According to a research report “NLP in Healthcare and Life Sciences Market by Component (Solutions and Services), NLP Type (Statistical, Hybrid), NLP Technique (IVR, OCR), Application (Clinical Trail Matching, Drug Discovery), End Users and Region – Global Forecast to 2027″ published by MarketsandMarkets, the NLP in healthcare and life sciences market size to grow from USD 2.2 billion in 2022 to USD 7.2 billion by 2027, at a Compound Annual Growth Rate (CAGR) of 27.1% during the forecast period. Various factors such as increasing demand of analyzing and extracting insights from narrative text and huge amount of clinical data, and the evolution of cognitive computing to fuel the demand for NLP solutions for precision medicine applications are expected to drive the adoption of NLP solutions in healthcare and life sciences market.
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According to Lexalytics, NLP is a computing technique that analyzes text to determine its meaning, enabling computer applications that can communicate effectively with humans. Healthcare providers, pharmaceutical companies, and biotechnology firms all use text analytics and NLP technology to improve patient outcomes, streamline operations, and manage regulatory compliances.
Automated Registry Reporting segment to register the highest CAGR during the forecast period
The segmentation of the NLP in healthcare and life sciences market by the application segment includes sentiment analysis, drug discovery, clinical trial matching, dictation & EMR implications, automated registry reporting, AI chatbots & virtual scribe, and other applications (review management, question answering, spell correction, and email filtration). The automated registry reporting segment is developing rapidly due to many major technological advancements resulting in enhancing the efficiency of the overall industry. Health systems will need to recognize when an ejection percent is recorded as part of a note and preserve each number in a format that can be used by the organization’s analytics platform for automated registry reporting to implement automated reporting.
Large Enterprises segment to register for the highest market size during the forecast period
Based on organization size, the NLP in healthcare and life sciences market is segmented into large enterprises and SMEs. Currently, the market share of large enterprises is higher; however, the market for SMEs is expected to increase at a higher CAGR during the forecast period. These organizations generate a large volume of data, which must be implemented in their work through collaboration among different teams. The data generated from these large enterprises can then be stored at a lower cost without compromising safety and agility through NLP solutions.
APAC to have a higher CAGR during the forecast period
The NLP in healthcare and life sciences market has been segmented into five major regions: North America, Europe, Asia Pacific, Latin America, and Middle East and Africa. Asia Pacific is expected to have the highest CAGR during the forecast period. The region is growing at a good pace because of the major multinational players coming over and a lot of new entrepreneur setups are adopting cloud-based NLP solutions, as using these solutions healthcare and life sciences firms harness the relevant insights and concepts from the clinical data that was previously considered buried in the text form. China, Japan, and India are displaying ample growth opportunities in the NLP in healthcare and life sciences market.
Some major players in the NLP in healthcare and life sciences market include IBM (US), Microsoft (US), Google (US), AWS (US), IQVIA (US), Oracle (US), Inovalon(US), Dolbey (US), Averbis (Germany), SAS Institute (US), Press Ganey (US), Ellipsis Health (US), Health Fidelity (US), Centene (US), Caption Health (US), Clinithink (US), HPE (US), Oncora Medical (US), Flatiron Health (US), Apixio (US), Forsee Medical (US), Gnani.ai (India), Notable (US), Biofourmis (US), Suki (US), Wave Health Technologies (US), Corti (Denmark), CloudMedx (US), MedlnReal (US), Emtelligent (US).
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