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This section contains documents produced by the CAIDX project as well as a curated list of external resources that has been assembled during the development phase of the CAIDX project.

Documents produced by CAIDX

IP Rights for SMEs Developing AI-Based Clinical Diagnostic Methods and Tools

This guide provides an overview of best practices and guidelines for exercising due diligence regarding third party patents, as well as strategies for safeguarding your own inventions.

Open guide

External Tools and References

The resources featured here are designed to complement the Clinical AI Pathway Guide. Each resource offers the opportunity to explore in greater depth the specific tasks and challenges identified in the Clinical AI Pathway, such as needs identification, evaluation, risk assessment, data security, regulatory compliance, and implementation strategies.

Go to category: Business | Ethics | AI Literacy | Educational Resource | Data Protection | Trustworthy AI | AI Development | Evaluation | HTA | Regulatory | Implementation | IT Infrastructure | AI Risk Management | Legal | Glossary |

Category: Business

• Business MakeOver: Platform for different business model tools.
• AI Canvas: Strategic toolkit for AI project planning and deployment.
• Business Model Canvas: Tool for developing and evolving business models.
• AI Project Canvas: Planning tool for AI project development.

Category: Ethics

• Building STANdards for data Diversity, INclusivity, & Generalisablity: Checklist for promoting data diversity and inclusivity.
• Z-Inspection: A Process to Assess Trustworthy AI: Assesses AI’s trustworthiness with a structured framework.
• Ethics and governance of artificial intelligence for health: Ethics and governance of artificial intelligence for health.

Category: AI Literacy

• Patient and public involvement to build trust in artificial intelligence: a framework, tools and case studies: Resources for involving patients/public in AI understanding.
• Conceptual framework for a holistic AI literacy assessment matrix: Framework for assessing AI literacy and ethics.

Category: Educational Resource

• FDA Digital Health and Artificial Intelligence Glossary – Educational Resource: Glossary of AI/digital health terms and definitions.

Category: Data Protection

• Asking the right questions before using an artificial intelligence system: Guide on questions for ethical AI use and data protection.
• Self-assessment guide for artificial intelligence (AI) systems: Self-assessment for AI system maturity and GDPR compliance.

Category: Trustworthy AI

• FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare: Framework for developing trustworthy AI healthcare tools.

Category: AI Development

• Technology readiness levels for machine learning systems: Introduces TRL cards to assess ML system readiness.
• Good Machine Learning Practice for Medical Device Development: Guiding Principles: Principles for ML medical device development.
• Guideline for high-quality diagnostic and prognostic applications of AI in healthcare: Dutch guideline for diagnostic and prognostic AI applications.
• AI software lifecycle specification: AI for health (AI4H) software life cycle.
• Kaapana: An open-source toolkit for building medical imaging platforms.
• Medical Open Network for Artificial Intelligence: Collaborative initiative that brings together experts to advance medical imaging AI.
• EvalML: EvalML is an AutoML library that builds, optimizes, and evaluates machine learning pipelines.
• Innovation Funnel for VAluable AI in Healthcare: Guideline for the development of valuable AI in healthcare.

Category: Evaluation

• To buy or not to buy—evaluating commercial AI solutions in radiology (the ECLAIR guidelines): Guidelines to evaluate commercial AI in radiology.
• Clinician checklist for assessing suitability of machine learning applications in healthcare: Checklist for clinicians evaluating ML applications in practice.
• Catalogue of Tools & Metrics for Trustworthy AI: Catalogue of tools for ensuring AI trustworthiness.
• Assessment of Radiology Artificial Intelligence Software: A Validation and Evaluation Framework: Framework for radiology AI evaluation, ensuring safety and relevance.
• Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement from the ACR, CAR, ESR, RANZCR and RSNA: Practical considerations for AI tools in radiology.
• Clinical Evaluation Plan: German guidelines for clinical evaluation of AI in medicine.
• AI for IMPACTS Framework for Evaluating the Long-Term Real-World Impacts of AI-Powered Clinician Tools: Framework for assessing the impact of AI tools in health care.
• APPRAISE-AI Tool for Quantitative Evaluation of AI Studies for Clinical Decision Support: Tool to evaluate the quality of AI prediction models for clinical decision support.
• The METRIC-framework for assessing data quality for trustworthy AI in medicine: Framework for assessing data quality for trustworthy AI in medicine.
• Clinical evaluation of AI for health: Considerations on clinical evaluation of artificial intelligence (AI) for health.
• AI for health evaluation consideration: Considerations on the testing, validation, benchmarking and evaluation of AI in health.

Category: HTA

• DigiHTA: Finnish framework for assessing digital health service suitability.
• Finnish Digi-HTA Assessment Model for Digital Health and an International Comparison: Conference paper 2024.
• Comparison report of DigiHTA and DiGAV ordinance: Report from 2023.
• Model for Assessing the value of Artificial Intelligence in medical imaging (MAS-AI): HTA model for AI value assessment in medical imaging.

Category: Regulatory

• COMPL-AI: Framework for evaluating generative AI model compliance.
• TÜV AI LAB Assessment Matrix: Matrix for organizing AI test resources and evaluations.
• The Data Protection Toolkit: Toolkit for GDPR compliance in small enterprises.
• OpenRegulatory Templates: Templates supporting compliance with various standards.
• Questionnaire „Artificial Intelligence (AI) in medical devices“: Questionnaire for AI in medical devices; regulatory focus.
• Guideline for AI for medical products: Guideline on safe AI methods in medical devices.
• List of Notified Bodies (NANDO): Database of EU-certified notified bodies for medical products.
• Regulatory considerations on artificial intelligence for health: Discussion of key regulatory concepts to be considered by all relevant stakeholders.
• Global AI Regulation Tracker: An interactive world map that tracks AI law, regulatory and policy developments around the world.

Category: Implementation

• Clinical AI Sociotechnical Framework (CASoF): Guides AI system design and implementation in clinical settings.
• A Development Framework for Trustworthy Artificial Intelligence in Health with Example Code Pipelines: Framework focusing on trustworthy AI in healthcare.
• The Clinical Practice Integration of Artificial Intelligence (CPI-AI) framework: a proposed application of IDEAL principles to artificial intelligence applications in trauma and orthopaedics: Integrates AI in trauma/ortho. clinical practice using IDEAL principles.
• From Bit to Bedside: A Practical Framework for Artificial Intelligence Product Development in Healthcare: Framework for developing AI healthcare products.

Category: IT Infrastructure

• Secure Machine Learning Architecture (SEMLA): Infrastructure framework for secure AI data handling.

Category: AI Risk Management

• Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile
• AI Act Risk Navigator: Navigates EU AI Act laws for compliance understanding.

Category: Legal

• The AI Act Explorer: Tool for navigating and understanding EU AI Act text.

Category: Glossary

• Common unified terms in artificial intelligence for health: Glossary with agreed terminology in artificial intelligence (AI) for health.
• Clinical AI Pathway Glossary: This glossary compiles keywords from the Clinical AI Pathway Guide.