AI Articles and Analysis in Healthcare

Life sciences companies are likely to begin experimenting further with AI in their workflows in the coming years, but they face challenges in AI adoption due to strict regulations. Machine learning has a "black box" problem, meaning that it's in many cases impossible to know how a machine learning algorithm comes to its conclusions.

Artificial intelligence is changing the way healthcare networks do business and physicians perform their routine activities from medical transcription to robot-assisted surgery. Although the more mature use-cases for AI in healthcare are those built on algorithms that have applications in various other industries (namely white-collar automation), we believe that in the coming three to five years, AI solutions for healthcare will become increasingly specialized to individual use-cases.

There are many possibilities for automation in the healthcare industry outside of AI. Robotic process automation (RPA) technology can serve healthcare companies with various use cases involving data transfer and clinical documentation. Moving important information from the business’ frontend to their deeper business processes is among the most common use cases for RPA in healthcare, and many other solutions emerge from this idea. A similar phrase and field to RPA is called White Collar automation and readers can find a full interview on white collar automation in healthcare today here. 

The healthcare industry holds perhaps the most responsibility of any industry when it comes to ensuring data privacy. A breach in electronic medical records (EMRs) could tarnish a healthcare company's reputation, put undue stress on patients, and render the company in violation of regulations.

Generic pharmaceuticals require less research and development than their brand name counterparts. As a result, AI applications for research and development don’t seem to be the most prominent solutions for generic drug companies. That said, despite the lack of precedence, there may be many areas in which AI could help generic drug companies.

AI applications for automating processes in clinical trials are among the most prominent AI applications for the pharmaceutical industry. AI vendors are currently offering software that allows pharmaceutical companies to leverage their scientists' notes for data science projects regarding their future trials. Additionally, there are some applications which help companies segment their customers into easily navigable groups when finding patients for clinical trials.

AI applications in healthcare are becoming more common for white-collar automation and diagnostics. However, medical robotics is an area that may be marginally underdeveloped. This is likely because of regulations concerning automated surgery.

The facilitation of research and development (R&D) is perhaps the most common use case for AI applications in the pharmaceutical industry. There are numerous solutions for extracting and organizing research data from clinical trial notes and other medical documents. Additionally, there is software that can purportedly analyze data from images of drug compounds at the molecular level.