AI in Medicine: Introduction and Fundamentals

Questions and Answers

Making AI systems more transparent and interpretable

A subset of AI that involves training algorithms to learn patterns from data

Using deep learning models that can automatically learn features from raw data

Being unable to handle complex tasks and adapt to new situations Signup and view all the answers

Artificial Intelligence Signup and view all the answers

Using machine learning algorithms that require significant feature engineering Signup and view all the answers

Both A and B Signup and view all the answers

Reasoning Signup and view all the answers

Supervised Learning Signup and view all the answers

Grid-like data processing Signup and view all the answers

To enable computers to understand human language Signup and view all the answers

Dimensionality reduction Signup and view all the answers

To break down text into smaller units Signup and view all the answers

Recurrent Neural Network (RNN) Signup and view all the answers

Autonomous vehicles Signup and view all the answers

To identify grammatical parts of speech Signup and view all the answers

Study Notes

Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, particularly computer systems, which includes learning, reasoning, and self-correction.
AI applications include expert systems, natural language processing (NLP), speech recognition, and machine vision.

First Generation (Symbolic AI): Also known as classical AI, it relied on manually crafted rules and logic, which limited its ability to handle complex tasks and adapt to new situations.
Second Generation (Statistical AI): Introduced machine learning (ML) where algorithms could learn from data, with techniques like decision trees, support vector machines, and Bayesian networks.
Third Generation (Deep Learning): Marked a significant advancement with the rise of deep learning, which can automatically learn features from raw data, leading to breakthroughs in image and speech recognition, NLP, and more.
Fourth Generation (Explainable AI): Focuses on making AI systems more transparent and interpretable, especially in critical fields like healthcare.

Definition: A subset of AI that involves training algorithms to learn patterns from data and make predictions or decisions without being explicitly programmed.
Types of ML:
- Supervised Learning: Trained on a labeled dataset, where input-output pairs are known, e.g., regression and classification tasks.
- Unsupervised Learning: Trained on an unlabeled dataset, where the model finds patterns and relationships within the data, e.g., clustering and dimensionality reduction.
- Reinforcement Learning: Learns by interacting with an environment and receiving feedback in the form of rewards or penalties.

Definition: A subset of ML that uses neural networks with many layers (deep neural networks) to model complex patterns in large datasets.
Applications: Revolutionary in areas such as image and speech recognition, NLP, and autonomous vehicles.
Key Concepts:
- Neural Networks: Computational models inspired by the human brain, consisting of interconnected nodes (neurons) organized in layers.
- Convolutional Neural Networks (CNNs): Specialized neural networks for processing grid-like data, such as images.
- Recurrent Neural Networks (RNNs): Neural networks designed for sequential data, such as time series or natural language.

Definition: A field of AI that focuses on the interaction between computers and humans through natural language.
Involves enabling computers to understand, interpret, and generate human language.
Applications: Used in applications like language translation, sentiment analysis, chatbots, and speech recognition.
Key Techniques:
- Tokenization: Breaking down text into smaller units, such as words or phrases.
- Part-of-Speech Tagging: Identifying the grammatical parts of speech in a sentence.
- Named Entity Recognition (NER): Detecting and classifying named entities, such as people, organizations, and locations.