Medical imaging has long been a cornerstone of modern healthcare, offering a window into the human body that guides diagnosis and treatment. Among its many applications, histopathology — the examination of biological tissues to observe the appearance of diseased cells and tissues — plays a pivotal role in diagnosing various conditions, including cancer. However, traditional histopathological techniques are labor-intensive and subject to variability in interpretation. This is where artificial intelligence (AI) steps in, promising to transform histopathology by enhancing accuracy, efficiency, and consistency.

Despite recent progress in the development of Machine Learning models, their application in clinical practice remains limited. Several barriers to entry exist, with the lack of transparency in a model's decision-making process being critical. This article reviews a recent study where explainability was a core component of Machine Learning methodology applied to medical image analysis.

In the rapidly evolving landscape of drug discovery, the fusion of multimodal data with artificial intelligence (AI) and machine learning (ML) technologies is setting the stage for groundbreaking advancements. This synergy is not only expediting traditional processes but also paving the way for innovative approaches to complex, man-power intensive challenges. Here, we delve into four transformative use cases, illustrating the power of this integration in revolutionizing drug discovery.

By Alexander Knop, Quantori Principal Mathematician

By Omar Kantidze, Quantori Principal Scientist​

Language models, pre-trained on both natural languages and molecular languages (DNA and proteins), are transforming the Life Sciences.

Medical imaging plays a crucial role within both clinical practice and scientific research, assisting healthcare professionals with patient diagnosis and subsequent treatment monitoring, using minimally invasive techniques.