Natural language processing (NLP) in healthcare isn’t going away anytime soon, quite the contrary. In the coming years, organizations that resist this rapidly advancing technology will fall behind on speed, precision, and ROI.
To that end, we’re spotlighting five standout stories from May that demonstrate the power and potential of clinical NLP – which transforms unstructured clinical data into clear, reliable insights – from pharma R&D to chronic disease management.
1. Clinical NLP market set for $7.2B surge
With use cases ranging from trial matching to patient data analysis, the global market for NLP in healthcare is expected to more than triple by 2027. According to a recent article, “Rising need to extract insights from unstructured clinical data, growing demand for automated medical documentation, increased use of AI in drug discovery, expansion of telehealth services, and the push for improved patient engagement and operational efficiency are driving NLP applications in healthcare and life sciences.” [May 29]
2. AI and NLP accelerate drug development
A recent study shows how clinical NLP is helping speed up drug discovery by extracting insights from clinical trial reports and electronic health record (EHR) data. While NLP has the potential to “enhance formulation, accelerate discovery, and repurpose existing medications,” it requires strong ethical oversight “to ensure its use remains safe, ethical, and free from bias.” [May 25]
3. Dementia detection gets smarter with clinical NLP
Healthcare NLP tools are now detecting subtle patterns in patient records, supporting earlier detection of dementia and more proactive care. Clinicians often miss early symptoms like mild cognitive changes or behavioral challenges in routine assessments. Clinical NLP can help uncover them faster and more consistently. [May 19]
4. Improving Ulcerative Colitis care with NLP
By analyzing endoscopy reports and clinical notes, NLP helps standardize scoring and guide biopsy decisions, making care more precise and efficient. These tools reduce variability in how disease severity is assessed, supporting more personalized treatment. [May 15]
5. NLP tech enhances Clinical Information Retrieval
Advanced NLP techniques like Named Entity Recognition are now reaching 91% accuracy in identifying diseases and medications, greatly enhancing clinical data usability. Improvements like this make healthcare information retrieval faster, more accurate, and more scalable. [May 27]
