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To plan a dental implant operation and the implant size and position, dentists need to know the exact location of the mandibular canal, a canal located in both sides of the lower jaw that contains the alveolar nerve. Typically, dentists and radiologists define the location of the mandibular canals manually from the X-ray or CT scans, which makes the task laborious and time-consuming. To bring a solution to this problem, researchers at the Finnish Center for Artificial Intelligence FCAI, Tampere University Hospital, Planmeca and the Alan Turing Institute developed a new model that accurately and automatically shows the exact location of mandibular canals. The model is based on training and using deep neural networks.
Based on the research results, this type of a deep learning model can localise the mandibular canals highly accurately. It surpasses the statistical shape models, which have thus far been the best, automatized method to localise the mandibular canals. In simple cases, the model is as accurate as a human specialist. 'In more complex cases, one may need to adjust the estimate, so we are not yet talking about a fully stand-alone system,' says Joel Jaskari, Doctoral Candidate and the first author of the research paper.
Using Artificial Intelligence has another clear advantage, namely the fact that the machine performs the job equally fast and accurately every time. 'The aim of this research work is not, however, to replace radiologists but to make their job faster and more efficient so that they will have time to focus on the most complex cases,' adds Professor Kimmo Kaski.
Outline the main issues raised.
(Reference: Adapted from: https://www.sciencedaily.com/releases/2020/04/200421094254.htm)
Example Candidate Response
A new AI solution uses a deep learning model to localise the mandibular canal highly accurately, seemingly providing the most effective method developed thus far.
However, it works well only in simple cases, and struggles with more complex cases.
One must question whether it is ethically acceptable for an AI to take over a diagnostic role from humans.
We might fear that human jobs will be lost, although we are reassured that this is not the case by the article.
We should certainly consider who would be responsible for setting the standards of complexity in which the AI is to work - seemingly corners could be cut by letting the AI take on more complex jobs that it is less able to do. We must trust that radiologists develop appropriate triaging systems to deploy this kind of technology in a way that lets patients benefit from their time to the greatest extent.