Natural Language Processing (NLP) explores how computers can analyze, interpret, and generate human language. In medicine and healthcare, NLP enables the extraction of meaningful information from clinical notes, discharge summaries, scientific literature, and other unstructured text sources, supporting research, decision-making, and the development of intelligent medical applications.
Hunting for the largest language model you can run on a personal computer has turned into a discipline of its own. Forums, repositories and social feeds circulate configurations that load models of 70, 120, even several hundred billion parameters, using consumer GPUs, generous amounts of system RAM and increasingly aggressive quantisation. The results are frequently … Read more
There is a specific moment, familiar to anyone who has gone down this road, when you finish downloading a model with “Med” in its name and realise you have no idea whether it is actually any good. The name promises domain expertise. The model card promises benchmarks. Neither tells you what happens when you ask … Read more
The most compliant place for a patient’s record to meet a large language model might be a machine sitting under your own desk. That sounds like a provocation. It is closer to a legal observation. In May and June 2026 the European Union rewrote part of its own AI rulebook, and most of the coverage … Read more
Machine Learning and Large Language Models for Clinical Text Analysis Article authored by Michele D. Pierri, MD Cardiac Surgeon & Medical Technology Researcher Last updated: May 2025 Reading time: 30 minutes Abstract The first two articles in this series traced a progression from rule-based pattern matching (regular expressions) to linguistic analysis (spaCy/scispaCy) for extracting structured … Read more
Introduction to Natural Language Processing for Medical Report Analysis: Advanced Linguistic Analysis with spaCy and scispaCy for Medical Reports Article authored by Michele D. Pierri, MD Cardiac Surgeon & Medical Technology Researcher Last updated: May 2025 Reading time: 30 minutes Abstract Regular expressions give us a solid foundation for pulling structured data out of clinical … Read more
Part 4: the conclusion of the “Local AI” series — The LM Studio surprise: same model, same hardware, 40-200x faster In Part 3, we pushed gemma4:e4b through four demanding tests. The results were impressive — excellent clinical reasoning, comprehensive summaries, working code. But the wait times were brutal: 5 to 16 minutes per response on … Read more
Part 3 of the “Local AI” series — Coding, reasoning, medical summarization, and clinical decision support Benchmarks tell you how fast a model runs. They don’t tell you if it can actually do anything useful. In Part 1 we set up Ollama, and in Part 2 we measured generation speeds. Now we push these models … Read more
Part 2 of the “Local AI” series — Benchmarks and recommendations for CPU-only inference In Part 1, we installed Ollama and ran our first local model. Now the question becomes: which model should you actually use? The answer depends on your hardware and your patience. On CPU-only systems, model size directly impacts speed — and … Read more
Part 1 of the “Local AI” series — A practical guide to running local LLMs on CPU-only consumer hardware You’ve probably heard that running AI models locally requires expensive hardware: a Mac with Apple Silicon, or a gaming PC with a high-end NVIDIA GPU. This isn’t entirely wrong — those setups are faster. But they’re … Read more
Introduction to Natural Language Processing for Medical Report Analysis: Pattern Matching with Regular Expressions Article authored by Michele D. Pierri, MD Cardiac Surgeon & Medical Technology Researcher Last updated: January 2025 Reading time: 25 minutes Abstract Medical discharge summaries contain critical clinical information that remains largely inaccessible to automated analysis due to their unstructured nature. … Read more