scholarly journals U.S. AI Workforce: Labor Market Dynamics

2021 ◽  
Author(s):  
Diana Gehlhaus ◽  
Ilya Rahkovsky

A lack of good data on the U.S. artificial intelligence workforce limits the potential effectiveness of policies meant to increase and cultivate this cadre of talent. In this issue brief, the authors bridge that information gap with new analysis on the state of the U.S. AI workforce, along with insight into the ongoing concern over AI talent shortages. Their findings suggest some segments of the AI workforce are more likely than others to be experiencing a supply-demand gap.

2020 ◽  
Vol 208 ◽  
pp. 03060
Author(s):  
Alena Vankevich ◽  
Iryna Kalinouskaya

Sustainable economic growth requires a system for forecasting the in-demand skills and competencies. The existing methods of analysis and forecasting of the labor market use truncated databases based on surveys of employers or registered vacancies on the state portal, which do provide reliable forecasts of the required competencies for the education system to ensure their timely formation. It is also impossible to analyze the need in terms of competencies, and not the number of employees. Therefore, a more reliable source of data is the analysis of vacancies and resumes collected by scraping from online job portals, which allows you to analyze vacancies and resumes in the context of the described competencies, and develop a forecast of their dynamics. The article presents an algorithm for using artificial intelligence in the analysis and forecasting of skills and competencies in demand, the advantages of which lie not only in the volume and speed of the processed information, but also in ensuring the quality and comparability of data.


2020 ◽  
Author(s):  
Bertalan Mesko ◽  
Stan Benjamens ◽  
Pranavsingh Dhunnoo

BACKGROUND At the beginning of the artificial intelligence (A.I.) era, the expectations are high, and experts foresee that A.I. shows potential for diagnosing, managing and treating a wide variety of medical conditions. However, the obstacles for implementation of A.I. in daily clinical practice are numerous, especially regarding the regulation of these technologies. OBJECTIVE Therefore, we provide an insight into the currently available A.I.-based medical devices and algorithms that have been approved by the U.S. Food & Drugs Administration (FDA). We aimed to raise awareness about the importance of regulatory bodies, clearly stating whether a medical device is A.I.-based or not. METHODS Cross-checking and validating all approvals, we identified 64 A.I.-based, FDA approved medical devices and algorithms. Out of those, only 29 (45%) mentioned any A.I.-related expressions in the official FDA announcement. RESULTS The majority (85.9%) was approved by the FDA with a 510(k) clearance, while 8 (12.5%) received de novo pathway clearance and one (1.6%) premarket approval (PMA) clearance. Most of these technologies, notably 30 (46.9%), 16 (25.0%) and 10 (15.6%) were developed for the fields of Radiology, Cardiology and Internal Medicine / General Practice respectively. CONCLUSIONS We launched the first comprehensive and open access database of strictly A.I.-based medical technologies that have been approved by the FDA. The database will be constantly updated. 


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