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AI Creates a Human-Computer System in Life Sciences

human-computer system

Previously, I discussed artificial intelligence (AI) enhancing clinical data review processes. This blog discusses how AI assists to create a human-computer system.

Humans and machines each have their own strengths. On the one hand, machines are good at processing and analyzing large volumes of data with high speed and accuracy. On the other hand, humans are good at making decisions based on data, interacting with other humans, and applying general intelligence to the data.

When it comes to reviewing clinical data, a human-computer system will perform better than either standalone method. The AI initiatives that revolve around clinical trials require humans and machines to work together.

Machine learning models can understand clinical data, do an initial analysis of the data, and perform an initial cleaning of the data using statistical analysis. Based on the clean/dirty probability, humans (data reviewers) can prioritize their review activity. They can search data using NLP and obtain the status of their activities using a self-documenting platform that gives them a current summary of the clinical trial state.

Analysis using machine learning models can provide more insight into clinical data and enable humans to determine the safety and efficacy of the trial.

To learn more about how artificial intelligence – including machine learning – can be used by pharmaceutical and medical device companies to improve the clinical data review and cleansing process, you can download the guide here or submit the form below.

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Prabha Ranganathan

Prabha Ranganathan is a delivery director at Perficient and is responsible for delivering data warehousing and analytics solutions for various life sciences and health care companies. Prabha works closely with customers providing strategic advice on clinical data flows, data reviewing and cleaning using latest technological tools and solutions. Prabha has experience in building and releasing products from concept to release at Oracle, in various roles as Product Manager, Architect and Lead Developer. During most of her career, she has worked on enterprise products dealing with large volumes of data from various sources that need to be reviewed, cleaned and analyzed. With a clear understanding of business and strong technical knowledge, she brings a unique skillset to solve complex problems. She received her MBA from Babson College and M.S in Computer Science from Illinois Institute of Technology.

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