Articles

The journey of developing an AI diagnostic tool is long and intricate. Rigorous development, validation, and regulatory approval are required before clinical use. The final step, deployment, brings this innovative tool into the hands of pathologists, integrating it seamlessly into their workflows. This integration requires meticulous planning to ensure ease of use and reliability.

Artificial intelligence relies heavily on algorithms – a set of computer instructions to accomplish a task. But the power actually comes from the data. Here’s why.

In this article, we get into why validating these tools is a pivotal step—not only to build trust among pathologists but also as a vital quality assurance for patients, ensuring accurate and reliable diagnoses in real-world settings.

AI is revolutionizing many fields, and the analysis of H&E histology is no exception. This classic method, used for over a century by pathologists, has now entered the digital age. It offers new opportunities for AI to assist in diagnostics and research. However, adapting these advances to histology in a point-of-care setting brings new challenges like scarce labeled data and variations across devices and facilities.

Artificial intelligence relies heavily on algorithms – a set of computer instructions to accomplish a task. But the power actually comes from the data. Here’s why.

Artificial intelligence relies heavily on algorithms – a set of computer instructions to accomplish a task. But the power actually comes from the data. Here’s why.

To recognize the advantages of AI tools and rely on their results in your everyday work, you need to understand how these tools are developed for pathology. With this understanding, you can make informed decisions about their use.

Four reasons why you might need to build your own computer vision solution.

Large vision models may seem attractive, but domain-specific models can get you farther.

A reading list for anyone looking to adapt their machine learning expertise to computational pathology.

The benefits of a self-supervised vision model for generalizability, adaptability, and more.

Machine learning projects are complex and iterative with uncertain outcomes. These are some ways to improve the efficacy of ML development.