Publication | Open Access
FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare
205
Citations
106
References
2025
Year
Artificial IntelligenceHealthcare Monitoring SystemsEngineeringAi SafetyIntelligent SystemsBiomedical Artificial IntelligenceAi ReliabilityResponsible AiData ScienceMedical Expert SystemDigital HealthClinical ApplicationAi HealthcarePublic HealthEthic Of Artificial IntelligenceTrustworthy Artificial IntelligenceHealth PolicyHealthcare PracticesComputer ScienceArtificial Intelligence EthicsTrust In Artificial IntelligenceTrustworthy AiMedical EthicsDeployable Artificial IntelligenceClinical PracticeInternational Consensus GuidelineHealth Informatics
Despite advances in AI for healthcare, its deployment in clinical practice remains limited. The paper introduces the FUTURE‑AI framework to guide the development and deployment of trustworthy AI tools in healthcare. The framework was built by a 117‑member, 50‑country consortium over two years, establishing six guiding principles and 30 best practices that span the entire AI lifecycle from design to monitoring.
Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. This paper describes the FUTURE-AI framework, which provides guidance for the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI Consortium was founded in 2021 and comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two year period, the FUTURE-AI guideline was established through consensus based on six guiding principles—fairness, universality, traceability, usability, robustness, and explainability. To operationalise trustworthy AI in healthcare, a set of 30 best practices were defined, addressing technical, clinical, socioethical, and legal dimensions. The recommendations cover the entire lifecycle of healthcare AI, from design, development, and validation to regulation, deployment, and monitoring.
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