Challenges and limitations of AI in pathology


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: The Benefits, Risks, and Applications


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: Health Care Solutions


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

Have a look at iToBoS scanner prototype


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

Edge Computing Platforms for Medical Applications


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

Use of Neural Radiance Fields in the Medical Domain


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.

Streamlining Operations: isahit's Management in iToBoS Project


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

How isahit's expertise and diversified workforce is enhancing Melanoma Research?


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

Privacy risk assessment of AI models


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

Generative AI for Specialized Dataset Enhancement and Expansion


An important challenge for applying machine and deep learning methods in applications where data collection is difficult, or costly is the reduced amount of annotated data.