnormal. ML algorithms are trained to give a specific answer by evaluating or learning a large number of exams that have been hand-labelled.

Fuzzy logic is a subtype of ML in which the computer algorithm learns the features required to classify the provided data. This does not require a hand-labelled data like ML.

AI can be used as a useful modality in diagnosis and treatment of lesions of oral cavity and can be employed in screening and classifying suspicious altered mucosa undergoing premalignant and malignant changes. Genetic predisposition for oral cancer for a large population can be detected by AI in near future. Studies have suggested that ANNs may act as an adjuvant diagnostic tool for dentist. Radiologists are primarily known for their image interpretation skills. Advanced breakthroughs in image recognition using AI systems have shifted from science fable into reality in the radiology practice in the last two decades. In head and neck imaging modalities AI provides advantage owing to its distinctive ability to learn and can be assimilated with other imaging modalities such as CBCT, MRI to determine deviations from normality that could have gone unrecognized with human eye. Illustrations include definite location of landmarks on radiographs aids in location of minor apical foramen, detection of vertical root fractures, cephalometric analysis, thereby strengthening the accuracy of working length determination;. However understanding a case requires multiple basic medical and clinical specialities to provide plausible explanations for imaging findings. Also, advanced imaging modalities necessitate specialized intelligence for detection of anomalies, segmentation, and image classification.

As said, there are two sides of a coin and only a balancing act that will put both the good with the risks associated will help in brightening a future that is technologically enabled and secured for the mankind.

The ability to invent intelligent machines has fascinated humans since the ancient times. Researchers are creating systems and programs that could mimic human thoughts and try doing things that human could do. The artificial Intelligence is a combination of computer science, physiology, and philosophy. Above all we have to always remember what Albert Einstein said that “The true sign of intelligence is not knowledge but imagination.” -






 

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