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Sir, artificial intelligence (AI) has been used in a variety of ways in healthcare. These include detecting gastric cancer in endoscopic images, estimating the impact of human papillomavirus types in influencing the risk of cervical dysplasia recurrence, classifying skin cancer, identifying microbial volatile organic compound signatures, detection of fractures, and many more.
AI can serve as a useful modality in diagnosis and treatment of lesions of the oral cavity and can be employed in screening and classifying suspicious altered oral mucosa undergoing premalignant and malignant changes. The advantage would be no observation fatigue, and even minute changes at single pixel level can be detected which might go unnoticed by the naked eye. The analysis of omics data and individual medical profiles by AI might accurately predict a genetic predisposition for oral cancer for large populations. Further, personalised medicine, long-term treatment outcomes, recurrences and survival of oral cancer patients might be specifically calculated by AI algorithms. With respect to resection surgery, the intraoperative pathological diagnosis would be real-time and the margin accuracy might be comparable to or even better than that of frozen sections, as hypothesised by Zhang et al.1
Analogous to the findings of Palma et al.,2 AI might detect oral microbial volatile organic compound signatures which has potential applications in oral microbiology and periodontal medicine practice. AI when integrated with endodontics, might bio-mechanically prepare the root canals with precision. The analysis of digital slides by AI might result in more accurate detection of occult metastasis and comparative analysis of immunohistochemistry and other techniques. More importantly, subjective and observer bias might be eliminated as perceived in the diagnosis of epithelial dysplasia. Digital imaging methods integrated with AI might improve radio-diagnosis and reduce observer fatigue.
Incorporation of AI in teaching and learning process can dramatically improvise the way student perceive knowledge. AI can contribute from the designing of meaningfully differentiated curriculum to error free evaluation pattern (humans are sometimes biased). Future AI-based dental education can significantly reduce the cost of education and burden on educators.
References
Zhang J, Song Y, Xia F et al. Rapid and accurate intraoperative pathological diagnosis by artificial intelligence with deep learning technology. Med Hypotheses 2017; 107: 98–99.
Palma S I, Traguedo A P, Porteira A R, Frias M J, Gamboa H, Roque A C . Machine learning for the meta-analyses of microbial pathogens' volatile signatures. Sci Rep 2018; 8: 1–15.
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Majumdar, B., Sarode, S., Sarode, G. et al. Technology: Artificial intelligence. Br Dent J 224, 916 (2018). https://doi.org/10.1038/sj.bdj.2018.485
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DOI: https://doi.org/10.1038/sj.bdj.2018.485
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