Shaip

In every stage of AI development for healthcare use cases, experts face tons of data-related challenges. What are they and how do we fix them? We’ve addressed them here. From doctors’ and healthcare providers’ perspectives, AI is paving the way for robotic arms, sophisticated analysis and diagnostic modules, assistive surgical bots, predictive wings to detect genetic disorders and concerns, and more. Check it out this link.  https://www.shaip.com/blog/4-unique-data-challenges-the-use-of-ai-in-healthcare-causes

We all understand that the performance of an artificial intelligence (AI) module depends entirely on the quality of datasets provided in the training phase. However, they are usually discussed on a superficial level. Most of the resources online specify why quality data acquisition is essential for your AI training data stages, but there is a gap in terms of knowledge that differentiates quality from insufficient data. When you delve deeper into datasets, you will notice tons of intricacies and subtleties that are often overlooked. We’ve decided to shed light on these less-spoken topics. After reading this article, you will have a clear idea of some of the mistakes you’re making during data collection and some ways you could optimize your AI training data quality. Let’s get started. The Anatomy of an AI Project For the uninitiated, an AI or an ML (machine learning) project is very systematic. It is linear and has a solid workflow. To give you an example, here’s how it looks in a generi