NLG is a section within AI technology that develops solutions to automatically generate natural language – or language spoken and written by humans. NLG platforms can create high-quality texts depending on the solution, based solely on Machine Learning (ML) or on the insights gained from data. Robert Weissgraeber at AX Semantics explains why use cases for NLG are continuing to expand as more and more companies work to leverage their data.
Extract:
‘How to Approach an NLG Solution in the Pharmaceutical Industry’
NLG is a section within AI technology that develops solutions to automatically generate natural language – or language spoken and written by humans. NLG platforms can create high-quality texts depending on the solution, based solely on Machine Learning (ML) or on the insights gained from data.
Use cases for NLG are continuing to expand as more and more companies work to leverage their data. The thinking on NLG is evolving, too. In 2015 Gartner argued that NLG was “the last mile in Business Intelligence” and would succeed as plug-ins describing graphs in plain language. In contrast, Gartner has a larger vision today, saying, “NLG is useful wherever there is a need to generate text or spoken content from data.”
As the use cases for NLG multiply and companies begin to deploy it en masse, a framework to evaluate NLG vendors is needed to cut through the rhetoric to determine which vendor is best suited for your use case.
There are some additional aspects that to consider internally. Internal preparations are required when introducing such a new technology, as new technology affects both the data requirements and the internal content-creating processes.
Clinical Study Reports – An NLG Use Case in the Pharmaceutical Industry
Given the pharmaceutical sector’s high demand for regulatory documentation, several suitable options for implementing content generation are available. In this context, clinical study reports (CSR) are good examples of the potential and benefits of automation.
The most challenging phase of bringing a drug to market is the human drug trial, during which time clinicians must write a CSR that describes the pharmacological impacts and trial outcomes. Typically, medical writing teams gather data from the human drug trials and then manually compile the report. However, this outdated, arduous and time-consuming process can potentially delay life-saving medications from coming to market sooner and cost pharmaceutical companies millions.
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