In the life sciences industry, quality assurance is more than a project necessity—it’s vital for regulatory compliance, solution reliability, and, most importantly, patient safety. With over two decades of experience in IT services for life science companies, we at Quantori understand the invaluable importance of quality assurance (QA) and the specific challenges of our domain. When it comes to systems and tools for life sciences, pharma, and medicine, it’s not just about having a skillful and attentive QA team—it’s about having QA engineers with relevant expertise and backgrounds and involving them at every stage of a project. In this blog post, we will explore how having a QA team with a strong life sciences background—especially PhDs in biology, biotechnology, or chemistry—contributes to the success, clarity, and scientific proof of IT projects at every stage.

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.

How can users access AlphaFold-like models and seamlessly predict protein structures without the necessity of advanced coding skills? This article explores the Quantori solution that makes this possible.

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.

No matter how hard you try to progress your research, without enough data, you’ll probably get stuck at some point. This is particularly crucial in the healthcare sector. To extract therapeutic insights from medical records, researchers typically need to work with many hundreds to thousands of patient cases, gathering and processing data from various sources. When the initial evidence at their disposal is limited, the conclusions may become distorted and biased.