The Evolution of the Brain From Analog to Digital: The Birth of Artificial Intelligence

Abstract

Recent advances in artificial intelligence (AI) have accelerated the development of systems capable of processing language, images, and multimodal clinical data with unprecedented scale. Among these, transformer-based large language models (LLMs) such as GPT-4 represent a major conceptual leap in how computers learn and generate human language. This review outlines the evolution of machine learning from handcrafted feature extraction to deep representation learning, explains the fundamental principles of transformer architecture and probabilistic text generation, and summarizes current applications of LLMs within plastic and reconstructive surgery. Emerging studies demonstrate promise in automated documentation, resident education, and patient communication, yet also highlight limitations, including hallucinations, bias, and the need for rigorous validation. This review aims to provide clinicians (particularly plastic surgeons) with a concise overview of model structure, training paradigms, and practical considerations for future usage of AI in clinical medicine.

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