In recent years, artificial intelligence (AI) has emerged as a transformative force in healthcare, mainly through advancements in improved large language models (LLMs)*. If you are a clinician looking to implement AI into your practice, understanding the nuances of this technology is pivotal for navigating the evolving healthcare AI terrain.
Understanding the AI Spectrum
Artificial Intelligence (AI): Artificial intelligence refers to the simulation of human intelligence processes by machines, particularly computer systems. These processes include learning (acquiring information and rules for using that information), reasoning (using rules to reach approximate or definite conclusions), and self-correction.
Machine Learning (ML): Machine learning is a subset of artificial intelligence (AI) that involves the development of algorithms and statistical models that enable computers to learn from and make predictions based on data. In machine learning, algorithms are trained on data to identify patterns or make predictions without being explicitly programmed.
E.g.: Predicting the chance of patient readmission.
Deep Learning (DL): Deep learning is a subset of machine learning that uses artificial neural networks with multiple layers (hence the term “deep”) to learn representations of data. Deep learning algorithms can automatically discover patterns and features from raw data, enabling tasks such as image recognition, natural language processing, and speech recognition. Deep learning models work via a mathematical technique called back-propagation. Deep learning may be a path towards AI.
E.g.: Predicting a pneumothorax on a chest X-ray