We evaluate the performance of four leading solutions for de-identification of unstructured medical text - Azure Health Data Services, AWS Comprehend Medical, OpenAI GPT-4o, and John Snow Labs - on a ground truth dataset of 48 clinical documents annotated by medical experts. The analysis, conducted at both entity-level and token-level, suggests that John Snow Labs' Medical Language Models solution achieves the highest accuracy, with a 96% F1-score in protected health information (PHI) detection, outperforming Azure (91%), AWS (83%), and GPT-4o (79%). John Snow Labs is not only the only solution which achieves regulatory-grade accuracy (surpassing that of human experts) but is also the most cost-effective solution: It is over 80% cheaper compared to Azure and GPT-4o, and is the only solution not priced by token. Its fixed-cost local deployment model avoids the escalating per-request fees of cloud-based services, making it a scalable and economical choice.

Smart assistants like Amazon's Alexa and Apple's Siri recognize patterns in speech using Natural Language Processing, comprehend meaning and provide a meaningful response. Search engines surface relevant results based on the similar search behaviors. For instance, when you start typing, Google not only predicts what searches may apply to your query, but looks at the whole picture rather than the exact search words. All thanks to NLP as it associates the ambiguous query to a relative entity and provides useful results. These are not the only use cases where Natural Language Processing emerges as a game changer; there are other applications as well.

Low-code and no-code platforms are used to build applications, websites, mobile apps, forms, dashboards, data pipelines, and integrations. No-code platforms help business users, sometimes termed citizen developers, to migrate from spreadsheets, extend beyond email collaborations, and transition from manual task execution to using tools and automations across departments. Low-code platforms are usually for technologists and provide ways to deliver and support software with little or no coding. "You have to remember low code is just a fancy term for abstraction. We are abstracting away non-essential elements in order to simplify the user experience," says Gordon Allott, President and CEO of K3.

Making AI & NLP solve real-world problems in healthcare, life science and related fields. When embarking on an artificial intelligence (AI) project, there's a lot of discussion about choosing the right tool to execute on your vision. While this is an important part of the equation, it skips a crucial first step: finding the right library. But with so many tools and libraries on the market, how do decision-makers find the best fit for the job at hand? Defining the nature of your project is a good place to start. At my company, John Snow Labs, we often get compared to Allen NLP, Stanza, SciSpacy and other libraries that are focused on academic use cases.

John Snow Labs is an award-winning AI and NLP company, accelerating progress in data science by providing state-of-the-art software, data, and models. Founded in 2015, it helps healthcare and life science companies build, deploy, and operate AI products and services. John Snow Labs is the winner of the 2018 AI Solution Provider of the Year Award, the 2019 AI Platform of the Year Award, the 2019 International Data Science Foundation Technology award, and the 2020 AI Excellence Award. John Snow Labs is the developer of Spark NLP - the world's most widely used NLP library in the enterprise - and is the world's leading provider of state-of-the-art clinical NLP software, powering some of the world's largest healthcare & pharma companies. John Snow Labs is a global team of specialists, of which 33% hold a Ph.D. or M.D. and 75% hold at least a Master's degree in disciplines covering data science, medicine, software engineering, pharmacy, DevOps and SecOps.

Don't miss our upcoming virtual workshop with John Snow Labs, Improve Drug Safety with NLP, to learn more about our joint NLP solution accelerator for adverse drug event detection. The World Health Organization defines pharmacovigilance as "the science and activities relating to the detection, assessment, understanding and prevention of adverse effects or any other medicine/vaccine-related problem." While all medicines and vaccines undergo rigorous testing for safety and efficacy in clinical trials, certain side effects may only emerge once these products are used by a larger and more diverse patient population, including people with other concurrent diseases. To support ongoing drug safety, biopharmaceutical manufacturers must report adverse drug events (ADEs) to regulatory agencies, such as the US Food and Drug Administration (FDA) in the United States and the European Medicines Agency (EMA) in the EU. Adverse drug reactions or events are medical problems that occur during treatment with a drug or therapy.

LEWES, Del., Oct. 05, 2021 (GLOBE NEWSWIRE) -- John Snow Labs, the Healthcare AI and NLP company and developer of the Spark NLP library, today announced that it will enable free access to its enterprise-grade Annotation Lab and NLP Server software for all users. This announcement comes on the first day of the company's annual NLP Summit, a free online event that brings together the AI community to discuss the most important trends, use cases, and solutions advancing natural language processing (NLP). The Annotation Lab, a robust data labeling and AI/ML solution for teams, enables users to annotate documents, images, and videos. The software automatically trains models using active learning and transfer learning. The simple and efficient project-based workflow helps users leverage real-time analytics on productivity, dataset bias, inter-annotator agreement, and more.

Natural language processing (NLP) has been a long-standing dream of computer scientists that dates back to the days of ELIZA and even to the fundamental foundations of computing itself (Turing Test, anybody?). NLP has undergone a dramatic revolution in the past few years, with the statistical methods of the past giving way to approaches based on deep learning, or neural networks. Applying deep learning to NLP has led to massive, sophisticated, general purpose language models, like GPT-3, capable of generating text that is truly indistinguishable from human writing. GPT-3, for example, unlocks features such as those found in Microsoft's new "no-code" Power Apps platform, where you can enter a natural language description of a query, and the back end will generate the code (a Power Fx expression based on Excel syntax). NLP has vast potential across the enterprise, and it's not just the giants like Google or Microsoft that are bringing products to the table.

The aim of this article is to run a realistic Natural Language Processing scenario to compare the leading linguistic programming libraries: enterprise-grade John Snow Labs' Spark NLP and Explosion AI's industrial-strength library spaCy, both of which are open-source with commercially permissive licenses. Comparing two different libraries is not as simple as it sounds. Each library has different implementation methods and thus will have different use cases, data pipelines, and characteristics. In this study, a detailed Spark NLP pipeline will be designed and a parallel code mimicking that will be written using spaCy, focusing primarily on runtime speed. We will then compare the results in terms of memory usage, speed, and accuracy.

Natural Language Processing (NLP) is one of the most exciting fields of artificial intelligence that enables computers to understand human languages. NLP techniques are constantly evolving and promising applications are increasingly implemented by organizations to solve a wide range of problems. What exactly are companies using NLP for? What are exciting NLP techniques in a practical context and what are the challenges when applying them? We talked to thought leaders applying NLP in different industries about their favorite NLP techniques, the biggest trends, as well as opportunities and challenges of NLP in 2020.