Clinical AI Pathway Glossary

This section compiles keywords from the Clinical AI Pathway Guide into a compact glossary.

Algorithm development plan: Algorithm development is the process of exploring different approaches to solve problems by defining a computational process that satisfies system specifications.

Analytical performance: The ability of a device to correctly detect or measure a particular analyte.

Application Programming Interface (API): API is a set of protocols and tools that allow different software applications to communicate and exchange data, features and functionality.

Certificate of Free Sale: A Certificate of Free Sale (CFS) is an official document that confirms that a medical device is legally marketed and freely sold.

Change management: A structured process aligning technical integration with human factors to boost readiness, adapt workflows, and build trust for safe, effective AI adoption.

Clinical performance: Clinical performance in AI refers to how accurately, reliably, and safely an AI system supports clinical tasks, decisions, or outcomes in real-world settings.

Conformity assessment: Medical conformity assessment in AI is the process of verifying that an AI-based medical device meets regulatory, safety, and performance standards before market entry.

Cost-effectiveness Analysis: CEA measures how much benefit is gained per euro spent, using outcomes like life expectancy or emissions reduced. It compares interventions or implementation methods.

Data anonymization: Data anonymization is the process of irreversibly altering data so individuals cannot be identified, directly or indirectly, even when combined with other information.

Data curation: A managed process where data is cleaned, documented, standardized, and linked across its lifecycle, including versioning, merging, and metadata annotation.

Data dictionary: A centralized repository defining data attributes, formats, and relationships of data elements in a database to ensure consistency and effective data management.

Data lakes: A repository storing structured and unstructured data at any scale, enabling analytics, real-time processing, and machine learning without prior data structuring.

Data provenance chain: Data provenance chain is the documented history of data, tracking its origin, transformations, and movements to ensure accuracy, trust, and accountability.

Data traceability: Data traceability tracks the full data lifecycle, ensuring transparency, reproducibility, and accountability by documenting data origin, changes, and impact.

Design verification: Design verification ensures a product’s design meets requirements and functions as intended before implementation.

End-user: An end-user is the individual or group, such as healthcare professionals, researchers, or patients, who directly interacts with or benefits from the AI solution.

EU AI Act: The AI Act is the first-ever comprehensive legal framework on AI worldwide. The aim of the rules is to foster trustworthy AI in Europe.

EU Declaration on conformity (DoC): DoC is a mandatory document where manufacturers declare their products comply with EU laws and take full responsibility for compliance.

European Economic Area (EEA): The EEA groups together the 27 EU Member States and 3 European Free Trade Association countries in a single market subject to the same rules.

General Data Protection Regulation (GDPR): GDPR is an EU regulation strengthening and harmonising EU/EEA procedures concerning the collection, storage, processing, access, use, transfer and erasure of personal data.

GDPR Risk assessment: GDPR risk assessment identifies and evaluates risks to personal data, ensuring compliance and applying measures to protect data privacy and security.

General Safety and Performance Requirements (GSPRs): GSPRs, in MDR and IVDR Annex I, set essential safety and performance standards for medical and diagnostic devices.

Gold-standard methods: Gold-standard methods are the most reliable and widely accepted procedures used to ensure accuracy and validate new approaches.

Governance Structure: A governance structure defines roles, policies, and processes to guide AI development and oversight, ensuring accountability, ethics, risk management, and compliance.

In Vitro Diagnostics Regulation (IVDR): The IVDR (EU 2017/746) regulates the safety and performance of in vitro diagnostic devices to improve patient safety and device reliability in the EU.

Intellectual Property Rights (IPR): IPR are legal protections granted to creators and owners of intellectual property, enabling them to control, use, and benefit from their inventions, designs, or artistic works.

ISO standards: ISO standards are international guidelines ensuring quality, safety, efficiency, and interoperability across industries.

Key assay parameters: Key assay parameters in clinical validation—like sensitivity, specificity, and accuracy—that demonstrates how reliably a test detects or measures a clinical condition.

Key opinion leaders (KOLs): KOLs are trusted experts in healthcare whose insights guide AI development, support validation, and influence clinical adoption in real-world settings.

Market position: Market position refers to how the consumers perceive a brand or product versus competitors, based on factors like quality, price, and benefits.

Market size analysis: Market size analysis is an analysis of the total number of potential clients or buyers in a particular market segment.

Medical Device Regulation (MDR): The Medical Device Regulation (MDR) (EU) 2017/745 is a regulation by the European Union that governs the safety and performance of medical devices.

Minimum Viable Product (MVP): MVP is a version of a product with just enough features to be usable by early customers who can then provide feedback for future product development.

Open-source: Open-source refers to software or projects whose source code is freely available for anyone to view, use, modify, and distribute, typically under a permissive license.

Person Responsible for Regulatory Compliances (PPRC): A PRRC ensures regulatory compliance in manufacturing and post-market activities. MDR requires manufacturers and representatives to appoint a qualified person for this role.

Post-market surveillance: Post-market surveillance monitors a medical device’s safety and performance after release to ensure compliance and detect risks.

Product life-cycle management (PLM): PLM manages a product’s lifecycle from inception to disposal, integrating people, data, processes, and systems to support companies and partners.

Proof-of-principle: Proof-of-principle is a demonstration that a concept or technology is feasible and works as intended in a preliminary or experimental setting.

PubMed: PubMed is a free database with 38M+ biomedical citations and abstracts, aiding health research. It links to full texts when available from other sources.

Quality Management System (QMS): QMS is a set of tailored processes and policies that standardize and improve quality to boost customer satisfaction across business operations.

Quality model: Quality models provides a framework to set data quality requirements, define measures, and guide evaluations, emphasizing ongoing management beyond development.

Research & Development (R&D): R&D is the process of exploring new knowledge and creating innovative products, services, or technologies through systematic investigation and experimentation.

Retrospective validation: Retrospective validation tests an AI solution’s performance on historical data to verify accuracy and reliability before real-world deployment.

Segmentation, Targeting and Positioning (STP): STP is a marketing process that divides a market into segments, selects key segments to focus on, and positions products to appeal to those segments.

Shelf life: Shelf life of an AI solution is the time it remains safe, effective, and compliant, including physical durability and model accuracy.

Standard Operating Procedures (SOP): SOPs are detailed, written guidelines that outline consistent, standardized processes for performing tasks in an organization.

SWOT-analysis: SWOT analysis evaluates Strengths, Weaknesses, Opportunities, and Threats by assessing internal and external factors to maximize positives and minimize negatives.

Tech Transfer Office (TTO): A TTO facilitates commercialization of research through licensing, patents, partnerships, and spin-offs to maximize intellectual property use.

Testing chain: A testing chain is the sequence of steps used to evaluate a product or system, ensuring it meets standards and functions correctly from initial tests to final validation.

UX designer: A UX designer enhances user satisfaction by researching, prototyping, and testing to create intuitive, user-centered digital experience.

Value drivers: Value drivers are key factors that impact an AI solution’s success, like cost-effectiveness, outcomes, efficiency, innovation, compliance, and user adoption.

Verification plan: A verification plan outlines the required level of accuracy and the process to ensure the algorithm meets specified performance standards.