| AI (Ambient) Scribe | Core Concept | Listens to sessions and generates a draft clinical note (e.g., SOAP/DAP). Requires clinician verification, vendor diligence (BAA), and clear client disclosure. | A tool that listens to a therapy session and writes a *draft* note for the therapist to review and fix. |
| AI Notetaker | Core Concept | General-purpose meeting summary tools. Often not built for clinical privacy/compliance. Therapists should distinguish consumer vs clinical-grade tools. | A tool that records and summarizes conversations—usually for meetings, not therapy. |
| Agentic AI | Core Concept | Systems that take actions toward goals (e.g., scheduling, sending messages). Raises consent, safety, and accountability concerns when “acting” rather than “suggesting.” | An AI that doesn’t just answer—it can take steps to complete a goal for you. |
| Algorithm | Core Concept | Foundational to how software makes decisions; helps therapists understand that AI outputs come from programmed or learned rules, not intuition. | A step-by-step recipe a computer follows to do something. |
| API (Application Programming Interface) | Core Concept | Explains how systems connect (e.g., EHR ↔ billing ↔ transcription). Critical for understanding where client data flows and where it can be exposed. | A controlled way for one app to ask another app for information—like a waiter taking an order. |
| Artificial Intelligence (AI) | Core Concept | Umbrella term for systems that perform tasks associated with human cognition (pattern recognition, prediction, language). Important for client psychoeducation and boundaries. | Computer tools that can learn patterns and do tasks that seem like “thinking.” |
| Data | Core Concept | AI quality depends on data quality and fit-for-purpose. Clinicians should assess what data is collected, shared, stored, and how it is protected. | Information (words, numbers, pictures) that computers use to learn or make decisions. |
| Digital Phenotyping | Core Concept | Passive behavior signals from devices (sleep/activity/typing) used to infer mental state. Needs careful interpretation, bias awareness, and transparent consent. | Apps using phone signals (like movement or sleep) to guess how someone might be doing. |
| Generative AI | Core Concept | Produces new text/images/audio. Relevant for homework support, summaries, and drafting—while managing hallucinations, privacy, and overreliance. | AI that can create new writing or images that didn’t exist before. |
| Machine Learning (ML) | Core Concept | Subset of AI where systems learn from examples. Helps clinicians frame AI as statistical learning, not “understanding.” | A way computers learn from many examples instead of being told every rule. |
| Training Data | Core Concept | The dataset used to train a model/tool. Clinicians should ask what it includes/excludes and whether it matches their client populations and use case. | The specific information used to teach the AI how to do its job. |
| Adversarial Attacks | Ethics / Safety | AI can be tricked by intentional inputs. Relevant for vendor security, data integrity, and risk assessment for clinical tools. | A sneaky way to trick an AI into doing the wrong thing. |
| Bias | Ethics / Safety | Skewed outputs from unrepresentative data or design choices. Important for equity, cultural humility, and avoiding harm in AI-assisted recommendations. | When the AI is unfair because what it learned from wasn’t balanced. |
| Explainability (XAI) | Ethics / Safety | Methods that provide partial, testable reasons for outputs (not full “mind-reading”). Supports accountability and safer clinical use. | Ways to get the AI to show *clues* about why it answered the way it did, so we can check it. |
| Privacy | Ethics / Safety | Core legal/ethical issue: what data is shared with third parties, how it is protected, and whether proper agreements/consents exist. | Keeping personal information safe and not shared without permission. |
| Regulation / Oversight | Ethics / Safety | Laws, professional guidance, and organizational policies governing AI use in healthcare and documentation workflows. | Rules and monitoring to make sure AI is used safely and fairly. |
| Sentience | Ethics / Safety | Helpful for boundary-setting: current AI can sound empathic without experiencing feelings or selfhood. | Truly feeling and being aware like a person—current AI does not have this. |
| Sentience Halo | Ethics / Safety | Risk that users infer consciousness and trustworthiness from fluent language. Can drive overreliance or attachment. | When an AI sounds so human that people start to think it truly understands or feels. |
| Chatbot | LLM-Specific | Interface clients commonly use. Requires guidance on limits, verification, and appropriate use cases. | A computer program you can talk with—usually by typing. |
| Fine-Tuning | LLM-Specific | Adapts a general model to a specific domain (e.g., mental health). Useful for evaluating whether a tool is specialized and how it was trained. | Extra training that makes a general AI better at one specific job. |
| Hallucination | LLM-Specific | Confident but incorrect output. Clinicians should assume outputs may be wrong and require verification—especially for citations, diagnoses, and policy. | When the AI makes up facts that sound real but aren’t true. |
| Large Language Model (LLM) | LLM-Specific | Underlying technology for many chatbots. Important to explain that models generate text by prediction, not by guaranteed factual recall or real-time access. | An AI trained on huge amounts of writing that predicts the next words to create answers. |
| Reinforcement Learning from Human Feedback (RLHF) | LLM-Specific | Technique shaping helpful/safe behavior and “empathetic-sounding” responses. Not evidence of real emotion; supports psychoeducation about boundaries. | Humans rate the AI’s answers, and the AI learns to give the kinds of answers people rate as helpful and safe. |
| Business Associate Agreement (BAA) | Operations / Compliance | Key vendor requirement when a tool handles protected health information (PHI). Helps determine whether a service is appropriate for clinical use. | A legal agreement that says a company must protect health information if they handle it for you. |
| De-identification vs Anonymization | Operations / Compliance | Clinicians often confuse these. De-identified data may still carry re-identification risk; policies should define what is removed and what remains. | Removing identifying details is not always the same as making it impossible to identify someone. |
| Minimum Necessary / Data Minimization | Operations / Compliance | Limits what information is disclosed to tools/vendors. Reduces exposure if systems are breached or misused. | Only share the smallest amount of information needed to do the job. |
| Retention & Deletion | Operations / Compliance | Where data is stored, for how long, and how it can be deleted. Essential for consent, policy, and vendor review. | How long the tool keeps the information and whether you can delete it. |
| Human-in-the-Loop Review | Operations / Compliance | Clinician remains responsible for accuracy and clinical judgment. A safeguard against hallucinations and inappropriate summaries. | A human checks and approves what the AI produces before it’s used. |
| Audit Logs & Access Controls | Operations / Compliance | Tracks who accessed data and when; limits access to authorized users. Core components of security governance. | Records of who looked at information and settings that limit who can see it. |