Aidence, ScreenPoint Medical and Thirona launch video series explaining medical imaging AI
Three leading AI scale-ups – Aidence, ScreenPoint Medical and Thirona – have launched an informative video series “Opening the black box of AI in medical imaging”. Their aim is to close knowledge gaps and increase trust in imaging AI by explaining how this emerging technology can be applied in radiology. The collaboration is unique in the industry.
Solving the “black box” problem
Artificial intelligence is referred to as a “black box” because its inputs and outputs are visible, but not the workings in-between. It is often the cause of public distrust and lack of acceptance.
A recent survey [1] of over 1,000 radiologists and radiology residents found that the more radiologists know about AI, the more likely they are to adopt it rather than fear replacement.
As front-runners in the MedTech industry, Aidence, ScreenPoint Medical and Thirona recognise their role in making AI comprehensible to healthcare professionals. They decided to bring their complementary insights together and answer frequent questions on AI in an innovative way.
One episode at a time
Each episode of “Opening the Black Box” covers one specific topic related to AI in medical imaging in approximately 10 minutes. The videos use a combination of animations and interviews with clinical, data science, regulatory, and industry experts.
The series starts with the basics: a short history of AI and how it differs from machine and deep learning. Mark-Jan Harte, Eva van Rikxoort and Nico Karssemeijer, founders of the three companies, explain what AI means to them.
Watch the first episode here: https://www.blackboxmed.ai/
Future episodes will address the benefits of AI for medical imaging and the development, validation, and certification of AI algorithms for automated image analysis. The series will also show real-world examples of AI deployments and applications in, for example, early breast and lung cancer detection.
Episodes will be made available here over the coming months: https://www.blackboxmed.ai/
Reference
1. https://pubmed.ncbi.nlm.nih.gov/33744991/
