mlsanityData to 14 Oct 2026

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

arXiv 2309.12325 ↗First posted 11 Aug 2023cs.CY cs.AI cs.CVcited by 60

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Abstract (arXiv)

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI.

Key facts. Cited by 60 papers in the map (data through 14 Oct 2026). Builds on 31 of its 225 references that are in the map. First posted on arXiv 11 Aug 2023.

Builds on

31 of 225 references.

References · 31

  1. 2018Datasheets for Datasetscited by 1,386
  2. 2018Model Cards for Model Reportingcited by 1,080
  3. 2020The Future of Digital Health with Federated Learningcited by 633
  4. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 511
  5. 2017Interpretation of Neural Networks is Fragilecited by 444
  6. 2017On Fairness and Calibrationcited by 406
  7. 2019A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAIcited by 404
  8. 2018AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Biascited by 319
  9. 2022From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AIcited by 302
  10. 2022Metrics reloaded: Recommendations for image analysis validationcited by 195
  11. 2020Fairness in Machine Learningcited by 194
  12. 2022Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyondcited by 171
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  2. 2020Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAIcited by 126
  3. 2022Explanations Can Reduce Overreliance on AI Systems During Decision-Makingcited by 111
  4. 2020Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentationcited by 96
  5. 2020Secure and Robust Machine Learning for Healthcare: A Surveycited by 80
  6. 2022Leakage and the Reproducibility Crisis in ML-based Sciencecited by 65
  7. 2020GANterfactual - Counterfactual Explanations for Medical Non-Experts using Generative Adversarial Learningcited by 50
  8. 2020Technology Readiness Levels for AI & MLcited by 30
  9. 2020How Good is your Explanation? Algorithmic Stability Measures to Assess the Quality of Explanations for Deep Neural Networkscited by 27
  10. 2021Estimating and Improving Fairness with Adversarial Learningcited by 26
  11. 2022Explainable Artificial Intelligence (XAI) from a user perspective- A synthesis of prior literature and problematizing avenues for future researchcited by 20
  12. 2021Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Diseasecited by 19
  13. 2022Data Harmonisation for Information Fusion in Digital Healthcare: A State-of-the-Art Systematic Review, Meta-Analysis and Future Research Directionscited by 19
  14. 2022Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysiscited by 18
  15. 2021An Objective Metric for Explainable AI: How and Why to Estimate the Degree of Explainabilitycited by 16
  16. 2022Improving the repeatability of deep learning models with Monte Carlo dropoutcited by 11
  17. 2022High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detectioncited by 11
  18. 2021EUCA: the End-User-Centered Explainable AI Frameworkcited by 10
  19. 2022Domain generalization in deep learning-based mass detection in mammography: A large-scale multi-center studycited by 10

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 6; 2025: 18; 2026: 34.

