FUTURE-AI
FUTURE-AI is a global guideline that helps ensure artificial intelligence (AI) tools used in healthcare are trustworthy and can be safely put into real-world practice. It was created by a large group of international experts and focuses on fairness, usability, transparency, and other key principles to guide developers and healthcare teams.
At a glance
Use when
Developing, evaluating, or deploying AI tools in healthcare settings where trustworthiness, safety, equity, and regulatory compliance are critical.
Avoid when
When seeking a technical validation framework only, or when a rapid, lightweight checklist without comprehensive lifecycle coverage is preferred.
Inputs
Requirements and context across the AI development lifecycle, including technical specifications, clinical needs, ethical considerations, and regulatory standards.
Outputs
A structured set of 30 best practices and six core principles to guide the trustworthy development, validation, regulation, deployment, and monitoring of AI in healthcare.
How it works
FUTURE-AI is an international consensus guideline developed by the FUTURE-AI Consortium, comprising 117 interdisciplinary experts from 50 countries. It establishes six guiding principles—fairness, universality, traceability, usability, robustness, and explainability—and defines 30 best practices to operationalize trustworthy AI in healthcare. The framework covers the entire AI lifecycle, including design, development, validation, regulation, deployment, and monitoring, with considerations across technical, clinical, socioethical, and legal domains.
- HTA domains
- Clinical Effectiveness, Safety, Organisational aspects
- Assumptions
- AI systems in healthcare must be developed with interdisciplinary input and global applicability; trustworthiness requires proactive attention to ethical, legal, and socio-technical factors throughout the AI lifecycle.
- Strengths
- Developed through international, interdisciplinary consensus involving experts from all continents,Covers the full lifecycle of AI in healthcare,Integrates technical, clinical, ethical, legal, and organizational perspectives,Provides actionable best practices for real-world deployment
- Limitations
- Implementation may vary across healthcare systems due to differing regulatory and infrastructural contexts,Does not provide detailed technical algorithms or software tools for enforcement,Relies on stakeholder commitment for adherence
- Also known as
- FUTURE-AI Guideline, FUTURE-AI Framework
Questions this answers
- › How can AI in healthcare be developed and deployed in a trustworthy manner?
- › What principles ensure fairness and equity in healthcare AI systems?
- › How can the transparency and explainability of AI tools be ensured for clinical use?
- › What best practices support the robustness and safety of AI in real-world settings?
- › How should healthcare AI be monitored post-deployment?
- › What ethical and legal considerations must be addressed during AI development?
References & sources
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