If an AI term has ever tripped you up or stopped you in your tracks, or if you’ve ever just wanted to learn more about something, this dictionary is for you! This running dictionary will serve as a resource for ECPs to gain insights on some of the most commonly-used AI lingo, and it will be consistently updated to reflect all of the latest AI innovations. To dive deeper into any of these definitions, check out our Real Talk podcast.
A
- Adaptive Curriculum: An educational framework powered by AI that personalizes the learning experience for each student based on their unique learning style, pace and existing knowledge gaps.
- Agentic AI: A type of AI that can act independently to achieve a goal, breaking down complex tasks into smaller steps and using various tools to complete them.
- AI Agent (or Knowledge Agent): An actionable AI system capable of interacting with websites, following workflows and completing tasks such as booking reservations or providing personalized tutoring.
- AI Scribe: An ambient AI tool that records and transcribes clinical conversations into electronic health records (EHRs), reducing the administrative burden of manual charting for healthcare providers.
- Algorithms: Mathematical programs or sets of rules used to teach computers how to process data, identify patterns and make predictions or decisions.
- Ambient AI: Technology that lives in the background of a clinical setting, such as a scribe, to capture and process data without requiring active, manual input from the provider.
- Anthropomorphic Avatar: A lifelike, digital representation of a person created using AI, often used for online profiles, social media, or as virtual tutors.
- API (Application Programming Interface): A set of rules and protocols that allows different software applications to communicate and share data with each other.
- Artificial Intelligence (AI): A broad field of technology that simulates human intelligence in machines, enabling them to analyze data and perform tasks with minimal human intervention.
- Artificial Neural Network (ANN): A processing system inspired by the human brain that consists of interconnected nodes (neurons) organized in layers to process information and learn from data.
- Augmented Reality (AR): Technology that overlays digital information, such as app notifications or navigation cues, onto a user’s view of the physical world, often through specialized eyewear.
- Autonomous Agent: An AI entity that can perceive its environment and take actions to achieve specific goals without constant human oversight.
- Autonomous Diagnostics: AI systems capable of analyzing diagnostic data, such as retinal images, to provide an interpretation or diagnosis without the immediate intervention of a doctor.
B
- Backpropagation: An older method of training neural networks where the correct answer is known beforehand, and the system works backward to adjust connection weights to achieve that result.
- Bias: Prejudicial errors in AI outputs that often stem from unrepresentative or skewed training data, potentially leading to unfair treatment of certain demographic groups.
- Black Box: A term describing the lack of transparency in how certain AI models reach their conclusions, making the internal decision-making process difficult for humans to see or understand.
C
- Chain of Thought: A feature in some advanced AI models that allows users to see the step-by-step reasoning process the AI uses to arrive at an answer.
- Conversational AI: AI systems designed to engage in human-like dialogue, increasingly capable of understanding context, emotion, and subtle linguistic cues.
D
- Data Liquidity: The ability of data to move easily across different systems and platforms, which is essential for training comprehensive AI models.
- Data Silo: An isolated repository of data that is not easily accessible by other systems or departments, hindering the development of integrated AI solutions.
- Deep Learning: A subset of machine learning that uses multi-layered artificial neural networks to recognize highly complex patterns in vast datasets, such as medical images.
- Digital Twin: A virtual AI-driven model of a patient—or a specific organ like the eye—used to simulate various treatment paths and predict potential health outcomes.
E
- Explainability: The degree to which a human can understand the reasoning and logic behind an AI model’s output or decision.
F
- Feed-Forward Neural Network (FNN): The simplest type of neural network, where information flows in one direction from the input layer to the output layer without feedback loops.
G
- Generative AI: A type of AI capable of creating new content, such as text, images or audio, by learning patterns from existing data.
H
- Hallucination: A phenomenon where an AI model confidently provides false or made-up information because it is predicting the next likely word rather than reasoning.
- Hebb’s Rule: A theory used in “forward learning” for AI, modeled after biological brain pathways where frequently used connections are strengthened.
- Home OCT: A remote diagnostic device that allows patients with conditions like wet macular degeneration to perform retinal scans at home, with AI analyzing the data to detect fluid changes.
L
- Large Language Model (LLM): An AI model trained on massive amounts of text data (books, articles, websites) to understand and generate human-like language.
M
- Machine Learning (ML): A core component of AI involving algorithms that improve their performance over time by learning from patterns in data without being explicitly programmed for every task.
- Multimodal AI: An AI system capable of analyzing various types of data simultaneously, such as clinical history combined with multiple forms of medical imaging.
N
- Natural Language Processing (NLP): A branch of AI focused on enabling computers to understand, interpret, and generate human language, often used for tasks like transcription or translation.
O
- Oculomics: The study of using the eye as a window into systemic health, where AI analyzes retinal images to identify biomarkers for conditions like heart or kidney disease.
P
- Phygital: A term describing the blending of physical and digital experiences, such as using AI-driven digital tools to enhance physical clinical examinations.
- Precision Learning: A personalized approach to education where AI identifies a student’s specific knowledge gaps and tailors the curriculum to fill them.
- Precision Medicine: A medical model that uses AI to tailor treatments to individual patients based on their specific genetic makeup, lifestyle, and clinical data.
- Prompt Engineering: The process of carefully framing and structuring questions or instructions to an AI model to obtain the most precise and actionable results.
R
- Recurrent Neural Network (RNN): A type of neural network with feedback loops that allow it to process sequences of data where order and timing are important, such as speech or language.
- Reinforcement Learning: A type of machine learning where algorithms learn through trial and error, receiving “rewards” for correct actions and “penalties” for incorrect ones.
- Remote Patient Monitoring: The use of digital technologies to collect medical and other health data from individuals in one location and electronically transmit that information securely to healthcare providers in a different location.
- Revenue Cycle Management (RCM): The financial process in healthcare that uses AI agents to track patient care episodes from registration and appointment scheduling to the final payment of a balance.
S
- Small Language Model (SLM): A scaled-down version of an LLM optimized to run on less powerful hardware, like phones or laptops, often used for specific tasks like summarization or simple Q&A.
- Supervised Learning: A type of machine learning where an algorithm is trained on labeled data to make predictions that are then verified by a human supervisor.
U
- Unsupervised Learning: A type of machine learning where an algorithm sorts through unlabeled data to discover hidden patterns and structures on its own.
V
- Vertical Integration: A business strategy where a company owns multiple stages of the supply chain; in eyecare, this may involve a single entity owning everything from the technology and diagnostics to the surgical centers and insurance.
- Visual Language Model (VLM): An advanced AI model that can understand and process both text and visual information, allowing it to “see” and describe images or videos.
- Voice Agent: An AI-powered assistant that can engage in natural, two-way voice conversations, often used for patient scheduling or answering healthcare queries.
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