biologit’s Post

I'm frequently asked, 'how can you really use AI in life sciences and healthcare?' Also, 'what are the practical applications and use cases?' One of the most direct is the use of AI in literature reviews and extraction. Traditionally literature review and data extraction in pharmacovigilance are often slow and manual, leading to inefficiencies. Generative artificial intelligence (AI), using advanced natural language processing (NLP), offers a powerful solution by automating the identification and categorization of adverse events (AEs) from science literature. Join this webinar (Thursday, 26 September 2024 at 10am ET / 7am PT), hosted by my colleagues Updesh Dosanjh and Kandala Vinitha as they explore how generative AI enhances the speed to accuracy of literature analysis, surpassing traditional methods. Key topics to be covered include: - The power of generative AI in literature case extractions - Current challenges in generative AI implementations - Learning experiences from pilots and implementations #AIandML #GenAI #Pharmacovigilance #Pharma #Safety #Patients #Innovation #NLP #AdverseEvents https://lnkd.in/e2_zmGyU

Ready for Part 2 of our AI in Quality Management series? In our previous post, we discussed how AI is transforming eQMS solutions in the life sciences industry. Now, we look deeper into those specific AI tools and technologies that are changing how quality is managed. In this post, we explore AI-powered solutions along with Harsh Thakkar, CEO of Qualtivate. Read on to learn how areas like machine learning (ML) and natural language processing (NLP) are accelerating innovation in areas like drug development, clinical trials, and manufacturing processes. 👉 https://lnkd.in/eiQ3xjrY #AI #LifeSciences #QualityManagement #QMS #Innovation #MachineLearning #NLP #Pharma #Biotech #DigitalTransformation

Using Natural Language Processing to Visualize Narrative Feedback in a Medical Student Performance Dashboard "Clinical competency committees rely on narrative feedback for important insight into learner performance, but reviewing comments can be time-consuming. Techniques such as natural language processing (NLP) could create efficiencies in narrative feedback review." In this study, the authors explore whether using NLP to create a visual dashboard of narrative feedback to preclerkship medical students would improve competency review efficiency. Read the full article here: https://ow.ly/eJuv50TfJV6. #MedicalEducation #AcademicMedicine #Feedback #AI #ArtificialIntelligence

💡 How is AI reshaping the future of medical writing? In this Xtalks Spotlight, Maria Hopfgarten, Head of Global Medical Writing at PPD Clinical Research, Thermo Fisher Scientific, explores how the integration of AI and human intelligence (HI) is creating new efficiencies. Learn about the challenges, opportunities, and the evolving role of medical writers in an AI-assisted environment, driven by advancements in natural language processing (NLP). Watch the full interview here: https://buff.ly/49yE5bv

Excited to share how Fast Data Science is revolutionising clinical trial analysis with NLP! This innovation enables more accurate cost estimations and streamlined trial planning. Learn more about our work: https://lnkd.in/dnrXZs4y #ClinicalTrials #NLP #AI #Pharma #clinicalresearch

Here's a fun fact about AI technology: AI can recognize and generate human-like speech! Through natural language processing (NLP), AI systems can understand and respond to text or spoken inputs in a way that closely mimics human communication. One fascinating example is how AI can generate creative content, like poems, stories, and even music, showcasing its potential not just in analytical tasks but also in the arts!

Discover Natural Language Processing! 🗣️ Transform your understanding of AI at the KInIT #NLP Day! 💭 Discover how NLP sits at the intersection of AI, linguistics, and computer science 💻 Learn about its main tasks, historical developments, and breakthrough applications 🧑💻 Understand the limitations and ethical considerations of current approaches. 🫵 Find more information here: https://lnkd.in/dWSK-4fg

How are Clinical Data Standards implemented? What makes the automation of EHR to EDC efficient? 🧐 Find the answers in the first #JSCDM issue of 2025! This issue features original research papers on the use of #ClinicalDataStandards among #CDM professionals, explores how Natural Language Processing (NLP) is streamlining patient identification in #ClinicalTrials, and analyzes the core #CDM competencies in Japan. Start the issue with a letter from the 2024 SCDM Chair, Patrick Nadolny, where he provides a "Year in Review" and shares what's coming up in 2025. Pull up a chair, relax, and immerse yourself in the #CDM knowledge 📖https://lnkd.in/efd7yyku Claudine Moore

Advancements in AI, particularly generative AI and Large-Language Models (LLMs), are revolutionising clinical trials. Traditional trial designs are inefficient, leading to a mere 10% success rate for FDA-approved drugs. TrialKey's integration of Natural Language Processing (NLP) transforms clinician notes into structured data, enhancing analysis across +450,000 trials. By addressing trial design flaws, TrialKey can improve success rates by up to 30%, saving time and resources while optimising outcomes. 🔗 Book a demo: https://appt.link/minhthu #clinicaltrials #ai #medicalresearch #trialkey #drugdiscovery #healthtech 

The #USMLE program recently attended #AAMC24 and shared program updates in a panel discussion. Key takeaways included: ✔️The program is exploring new ways to assess core clinical skills and competencies, such as clinical reasoning and communication skills, and how new technologies like AI and natural language processing (NLP) may play a role. ✔️A behind-the-scenes transition to streamline examinee services will start no earlier than mid-2025. ✔️Recent updates were made to USMLE.org to improve navigation and make content more accessible. Stay tuned for more enhancements to examinee communications in 2025, including a monthly newsletter.