Towards International Standards for the Evaluation of Artificial Intelligence for Health Healthcare can benefit considerably from advanced information processing technologies, in particular from machine learning (ML) and artificial intelligence (AI). However, the health domain only hesitantly adopts these powerful but complex innovations so far, because any technical fault can affect peopleu0027s health, privacy, and consequently their entire lives. In this paper, we substantiate that international standards are required for thoroughly validating AI solutions for health, by benchmarking their performance. These standards might ultimately create wellfounded trust in those AI solutions that have provided conclusive evidence to be accurate, effective and reliable. We give reasons that standardized benchmarking of AI solutions for health is a necessary complement of established assessment procedures. In particular, we demonstrate that it is beneficial to tackle this topic on a global scale and summarize the achievements of the first year of the ITUWHO focus group on xe2x80x9cAI for Healthxe2x80x9d that has tasked itself to work towards creating these evaluation standards.

Artificial Intelligence for Public Health In this talk, we examine artificial intelligence approaches for extracting actionable insights from health care data in order to improve public health. Our goal is to simultaneously identify subpopulations with distinct health risks and health trajectories and find the distinct risk factors or determinants associated each subpopulation. These determinants can then be used treatments, programs, and policies in order to reduce mortality and comorbidity and provide more efficient healthcare. We examine novel cadre machine learning approaches that combine predictive neural network modeling with more traditional statistical epidemiology methods for risk and survival analyses. We embed the cadre methods into a Semantically Targeted Analytics (Semantalytics) System that combines semantics, inference, automatic machine learning, and explainable AI. The AI system translate the public health questions to an analysis plan, prepares data, conducts analysis and reports results with visualization and text. We demonstrated these approach on public health care surveillance datasets and electronic medical records. The award winning xe2x80x9cMortalityMinderxe2x80x9d app examines the social determinants of xe2x80x9cDeaths Despairxe2x80x9d (deaths from suicide and substance abuse) and other causes of mortality that are unexpectedly rising in the United States. Other applications include association of environment toxins associated with diseases, high needs patient management for a health management organization, and emergency department readmissions. We conclude with the discussion of the open challenges to create population health AI systems that can transform health care questions into datadriven actionableinsights on the fly.