AI Articles and Analysis in Healthcare

According to the National Institute of Mental Health, the United States is currently battling a mental health epidemic. One in every five Americans struggles with mental illness in one form or another. According to the National Institute on Drug Abuse, opioid abuse claims about 115 American lives every day. According to the Center for Workplace Mental Health founded by the American Psychiatric Association, up to 7% of full-time workers in the U.S. suffer from major depressive disorder, the economic cost of which is estimated to be $210.5 billion per year.

Machine vision has numerous use cases within the healthcare industry, including clinical solutions such as medical imaging and medical diagnostics. There are also possibilities in white collar automation such as medical transcription, which was one of the main interview topics in our white collar automation interview with the Executive Chairman of CognitiveScale.

Predictive and prescriptive analytics seem to be the most common types of AI applications at the top medical device companies. These companies, particularly Becton Dickinson, Medtronic, and Fresenius Medical Care, tend to partner with AI firms and vendors to create software that they intend to sell to hospitals most often.

Although one might assume healthcare companies are investing the most amount of money into AI for R&D efforts, such as clinical trials, drug discovery, and surgical robotics, the top healthcare companies don't seem to be publicizing their efforts if they are.

Various use cases and applications for AI and machine learning in the healthcare industry are proposed more frequently now than ever. Healthcare leaders may find it difficult to keep up with where AI is being applied in their sector.

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.