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Next-generation technologies such as Natural Language Processing (NLP) and Natural Language Generation (NLG) can prove to be crucial in advancing regulatory medical writing and has unlocked new possibilities in the field of text mining, processing and generation. Additionally, it has been instrumental in streamlining the medical writing process. In part-1 of the article, experts from TCS ADD investigate the best AI-driven approaches and summarization techniques for generating accurate articles with minimal efforts. Through the part-2 of this article, the authors provide their insights on the pure abstraction technique for medical writing. They also discuss how adopting a hybrid strategy, combining both extractive and abstractive summarization techniques can be instrumental in regulatory medical writing.
About the authors
Image of Niketan Panchal
Niketan Panchal
Niketan Panchal works for Tata Consultancy Services as a Researcher (TCS). He has been with the TCS ADD Life Sciences Platform for about 1.5 years as a Researcher in the R&I Team. Niketan possesses Data Science skills. In his current position, Niketan uses cutting-edge Deep Learning and Artificial Intelligence techniques and models to solve business problems in the Life Sciences domain.
Image of Ashutosh Pachisia
Ashutosh Pachisia
Ashutosh Pachisia is a Data Scientist at Tata Consultancy Services (TCS). With nearly half a decade of experience in IT. He has a rich experience leading large complex programs and driving key initiatives for TCS Healthcare.
Image of Rohit Kadam
Rohit Kadam
Rohit Kadam is a Researcher at Tata Consultancy Services (TCS). He is working with the TCS ADD Life Sciences Platform as a Researcher in R&I Team for nearly 2.5 years and contributed extensively in developing key solutions for TCS Healthcare Domain.
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