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AI-Based Fine-Tuning: How Signia Assistant Improves Wearer Acceptance Rates

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1.  The type of artificial intelligence used in Signia Assistant is best described as which of the following?
  1. Deep neural network
  2. Convoluted neural network
  3. Deep neural encoders
  4. Shallow neuronal learning
2.  Signia Assistant is believed to save time during which phase of the patient journey?
  1. Initial fitting
  2. Fine-tuning follow-up appointments
  3. Counseling sessions with the provider
  4. Hearing aid orientation
3.  Over time, as the knowledge base of Signia Assistant increases as more data are accumulated and fed into the system, which of the following is likely to occur?
  1. The new data are used to retrain the DNN, leading to better proposed solutions
  2. The new data are ignored, and the wearer must see the HCP for an in-person appointment
  3. The original fitting is lost forever
  4. The new data retrains the DNN and the wearer never needs to make another in-person appointment with the HCP
4.  What is a general trend in the study outlined in this article?
  1. Acceptance rates tend to decline as more data is fed into the system
  2. Acceptance rates show no difference as more data is fed into the system
  3. Acceptance rates tend to increase as more data is fed into the system
  4. Results of the study are inconclusive
5.  What best describes Signia Assistant?
  1. Assists the HCP in setting proper gain and output during the fitting
  2. AI-based approach to first fit in the clinic
  3. Trainable compression
  4. AI-based approach to fine-tuning using a smartphone app

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