Artificial intelligence (AI), Machine Learning (ML), Deep Learning (DL) – since the start of the Fourth Industrial Revolution, these buzzwords have been trending and sometimes falsely used as synonyms for each other [1, 2]. The technologies associated with AI are mainly responsible for advances in many fields like image and speech recognition. Since AI technologies can be applied to a wide range of fields including medical and education fields, many efforts have been actively given to identify the current technology trends and analyze development directions of it. [3, 4, 5]. Podcasting has emerged as an important information technology tool for health professionals and consumers around the world . An initial literature research indicated, that there is a lack of research using podcasts as a data set for trend analysis.
The goal of this thesis is to review the major research trend on the topics of AI, ML and DL using trend analysis on health-care podcasts. A representative amount of trending health-care podcasts will be screened and selected. All their podcasts will be downloaded and transcribed to text using a Speech-to-Text API. The resulting text data will be used for quantitative trend analysis and to research how the buzzwords around AI and its subsets have evolved over time.
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