Artificial Intelligence

What are the main use cases for drones in 2030, 2040? Who are the major players in the drone market? What are the key drivers for market growth? What is the projected revenue for the drones market from 2020 onwards?

AI in Medical Diagnostics 2020-2030 - this IDTechEx report benchmarks 60+ companies for image recognition AI performance, market readiness, value proposition, technical maturity and more. It includes granular forecasts for 12 clinical applications, insights into addressable market size and penetration until 2030.

Thursday 19 November 2020 - AI in Medical Diagnostics - An Impending Revolution? This webinar includes a Q&A session. Please submit any questions to annick@IDTechExWebinars.com by Sunday 15th November.

This article discusses key technological issues that AI image diagnostics companies face and provides a roadmap describing when and how they are likely to be overcome over the next decade, based on research conducted by IDTechEx in the report "AI in Medical Diagnostics 2020-2030: Image Recognition, Players, Clinical Applications, Forecasts".

This IDTechEx article assesses the technological status of image recognition AI in medical diagnostics today, detailing its current capabilities and value proposition. The article also identifies key technological issues currently limiting the uptake of image recognition AI and discusses opportunities for improvement.

The new IDTechEx report, "Sensors 2021-2041" uniquely analyses the whole subject in the light of such seismic changes as the COVID-19 virus. The sensor business is in turmoil. The industries that spend most on sensors are changing as are the sensors.

In the past two decades, the car market has declined twice: first due to the 2008 economic crisis, and then due to falling sales in China. Most recently, the lockdowns implemented to combat the coronavirus pandemic, causing auto-production plants to close globally and a loss of consumer spending will lead to an unprecedented 23% decline in 2020 (IDTechEx prediction).

It is often imagined that agriculture is alien to new technologies. This is far from reality. In fact, agriculture has always been a pioneer in the adoption of new technologies, embracing motor/power, modified seed, and agrochemical technologies over many decades to boost productivity.

Nearly every sector is proposing the use of artificial intelligence. Materials science R&D is relatively late to this trend, and there are many industry-specific hurdles, but the opportunities are beginning to be realized and the potential impact is significant.

Deep learning has revolutionized image recognition and analysis, making unprecedented performance leaps between 2010-2014. These rapid advancements enabled the development of automated, accurate, accessible, and cost-effective medical diagnostics.