Artificial Intelligence (AI) is being adopted across the world and promises a new revolution in healthcare. While AI-enabled medical devices in North America dominate 42.3% of the global market, the use of AI-enabled medical devices in other countries is still a story waiting to be unfolded. We aim to delve deeper into global regulatory approaches towards AI use in healthcare, with a focus on how common themes are emerging globally. We compare these themes to the World Health Organization's (WHO) regulatory considerations and principles on ethical use of AI for healthcare applications. Our work seeks to take a global perspective on AI policy by analyzing 14 legal jurisdictions including countries representative of various regions in the world (North America, South America, South East Asia, Middle East, Africa, Australia, and the Asia-Pacific). Our eventual goal is to foster a global conversation on the ethical use of AI in healthcare and the regulations that will guide it. We propose solutions to promote international harmonization of AI regulations and examine the requirements for regulating generative AI, using China and Singapore as examples of countries with well-developed policies in this area.

That's according to a recent World Health Organization policy brief explaining that data used by A.I. in healthcare can be unrepresentative of older people. A.I. is a product of its algorithms, the brief explains, and can draw ageist conclusions if the data that feeds the algorithms is skewed toward younger individuals. This could affect, for example, telehealth tools used to predict illness or major health events in a patient. It could also provide inaccurate data for drug development. Ultimately, not including older adults in the development process for A.I. can make it harder to get them to adopt new A.I. applications in the future.

Don't miss our upcoming virtual workshop with John Snow Labs, Improve Drug Safety with NLP, to learn more about our joint NLP solution accelerator for adverse drug event detection. The World Health Organization defines pharmacovigilance as "the science and activities relating to the detection, assessment, understanding and prevention of adverse effects or any other medicine/vaccine-related problem." While all medicines and vaccines undergo rigorous testing for safety and efficacy in clinical trials, certain side effects may only emerge once these products are used by a larger and more diverse patient population, including people with other concurrent diseases. To support ongoing drug safety, biopharmaceutical manufacturers must report adverse drug events (ADEs) to regulatory agencies, such as the US Food and Drug Administration (FDA) in the United States and the European Medicines Agency (EMA) in the EU. Adverse drug reactions or events are medical problems that occur during treatment with a drug or therapy.

The World Health Organization (WHO) recently released a report presenting guidance around the ethical use of artificial intelligence (AI) in the health sector. The lack of a general consensus for ethical use of AI has sparked debate among those in the industry, with some raising concerns about the implications of this technology. This has led to organizations seeking to offer their own solutions, such as the National Institute of Standards and Technology's recent proposal to reduce bias in the use of AI. The WHO's report, titled Ethics and Governance of Artificial Intelligence for Health, seeks to address similar concerns -- as well as potential benefits -- of AI's potential roles in the health sector. It offers six primary principles for the use of AI: promote human well being, human safety and the public interest ensure transparency, explainability and intelligibility foster responsibility and accountability ensure inclusiveness and equity promote AI that is responsive and sustainable The organization's hope, the report states, is that these principles will be used as a foundation for AI stakeholders, including governments, developers and society.

Gaming disorder could soon be recognized by the World Health Organization. The World Health Organization might soon say you suffer from "gaming disorder." A draft of WHO's 11th update of International Classification of Diseases characterizes the disorder as "recurrent" gaming behavior manifested by "impaired control over gaming," "increasing priority given to gaming," and "escalation of gaming despite the occurrence of negative consequences." The term "gaming disorder" isn't limited to simply excessive time spent playing video games in the entry. The disorder could include adverse online and offline behavior tied to excessive video game activity, WHO spokesperson Tarik Jašarević said.

The quality of machine translation is rapidly evolving. Today one can find several machine translation systems on the web that provide reasonable translations, although the systems are not perfect. In some specific domains, the quality may decrease. A recently proposed approach to this domain is neural machine translation. It aims at building a jointly-tuned single neural network that maximizes translation performance, a very different approach from traditional statistical machine translation. Recently proposed neural machine translation models often belong to the encoder-decoder family in which a source sentence is encoded into a fixed length vector that is, in turn, decoded to generate a translation. The present research examines the effects of different training methods on a Polish-English Machine Translation system used for medical data. The European Medicines Agency parallel text corpus was used as the basis for training of neural and statistical network-based translation systems. The main machine translation evaluation metrics have also been used in analysis of the systems. A comparison and implementation of a real-time medical translator is the main focus of our experiments.