AI Articles and Analysis in Healthcare

We've spoken to many leaders in healthcare and pharma over the last half a decade, and when it comes to AI, the most pressing challenge that healthcare and pharma leaders report is that they're unsure of how to streamline and structure their data in a way that lets them build machine learning models. Healthcare companies are stuck in the data consolidation phase of their potential AI initiatives while vendor after vendor is trying to sell them on a new application that the company might not even be close to ready for.

Natural language processing (NLP) seems to see less use in pharma than AI approaches such as machine vision and predictive analytics, but nevertheless there are a few applications for NLP in pharma. The industry deals mostly with structured data, but in some business areas, unstructured data is the norm. In this article, we discuss how natural language processing can help pharmaceutical companies make sense of their unstructured data and use it to make decisions.

Episode Summary: There's a lot of venture money pouring into artificial intelligence in healthcare. From pharma to hospitals and beyond, the potential applications in healthcare are promising. 

Business leaders in pharmaceuticals may be concerned with how machine vision will affect their operations in the clinical and scientific departments more than those of packaging and administration. However, prioritizing these types of operations may not provide the ROI as it’s hyped up to be. Instead, there are many possibilities for machine vision in pharmaceuticals related to packaging, shipping, and data entry.

It may feel as though AI applications like machine vision and natural language processing hold the most potential value to pharmaceutical companies because of their capabilities to intake and transform unstructured medical data. This is especially true with machine vision, as medical imaging data can be used across multiple departments when analyzed by AI software.

When it comes to the healthcare industry, one might be able to think of numerous use cases for AI approaches like machine vision or predictive analytics. However, the applications of natural language processing (NLP) in healthcare are just as varied.

Health insurance fraud may prove especially pressing due to the opioid epidemic, and thus routing out fraud could be of greater importance in the coming years.

There are many Indian startups claiming to develop artificial intelligence software to help the healthcare industry. However, their websites are filled with marketing terms and hype that could lead one to believe a company leverages AI when it does not. In sectors with a booming landscape of startups, we caution readers to take a closer look at each company before considering any of their solutions.