Under the Hood: A Healthcare Executive's Guide to AI Buzzwords Pt. 1

Editor’s Note: We're excited to introduce a new monthly series designed to demystify technical concepts in healthcare AI, empowering healthcare leaders to make informed decisions. This month kicks off with Part 1, providing clear explanations of common AI acronyms and terms.

Healthcare is already notorious for its jargon overload (GMLOS, anyone?). Now, as artificial intelligence moves to center stage, executives face a fresh wave of confusing buzzwords. Vendors seem locked in a high-stakes "buzzword bingo," tossing around promises like "transformative AI" and "cutting-edge RLHF." But what's behind these terms—and how can healthcare leaders sift through the noise to identify genuine opportunities?

Artificial intelligence as a research discipline began in earnest in the mid-20th century, but most real-world applications in healthcare come from its specialized branches. Though AI encompasses many areas, a select few have become central to healthcare innovation:

Machine Learning (ML): ML is the backbone of most healthcare-focused AI solutions, involving algorithms that improve their performance by learning from data without explicit, step-by-step instructions. Common applications include predicting patient deterioration through vital signs and spotting billing anomalies to preempt claim denials.

ML typically breaks down into two types:

• Supervised Learning: Algorithms learn from labeled examples where outcomes are known, such as predicting hospital readmissions based on historical patient data, identifying denied claims from past billing records, or diagnosing conditions from labeled imaging datasets.

• Unsupervised Learning: Algorithms detect patterns in unlabeled data, useful for tasks like grouping patients into risk cohorts based on health characteristics, segmenting patient populations for targeted interventions, or identifying anomalies in large clinical datasets without prior definitions.

Classical ML techniques like random forests (algorithms that use multiple decision trees to improve accuracy) and logistic regression (a statistical model used to predict binary outcomes) have long powered essential healthcare analytics—from predicting hospital stays to assessing readmission risks.

Natural Language Processing (NLP) and Computer Vision (CV) While technically considered broader domains rather than strict subfields, NLP and CV cut across multiple methods and techniques:

• NLP enables computers to understand, interpret, generate, and respond to human language. In healthcare, NLP extracts insights from clinical notes, identifies social determinants of health, and suggests medical codes.

• CV empowers computers to interpret visual data, including images and video. Healthcare applications include analyzing chest X-rays for pneumonia, mammograms for breast cancer, and retinal scans for diabetic retinopathy.

Next month, we'll explore deep learning, neural networks, transformers, and large language models, the building blocks for the explosion in progress we’ve seen in AI over the last several years. Understanding these critical technologies will help illuminate exactly how it’s no longer science fiction to imagine a world where AI agents fully replace humans in complex workflows.