2018/09/13 by Marcel Salathé, Salathé, Marcel, Thomas Wiegand +3 · 1 citation
Computer Science · Medicine · #AI in cancer detection #Artificial Intelligence (cs.AI) #Artificial Intelligence in Healthcare and Education #COVID-19 diagnosis using AI #Computers and Society (cs.CY) #FOS: Computer and information sciences #cs.AI #cs.CY
paper · pdf · doi:10.48550/arxiv.1809.04797
Whitepaper on ITU Focus Group AI4H for 1st workshop at WHO
arxiv created 2018/09/13 · openalex publication_date 2018/09/13 · arxiv updated 2018/09/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Artificial Intelligence (AI) - the phenomenon of machines being able to solve problems that require human intelligence - has in the past decade seen an enormous rise of interest due to significant advances in effectiveness and use. The health sector, one of the most important sectors for societies and economies worldwide, is particularly interesting for AI applications, given the ongoing digitalisation of all types of health information. The potential for AI assistance in the health domain is immense, because AI can support medical decision making at reduced costs, everywhere. However, due to the complexity of AI algorithms, it is difficult to distinguish good from bad AI-based solutions and to understand their strengths and weaknesses, which is crucial for clarifying responsibilities and for building trust. For this reason, the International Telecommunication Union (ITU) has established a new Focus Group on "Artificial Intelligence for Health" (FG-AI4H) in partnership with the World Health Organization (WHO). Health and care services are usually the responsibility of a government - even when provided through private insurance systems - and thus under the responsibility of WHO/ITU member states. FG-AI4H will identify opportunities for international standardization, which will foster the application of AI to health issues on a global scale. In particular, it will establish a standardized assessment framework with open benchmarks for the evaluation of AI-based methods for health, such as AI-based diagnosis, triage or treatment decisions.