Last edited on January 10, 2024


AI (Artificial Intelligence). AI involves the replication of human-like intelligence processes using computer systems. This encompasses tasks such as learning, reasoning, problem-solving, and making decisions in a way that mimics human cognitive abilities.

Algorithm. An algorithm is a structured set of instructions used by artificial intelligence systems to perform specific tasks or solve complex problems. It's like a well-defined procedure that guides a computer on how to execute a task step by step.

AI Ethics. AI ethics pertains to the ethical considerations and responsibilities that AI stakeholders such as developers, engineers, and policymakers must address to ensure the responsible development and use of AI technology. This involves establishing systems and principles to promote safety, fairness, and environmental sustainability in AI applications.


Bias (in AI). A systemic prejudice built into algorithms or data that can lead to unfair or discriminatory outcomes.

Big data. Extremely large and complex datasets that are difficult to process with traditional methods.

Biomedical Informatics. The application of information technology and computational methods to solve problems in medicine and healthcare.

BioNLP (Biomedical Natural Language Processing). A subfield of natural language processing specializing in processing biomedical text data.


ChatGPT. ChatGPT is a chatbot developed by OpenAI that gained considerable attention for its ability to generate human-like text responses. It relies on sophisticated algorithms to produce natural language responses to user input.

Chatbot. A chatbot is an artificial intelligence-powered conversational tool designed to engage with users in a human-like manner. Generative AI chatbots like ChatGPT can serve as alternatives to traditional search engines for information retrieval.

Cognitive computing. A subfield of AI that focuses on creating intelligent systems that can learn, reason, and solve problems.

Computational Biology. The application of computer science and computational methods to study biological systems.

Corpus. A corpus is a substantial dataset comprising written or spoken language that serves as the foundation for training language models. It is instrumental in enabling computers to understand and generate human language effectively.


Data mining. Data mining is the process of systematically extracting valuable information and identifying patterns from large and complex datasets. It can uncover insights, such as entity names, within the data provided.

Data validation. Data validation involves the critical assessment of data quality and accuracy before utilizing it to develop and train AI models. Ensuring data integrity is a crucial step in the AI development process.

Dall-E. Dall-E is an advanced image generation AI tool developed by OpenAI. It accepts textual prompts and produces corresponding images, demonstrating the capability of AI to bridge the gap between text and visual content.

Deepfake. A deepfake is a convincing AI-generated multimedia hoax, including images, audio, and videos. They can fabricate entirely new content, including fabricated actions or statements by individuals, and are also employed in generating fake news events.

Deep learning. Deep learning is a subset of machine learning that emulates the way humans acquire certain types of knowledge by using neural networks with multiple layers. It excels at tasks that involve pattern recognition and complex data representations.


Explainable AI (XAI). A set of techniques that aim to make AI models more transparent and interpretable for humans.


Fuzzy logic. A system of logic that allows for degrees of truth, rather than just true or false.


Generative AI. Generative AI is a class of artificial intelligence that creates content by learning patterns from large datasets and generating new material with characteristics similar to the learned data. This technology is used in various creative applications.

Generative pre-trained transformer (GPT). GPTs are a type of large language model and framework for generative AI developed by OpenAI. They are adept at generating human-like text and have been influential in natural language processing.

Guardrails. Guardrails represent policies and constraints imposed on AI models to ensure responsible data handling and prevent the generation of inappropriate or harmful content.


Human-in-the-loop (HITL). A system where humans and machines work together to make decisions.


Intelligent tutoring systems. Computer programs that provide personalized instruction and feedback to students.

Internet of medical things (IoMT). The network of medical devices and applications that collect and share health data.


Large language model (LLM). LLMs are sophisticated machine learning algorithms capable of understanding, summarizing, generating, and predicting textual content. They typically contain a vast number of parameters and are trained on extensive unlabeled text data.


Machine learning. Machine learning is a subset of AI that enables software applications to improve their predictive accuracy by autonomously learning from data. It encompasses a wide range of algorithms and techniques.

Medical imaging. The use of imaging technologies to diagnose and treat medical conditions.

Metadata. Metadata is supplementary data that provides information about other data. It serves to describe, organize, or categorize primary data, making it more manageable and interpretable.

Model. An AI model is a machine learning algorithm that has undergone training and can perform specific tasks or make informed decisions based on the knowledge it has acquired.

Multimodal language model (MLM). Unlike traditional large language models that are trained solely on text, a multimodal language model is trained on both textual and non-textual data, allowing it to generate responses across various input modalities, including text, images, audio, and video.


Natural language processing (NLP). A subfield of AI that focuses on understanding and generating human language.

Neural networks. A type of computer model inspired by the structure and function of the human brain.


Parameter. Parameters in AI refer to internal settings learned by machine learning models during the training process. These settings enhance the model's ability to recognize patterns and make informed decisions.

Precision medicine. A medical approach that tailors treatment to individual patients based on their genetic, molecular, and environmental factors.

Predictive analytics. The use of data to predict future outcomes.

Predictive artificial intelligence. Predictive AI leverages machine learning to analyze historical data and identify patterns and trends, enabling it to make forecasts about future behavior and events.

Prompt. A prompt is a user-provided input, often in the form of words or keywords, that instructs an artificial intelligence system on the expected response or action.


Reinforcement learning. A subfield of AI that focuses on training agents to make decisions in an environment to maximize a reward.

Responsible AI. Responsible AI emphasizes the ethical and trustworthy development, implementation, and use of AI systems, with a focus on positively impacting society while avoiding harmful consequences and fostering trust among users.

Robotics. A field of engineering that deals with the design, construction, and operation of robots.


Speech recognition. Speech recognition is the technology that converts spoken language into text using AI algorithms, enabling computers to understand and transcribe human speech.

Supervised learning. A type of machine learning where the algorithm is trained on data with labeled examples.

Support vector machines (SVM). A type of algorithm that is often used for classification tasks.


Training data. Training data refers to the information or examples used to train machine learning models. These data sets are essential for teaching AI systems to recognize patterns and make informed decisions.

Token. A token is the basic unit of language used by large language models to understand and generate text. Tokens can represent whole words or smaller linguistic elements, such as prefixes or suffixes.


Unsupervised learning. A type of machine learning where the algorithm is trained on unlabeled data.


Virtual reality (VR). A computer-generated simulation of a three-dimensional environment that can be interacted with in a seemingly real or physical way.