Artificial Intelligence (AI) in Health Sciences

Recently we have heard a lot about Artificial Intelligence (AI).  The field has expanded tremendously, touching nearly every facet of our daily lives—from tailored suggestions we receive on streaming platforms to the predictive text on our phones.

At its core, AI refers to systems that can perform tasks that normally require human intelligence.  These tasks include problem-solving, understanding natural language, recognizing patterns, and making decisions.

• Problem-solving: This includes tasks such as pathfinding (how Google Maps finds the best route) and optimization problems (determining the best configuration of components in a machine).
• Understanding natural language: This involves not just recognizing spoken or written words, but also understanding their meaning in context. Chatbots, translation software, and voice assistants like Alexa or Siri use this AI capability.
• Recognizing patterns: This is a crucial component of many AI systems, from facial recognition software to fraud detection systems. Machine learning, a subset of AI, often revolves around recognizing and predicting patterns in data.
• Making decisions: This is important for AI systems that need to make decisions in real-time. For instance, autonomous vehicles make decisions about speed, direction, etc. to navigate safely or in a clinical setting an AI tool may analyze a patient's medical records, imaging scans, or laboratory results to suggest potential diagnoses.

One of the defining features of modern AI systems is their ability to learn from data, typically through a process called machine learning. Rather than being explicitly programmed to perform a task, these systems use algorithms to find patterns in data and make predictions or decisions based on those patterns.

• Supervised Learning: This is where the model is provided with input-output pairs. The goal is to find a general rule that maps inputs to outputs. Most of the practical applications of AI, such as image recognition, use supervised learning.
• Unsupervised Learning: This model is given data without explicit instructions on what to do with it. The system tries to learn the patterns and the structure from the data without any labeled responses to guide the learning process. Clustering and association are two types of problems solved by this method.
• Reinforcement Learning: This model iteratively refines its decisions based on feedback, much like how students might refine their clinical skills based on feedback from simulations or actual patient interactions. This approach is often used in robotics and gaming and has been explored in health care setting for personalizing treatment strategies for patients.