FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare

2025/02/05 by Karim Lekadir, Alejandro F. Frangi, Antonio R. Porras +46 · 1 voice · 3 citations
Medicine · Health Professions · #Artificial Intelligence in Healthcare and Education #Ethics in Clinical Research #Artificial Intelligence in Healthcare

paper · pdf · doi:10.1136/bmj-2024-081554

openalex publication_date 2025/02/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/03

Abstract

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.

Citations

• Model Cards for Model Reporting
• Practical Guidance for Involving Stakeholders in Health Research
• Artificial intelligence, bias and clinical safety
• Adversarial attacks on medical machine learning
• A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI
• The future of digital health with federated learning
• Explainable AI: A Review of Machine Learning Interpretability Methods
• A Transfer Learning Approach for Early Diagnosis of Alzheimer’s Disease on MRI Images
• The importance of being external. methodological insights for the external validation of machine learning models in medicine
• The medical algorithmic audit
• Low‐field MRI: Clinical promise and challenges
• From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI
• Data drift in medical machine learning: implications and potential remedies
• Privacy-preserving artificial intelligence in healthcare: Techniques and applications
• TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods

Cited by

• Generalist Models in Medical Image Segmentation: A Survey and Performance Comparison with Task-Specific Approaches
• PiPViT: Patch-based Visual Interpretable Prototypes for Retinal Image Analysis
• WISCA: A Consensus-Based Approach to Harmonizing Interpretability in Tabular Datasets

Discussions

• Lekadir et al. (2025). #Future #AI: Intern. consensus guideline for #trustworthy and deployable AI in #healthcare. BMJ, 388, e081554. doi.org/10.1136/bmj-... #explainability #fa [bsky, 0 points, 0 comments]