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  1. 2026From Performance to Practice: Knowledge-Distilled Segmentator for On-Premises Clinical Workflowscited by 7
  2. 2023MRxaI: Black-Box Explainability for Image Classifiers in a Medical Settingcited by 6
  3. 2024Copycats: the many lives of a publicly available medical imaging datasetcited by 6
  4. 2025Advances in Automated Fetal Brain MRI Segmentation and Biometry: Insights from the FeTA 2024 Challengecited by 5
  5. 2026Medical world models in healthcare: foundations, applications, and challenges for trustworthy clinical translationcited by 4
  6. 2024Denoising Diffusion Models for Anomaly Localization in Medical Imagescited by 3
  7. 2025Fair Foundation Models for Medical Image Analysis: Challenges and Perspectivescited by 3
  8. 2025Federated nnU-Net for Privacy-Preserving Medical Image Segmentationcited by 3
  9. 2025Towards deployment-centric multimodal AI beyond vision and languagecited by 3
  10. 2025Generalist Models in Medical Image Segmentation: A Survey and Performance Comparison with Task-Specific Approachescited by 2
  11. 2025Critical Appraisal of Fairness Metrics in Clinical Predictive AIcited by 2
  12. 2025Debugging Concept Bottleneck Models through Removal and Retrainingcited by 2
38 more
  1. 2025Assessing the Reliability of Large Language Models in the Bengali Legal Context: A Comparative Evaluation Using LLM-as-Judge and Legal Expertscited by 2
  2. 2025Foundation Models in Biomedical Imaging: Turning Hype into Realitycited by 2
  3. 2025Auditing Significance, Metric Choice, and Demographic Fairness in Medical AI Challengescited by 2
  4. 2026The MAMA-MIA Challenge: Advancing Generalizability and Fairness in Breast MRI Tumor Segmentation and Treatment Response Predictioncited by 2
  5. 2026From Black-Box Confidence to Measurable Trust in Clinical AI: A Framework for Evidence, Supervision, and Staged Autonomycited by 2
  6. 2026Causal multi-modal AI for personalized chemosensitivity predictioncited by 2
  7. 2024Foundational Models for Pathology and Endoscopy Images: Application for Gastric Inflammationcited by 1
  8. 2025Open and Sustainable AI: challenges, opportunities and the road ahead in the life sciences (October 2025 -- Version 2)cited by 1
  9. 2025Glucose-ML: A collection of longitudinal diabetes datasets for development of robust AI solutionscited by 1
  10. 2026Engineering AI Agents for Clinical Workflows: A Case Study in Architecture,MLOps, and Governancecited by 1
  11. 2026TRACE: A Metrologically-Grounded Engineering Framework for Trustworthy Agentic AI Systems in Operationally Critical Domainscited by 1
  12. 2026RISED: A Pre-Deployment Evaluation Framework for High-Stakes AI Decision-Support Systems, with Application to Healthcarecited by 1
  13. 2026Vision Foundation Models in Radiology: A Scoping Review of Data, Methodology, Evaluation and Clinical Translationcited by 1
  14. 2023RAISE -- Radiology AI Safety, an End-to-end lifecycle approachcited by 0
  15. 2024Evaluation of Predictive Reliability to Foster Trust in Artificial Intelligence. A case study in Multiple Sclerosiscited by 0
  16. 2024AI in radiological imaging of soft-tissue and bone tumours: a systematic review evaluating against CLAIM and FUTURE-AI guidelinescited by 0
  17. 2024A causal viewpoint on prediction model performance under changes in case-mix: discrimination and calibration respond differently for prognosis and diagnosis predictionscited by 0
  18. 2025RELICT: A Replica Detection Framework for Medical Image Generationcited by 0
  19. 2025Explaining Uncertainty in Multiple Sclerosis Cortical Lesion Segmentation Beyond Prediction Errorscited by 0
  20. 2025WISCA: A Consensus-Based Approach to Harmonizing Interpretability in Tabular Datasetscited by 0
  21. 2025PiPViT: Patch-based Visual Interpretable Prototypes for Retinal Image Analysiscited by 0
  22. 2025A global log for medical AIcited by 0
  23. 2025Enhancing Transparency and Traceability in Healthcare AI: The AI Product Passportcited by 0
  24. 2026Trustworthy Data-driven Chronological Age Estimation from Panoramic Dental Imagescited by 0
  25. 2026A Master Class on Reproducibility: A Student Hackathon on Advanced MRI Reconstruction Methodscited by 0
  26. 2026Incentive Aware AI Regulations: A Credal Characterisationcited by 0
  27. 2026Consumer Attitudes Towards AI in Digital Health: A Mixed-Methods Survey in Australiacited by 0
  28. 2026Generating synthetic electronic health record data using agent-based models to evaluate machine learning robustness under mass casualty incidentscited by 0
  29. 2026Radiuma: A Unified Zero-Code Executable Graphical Workflow Generator for Reproducible and Shareable Medical Image Analysis and Machine Learningcited by 0
  30. 2026X-Edit: Exact, Explicit, and Explainable Null-Space Editing for Medical Vision Transformerscited by 0
  31. 2026Entropy Minimization without Model Collapse: Mitigating Prediction Bias in Medical Imagingcited by 0
  32. 2026Quantifying Explainable AI-introduced signal noise on ECG data with Spectral Entropycited by 0
  33. 2026Clinical Harness for Governable Medical AI Skill Ecosystemscited by 0
  34. 2026Healthier LLMs: Retrieval-Augmented Generation for Public Health Question Answeringcited by 0
  35. 2026Development of an automated, reliable, and clinically meaningful artificial intelligence (AI) tool for diagnosing cardiac disease from conventional cardiovascular magnetic resonance (CMR) imagescited by 0
  36. 2026Agentic AI in medicine: architectures, applications, evaluation, and challenges for clinical translationcited by 0
  37. 2026RESPClinBench: Benchmarking Multimodal Clinical Decision-Making and Longitudinal Disease Management in Respiratory Specialty Carecited by 0
  38. 2026SPOTting the Future: Lookahead Explanations for Deep Reinforcement Learningcited by 0

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