Whitepaper for the ITU/WHO Focus Group on Artificial Intelligence for Health
This whitepaper outlines the establishment, objectives, and ongoing work of the ITU/WHO Focus Group on Artificial Intelligence for Health (FG-AI4H). It details how the group develops international evaluation standards, guidelines, and an online benchmarking platform to assess AI solutions in healthcare. Additionally, it discusses current AI applications in health, major technical and ethical challenges such as data bias and interpretability, and future directions for global digital health standardization.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
This document is for AI developers, researchers, healthcare practitioners, regulators, and policymakers interested in the standardized evaluation and benchmarking of artificial intelligence in healthcare.
What the document contains
The document covers the motivation, background, and operational structure of the ITU/WHO Focus Group on Artificial Intelligence for Health, including its deliverables, topic groups, working groups, and plans for an online benchmarking platform. It also provides a technical overview of modern AI and deep learning in healthcare alongside associated challenges like bias and interpretability.
The text does not contain legally binding regulations, explicit enforcement mechanisms, or finalized text for international treaties.
- The ITU and WHO established the Focus Group on Artificial Intelligence for Health (FG-AI4H) to build international evaluation standards and benchmarking frameworks for health AI solutions.
- Recent advances in deep learning and big data enable powerful AI applications in medical image interpretation, natural language understanding, and laboratory result analysis.
- Key challenges in health AI include a lack of model explainability, insufficient comparative effectiveness data, potential biases in training data, and strict legal restrictions regarding health data access.
- FG-AI4H operates through topic groups focused on specific health use cases and working groups addressing overarching themes like ethics, regulation, and data specifications.
- The group is developing an open-source online benchmarking platform using undisclosed test data and standardized metrics to validate AI models securely.
· Abstract · 1 Introduction · 2 Artificial intelligence · 3 Artificial intelligence for health · 4 Focus Group on "Artificial Intelligence for Health" (FG-AI4H) · 5 Future directions
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
2/5Clear responsibility for AI outcomes
This is crucial for clarifying responsibilities and for building trust among AI developers, AI regulators, and AI users.
Abstract
screening score · 2026-10-23
Human Oversight
0/5Humans remain in control of consequential decisions
screening score · 2026-10-23
Privacy
2/5Protection of personal data and private life
because data are sensitive and subject to (country and region dependent) privacy-protection laws as well as ethical considerations
3 Artificial intelligence for health
screening score · 2026-10-23
Transparency
3/5Openness about how AI systems work and are used
Thus, interpretability, explainability, and proven robustness (e.g., to outliers and to adversarial attacks) are crucial aspects that have to be considered
3 Artificial intelligence for health
screening score · 2026-10-23
Enforcement
0/5Consequences and compliance mechanisms with real force
screening score · 2026-10-23
Impact Assessment
3/5Required assessment of an AI system's risks/impacts before or during use
The data are evaluated to avoid the risk of bias and other relevant data quality aspects.
4. Focus Group on 'Artificial Intelligence for Health' (FG-AI4H)
screening score · 2026-10-23