Insights on NLP in Healthcare & Life Sciences Global Market to 2028

Global NLP

The “Global NLP in Healthcare and Life Sciences Market Size, Share & Industry Trends Analysis Report By Component, By Solution Type, By End User, By NLP Type, By Deployment Mode, By Organization Size, By Application, By Regional Outlook and Forecast, 2022 – 2028″ report has been added to’s offering.

The Global NLP in Healthcare and Life Sciences Market size is expected to reach $6.8 billion by 2028, rising at a market growth of 20.3% CAGR during the forecast period.

Natural Language Processing (NLP) refers to a computer program’s ability to comprehend and present data in the form of current human language, speech phrases, and text. In the healthcare industry, NLP is employed in a variety of ways, including improving the quality of care and raising outcomes, as well as automating virtual patient conversational activities. Email filtration, predictive messaging, smart assistance, digital phone calls, and language translations are all examples of where NLP is applied.

Doctors may spend as long as necessary with their patients and give them their undivided attention due to the NLP platform. A number of clinicians prefer printed or typed voice notes. As a result, the NLP platform may be utilized to accurately analyze speech and update data. Unstructured data in real-world data sources like EHRs, patient forums, and other sources make extracting usable insights from the data challenging and time-consuming.

This issue is alleviated by AI-powered NLP technology. Pharma companies are using natural language processing (NLP) in drug discovery, text mining EHR data, and utilizing data to produce future insights for commercial advantages, resulting in actionable insights that improve care and efficacy. Furthermore, NLP has a wide range of applications in the pharmaceutical industry, including drug development, clinical trials, regulatory insights, market insights, real-world data, pharmacovigilance, and more.

Market Growth Factors

Need for Analyzing and Extracting Insights from Narrative Text

The need for improved utilization of unstructured data is being driven by a shift in business models and outcome expectations. Traditional health information systems have concentrated on extracting value from the relatively small quantities of structured healthcare data received through clinical channels. However, NLP can extract patient information from unstructured, free-form language and generate actionable data that can be utilized to improve patient care and expedite workflow. NLP systems that are well-designed can assess text-free dictation, recognize situations, and tag the most important clinical data items such as problems, social history, drugs, allergies, and treatments.

Development of Cognitive Computing

Some well-known businesses in the market have made considerable investments in semantic big data analytics and cognitive computing technologies in the healthcare and life sciences industry. NLP offers a wide range of applications in healthcare, from cutting-edge precision medicine applications to the simple task of coding a claim for reimbursement or billing. However, developing algorithms that are smart, accurate, and specific to ground-level concerns in the healthcare and life sciences industries will be critical to the success of deploying this technology. In order for patients to have an accurate record of their health in a language they can comprehend, NLP will have to achieve the dual aims of data abstraction and data presentation. Within the healthcare industry, this enhanced approach is expected to improve physical efficiency while lowering operating expenses.

Market Restraining Factors

High Cost of R&D in NLP

NLP is a technique for processing sequential data such as text, speech, financial data, time series, audio, and video that employs neural networks and deep learning algorithms. The most sophisticated technologies that are laying the groundwork for NLP to acquire momentum in the market are neural networks and deep learning. However, developing these technologies is extremely costly and necessitates a significant investment in both R&D funds and time, which is difficult for small or startup enterprises looking to enter the NLP market in healthcare and life sciences.

Scope of the Study

Market Segments Covered in the Report:

By Component

  • Solution
  • Services
    • Clinical Variation Management
    • Population Health Management
    • Counter Fraud Management
    • Others

By End User

  • NLP for Physician
  • NLP for Patients
  • NLP for Researchers
  • NLP for Clinical Operators

By NLP Type

  • Rule-based
  • Statistical
  • Hybrid

By Deployment Mode

  • Cloud
  • On-premise

By Organization Size

  • Large Enterprises
  • Small & Medium Enterprises (SMEs)

By Application

  • IVR
  • Summarization & Categorization
  • Reporting & Visualization
  • Pattern & Image Recognition
  • Text & Speech Analytics
  • Predictive Risk Analytics
  • Others

By Geography

  • North America
  • US
  • Canada
  • Mexico
  • Rest of North America
  • Europe
  • Germany
  • UK
  • France
  • Russia
  • Spain
  • Italy
  • Rest of Europe
  • Asia Pacific
  • China
  • Japan
  • India
  • South Korea
  • Singapore
  • Malaysia
  • Rest of Asia Pacific
  • Brazil
  • Argentina
  • UAE
  • Saudi Arabia
  • South Africa
  • Nigeria
  • Rest of LAMEA

Key Market Players

  • 3M Company
  • IBM Corporation
  • Microsoft Corporation
  • Google LLC
  • Amazon Web Services, Inc.
  • Cerner Corporation
  • Corti ApS
  • Lexalytics, Inc.
  • Health Fidelity, Inc.
  • Linguamatics

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