IMPLEMENTING AI APPLICATIONS IN RADIOLOGY: HINDERING AND FACILITATING FACTORS OF CONVOLUTIONAL NEURAL NETWORKS (CNNS) AND VARIATIONAL AUTOENCODERS (VAES)

Abstract

Radiology is changing as a result of artificial intelligence (AI), which improves diagnostic
accuracy and efficiency. In particular, CNNs and VAEs (variational autoencoders) are making
a significant impact. In addition to helping radiologists by managing the increasing complexity
and volume of imaging data, CNNs are excellent at automating image processing and
recognizing abnormal states like tumors. VAEs are less prevalent, but they have a special
benefit: they may create artificial medical images for data augmentation and privacy protection,
which is important when there is a lack of data. The requirement for sizable annotated datasets,
model interpretability, and ethical issues including data privacy and bias in AI-driven diagnoses
all pose obstacles to the mainstream implementation of AI in radiology, despite its potential.
In order to overcome these obstacles, AI must be integrated into the current healthcare systems
while taking ethical and technical concerns into consideration. With ongoing developments
anticipated to improve its applicability in clinical operations and eventually improve patient
outcomes, artificial intelligence in radiology has a bright future.