Pharmaceutical organizations have to submit proposals for the approval of new drugs. These proposals also contain the information for 'labels.' Label information is the information that is provided with the drug when it rolls out in the market. It is the information that we see on the packaging, the containers, or in the form of paper pamphlets inside them.

When a proposal is submitted for approval, it is not just submitted to one organization but many across the globe. This is why, before submission, it needs to be reviewed extensively for any errors. More than that, it needs to be compared with the labels of other drugs in the same organization or with the labels of similar drugs from other organizations. This comparison highlights a plethora of information, like grammatical errors, differences in the safety information, terminology inconsistencies, missing information, etc.

These labels are much more extensive when submitted for approval (can near up to a 100 pages), which is why their comparison and revision is a painstakingly slow and an excruciatingly extensive process.

This is where I come in. I worked as the UI UX Designer on this project. My team at Tata Consultancy Services created an AI system that could do this job within minutes. Now it lends this service to some of the major pharmaceutical companies all over the world.