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Artificial Intelligence for Credit Cards Current Applications

AI-based fraud detection is among the most widely discussed AI applications in the financial sector, and it seems to work for credit cards similarly to how it works for banks.

this article, we detail three prominent use-cases for AI software in the credit card industry and describe how customers interact with some of them daily.

Artificial Intelligence for Generic Drug Companies – Current Applications

Generic pharmaceuticals require less research and development than their brand name counterparts.

In this article, we discuss what might be possible with AI in the generic drug industry, such as: We begin our exploration of AI for generic drug companies with how predictive analytics might help generics companies find biosimilars: Generic drug companies might use AI for finding alternatives to brand-name drugs, otherwise known as biosimilars.

These companies likely have a large database of existing drug and chemical data, but that data may need to be processed and labeled before it can be run through a machine learning algorithm.

As such, data on these drugs might be labeled according to their solubility, their reactions to manufacturing, the shapes of their crystal structures, and other data points that an algorithm might use to correlate the drug to brand name counterparts.

For example, microscopic images of a drug compound forming a crystalline structure would need to be labeled according to where the molecules bind and the shape of the structure.

Natural language processing-based drug discovery applications for example, are typically used for sifting through large amounts of clinical trial notes or electronic medical records.

Augusta uses predictive analytics to determine if drug compounds will bind, among other use-cases, which could help generic drug companies determine if the biosimilars they discover can be combined to create a generic drug.

A generic drug company that employs in-house data scientists and subject-matter experts that know how to speak what we call the “language of data science”

However, finding useful training data for a machine learning model that analyzes the drug at the molecular level may pose a challenge to generic drug companies.

This is because they are able to form predictions about the solubility of a biosimilar based on large databases on past drugs, molecules, and research related to past clinical trials.

Some AI vendors claim their solutions can analyze large amounts of information on biosimilars to reveal information about their chemical traits, such as compound solubility and the shapes it can take when manufactured differently.

A predictive analytics solution could analyze client research data on thousands of compounds for correlative data points about a compound’s solubility.

claims to offer a software that can analyze molecules and search for crystal structures within its database Below is an image showing how their software purportedly does this:

This could be helpful in determining many important factors to generic drugs, including how the active compound in the original drug reacts to being processed and manufactured differently.

This widespread lack of adoption could be indicative of the fact that the brand name pharmaceutical industry has more practical uses for these innovations along with the means to integrate and develop them further.

When we asked him about how innovations from the top global companies will find their way to more companies, Tripathy said, Now what startups will do, like us, they will basically take all of these algorithms that get pumped out and figure out how to apply them to specific business cases, so it’s the application of AI.

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