Generative AI

• Artificial Intelligence (AI)

  Artificial intelligence (AI) and its application is the next big thing in dermatological imaging, and includes, but is not limited to, image acquisition, processing, interpretation, reporting and follow‐up planning.[[ 1]] Furthermore, there are additional benefits of data integration, data storage and data mining. In fact, the possible applications are so many that AI is expected to become an inseparable tool in a dermatologist's life. However, most dermatologists are still illiterate in AI. While we are still trying to understand AI, two other terminologies: machine learning (ML) and deep learning (DL) are already the talk of academicians. Most of the medical schools still do not teach AI as a part of academic curriculum, and the interchangeable use of these three terms in literature provides no help to young researchers. The younger generation, which is at the forefront of facing this revolution, needs an awareness and clarity in terms of definition and differences of the three terminologies.

• Artificial Intelligence (AI) Literacy

1. Understand the basics of how AI works
2. Use AI effectively and ethically
3. Make informed decisions about using AI technologies

• Bias

  Biases in generative AI refer to systematic and unfair tendencies in the outputs produced by AI models, often reflecting prejudices present in the training data or the algorithms themselves. A type of error that can occur in a large language model if its output is skewed by the model’s training data. 

• ChatGPT (Chat-based generative pre-trained transformer)

  A system built with a neural network transformer type of AI model that works well in natural language processing tasks (see definitions for neural networks and Natural Language Processing below). In this case, the model: (1) can generate responses to questions (Generative); (2) was trained in advance on a large amount of the written material available on the web (Pre-trained); (3) and can process sentences differently than other types of models (Transformer).

• Deep Learning

  Deep learning models are a subset of neural networks. With multiple hidden layers, deep learning algorithms are potentially able to recognize more subtle and complex patterns. Like neural networks, deep learning algorithms involve interconnected nodes where weights are adjusted, but as mentioned earlier there are more layers and more calculations that can make adjustments to the output to determine each decision. The decisions by deep learning models are often very difficult to interpret as there are so many hidden layers doing different calculations that are not easily translatable into English rules (or another human-readable language).

• Generative A.I.

  Technology that creates content — including text, images, video and computer code — by identifying patterns in large quantities of training data, and then creating original material that has similar characteristics. Examples include ChatGPT for text and DALL-E and Midjourney for images.

• Large language model

  A type of neural network that learns skills — including generating prose, conducting conversations and writing computer code — by analyzing vast amounts of text from across the internet. The basic function is to predict the next word in a sequence, but these models have surprised experts by learning new abilities.

• Machine Learning

  A program or system that trains a model from input data. The trained model can make useful predictions from new (never-before-seen) data drawn from the same distribution as the one used to train the model.

• Natural Language Processing (NLP)

  Natural Language Processing is a field of Linguistics and Computer Science that also overlaps with AI. NLP uses an understanding of the structure, grammar, and meaning in words to help computers “understand and comprehend” language. NLP requires a large corpus of text (usually half a million words). NLP technologies help in many situations that include: scanning texts to turn them into editable text (optical character recognition), speech to text, voice-based computer help systems, grammatical correction (like auto-correct or grammarly), summarizing texts, and others.

• Neural Networks (NN)

  Neural networks also called artificial neural networks (ANN) and are a subset of ML algorithms. They were inspired by the interconnections of neurons and synapses in a human brain. In a neural network, after data enter in the first layer, the data go through a hidden layer of nodes where calculations that adjust the strength of connections in the nodes are performed, and then go to an output layer.

• Transformer model

  A neural network architecture useful for understanding language that does not have to analyze words one at a time but can look at an entire sentence at once. This was an A.I. breakthrough, because it enabled models to understand context and long-term dependencies in language. Transformers use a technique called self-attention, which allows the model to focus on the particular words that are important in understanding the meaning of a sentence.

Follow this link (https://www.aiprm.com/ai-glossary/) to visit AIPRM's specialized Generative AI Glossary.