# Unsupervised Learning in Precision Medicine: Unlocking Personalized Healthcare through AI
## 2 Artificial Intelligence  
The first modern definition of AI dates back to 1956 and is attributed to Professor [PERSON] (Stanford University), who described AI as the science and engineering of creating intelligent machines. Paraphrasing [PERSON], AI can be defined as _\"the use of computers and technology to simulate intelligent behavior and critical thinking comparable to that of a human being\"_. Early foundational contributions began as far back as the 18 th century with pioneers like [PERSON] and [PERSON], whose mechanical inventions laid the groundwork for modern computing [3,4]. By the 1940s and 1950s, researchers such as [PERSON], [PERSON], and others developed the first artificial neurons and neural networks, pushing the field forward. Early computers were particularly adept at solving mathematically driven tasks, which are intellectually challenging for humans but relatively simple for machines, as they can be described by a set of formal mathematical rules. However, the real challenge for AI emerged in replicating perceptual skills that humans have developed over hundreds of thousands of years through evolution. This challenge led to the advent of machine learning (ML), and more recently, deep learning [5], which mimics the layered learning process of the human brain. In a general frame, AI models are capable of acquiring, storing, and using data--known as training data--to learn and enhance their performance [6]. The way these data are gathered, processed, and labeled determines the key differences between various types of AI models, particularly those within ML. An AI algorithm typically receives data either provided by developers or collected autonomously, which it then uses to expand its knowledge base and perform tasks more effectively. There are three main categories of AI algorithms: supervised learning (SL), unsupervised learning (UL), and reinforcement learning (RL) [7]. Each of these categories works based on distinct principles, which influence their applicability in various fields, including Precision Medicine (PM).  
* **Supervised Learning**: In this approach, the training data are labeled with a target, representing the \"expected result\". After the training phase, the system can use the learned information to address problems that involve similar foundational knowledge.
* **Unsupervised Learning**: This method operates on an unlabeled training set, focusing on discovering patterns and relationships within the data without any prior knowledge about its structure or categories.
* **Reinforcement Learning**: Unlike the other methods, reinforcement learning also uses an unlabeled training set but provides feedback in the form of positive or negative results. This feedback creates a loop that allows the algorithm to assess whether its proposed solutions effectively resolve a problem, resembling the human learning process through \"trial and error\".  
The primary differences between these algorithms lie in how they are trained to learn from data, and consequently, in how they operate. While SL relies on labeled datasets to make predictions, UL focuses on uncovering hidden structures without prior labels, enabling it to handle complex, high-dimensional data typical in PM. RL, on the other hand, emphasizes learning through interaction with the environment, refining its strategies based on the outcomes of its actions. While SL and RL have their strengths, UL's ability to analyze unstructured data without prior labeling makes it a powerful tool in PM. For this reason, this review will introduce the most popular and common UL models and explain where they are typically employed in the field of PM.