This blog is the next in a series of 5, to discuss the results of the Ethical AI workshop at MPNE Consensus Data 2024.

Artificial intelligence in medical applications is always faced with a series of difficulties. The challenges range from the quality of input data and model performance to ethical problems.

Foundation models have taken the world of AI by storm. These pre-trained powerhouses have revolutionized natural language processing, computer vision, and speech processing, remarkable advancements in various domains.

Generative AI, a rapidly evolving subset of artificial intelligence, transforms how we create and interact with digital content.

Have a quick look at the iToBoS scanner prototype at the Bosch Manufacturing Solutions facilities.

The demand for mobile and multitask devices has shifted the focus of research towards embedded systems and microcomputers.

Understanding the geometry of an existing scene and being able to use this knowledge to produce (and refine) data, is an important task in any research field, particularly in the medical domain where the study and understanding of 3D structures of interest play a crucial role in abnormality detection.

The iToBoS project is a demonstration of collaborative efforts, where multiple partners combine their specialized knowledge towards a common objective.

Precision and diversity are crucial in melanoma research, and isahit's approach to data annotation reflects this necessity.

The need to analyze personal data to drive business, alongside the requirement to preserve the privacy of data subjects, creates a known tension.