Traditionally, drug discovery relied heavily on trial and error, with long timelines and high costs. The introduction of ...

Drug discovery has long been criticized for its slow, costly, and failure-prone nature. Traditional approaches, particularly ...

In April 2025, the US Food and Drug Administration (FDA) published a roadmap for leveraging new approach methodologies (NAMs), including in silico approaches such as artificial intelligence (AI), to ...

How to maintain quality control over pre-specified activities in AI/ML for drug development? Regarding data quality, reliability, and representativeness issues, the discussion paper spotlights how ...

A major challenge in using AI for drug development is the need for high-quality data. AI models require consistent, comprehensive and well-annotated data to make accurate predictions.

Unlike other fields that have large, high-quality datasets available to train AI models, such as image analysis and language processing, the AI in drug development is constrained by small, low ...

AI systems trained on such data can streamline and optimize the drug development process, including drug discovery, diagnosing diseases, identifying treatments and risks, designing clinical trials ...

Most work using AI in drug development intends to reduce the time and money it takes to bring one drug to market – currently 10 to 15 years and US$1 billion to $2 billion.

Automated quality control leverages AI that has been trained and tested on thousands of samples to identify commonly-occurring quality issues in every image of H&E stained slides.

Multimodal data AI processes diverse data sources, revealing insights that can enhance drug candidate development and healthcare delivery. How does Tevogen Bio ensure data quality for AI models?

Today, Snowflake announced an investment in Metaplane, a Boston-based startup helping enterprises identify and rectify data quality issues with an end-to-end AI-powered platform. While the amount ...