Despite regulatory efforts to establish reliable content-rating guidelines for mobile apps, the process of assigning content ratings in the Google Play Store remains self-regulated by the app developers. There is no straightforward method of verifying developer-assigned content ratings manually due to the overwhelming scale or automatically due to the challenging problem of interpreting textual and visual data and correlating them with content ratings. We propose and evaluate a visionlanguage approach to predict the content ratings of mobile game applications and detect content rating violations, using a dataset of metadata of popular Android games. Our method achieves ~6% better relative accuracy compared to the state-of-the-art CLIP-fine-tuned model in a multi-modal setting. Applying our classifier in the wild, we detected more than 70 possible cases of content rating violations, including nine instances with the 'Teacher Approved' badge. Additionally, our findings indicate that 34.5% of the apps identified by our classifier as violating content ratings were removed from the Play Store. In contrast, the removal rate for correctly classified apps was only 27%. This discrepancy highlights the practical effectiveness of our classifier in identifying apps that are likely to be removed based on user complaints.

Central and Eastern Europe is well positioned to take a leading role in the development of AI in healthcare, but the creation of a marketplace for data is crucial. Just how important a role will artificial intelligence (AI) have in medicine over the coming years? That it will revolutionise healthcare is now beyond doubt, particularly in early diagnosis. Even so, its importance – and the need to speed up its implementation – cannot be overstated. Ligia Kornowska, the managing director of the Polish Hospital Federation, and a leader of the AI Coalition in Healthcare, is clear: "not to make use of AI," she says, "will soon be viewed as medical malpractice."

The rapid entry of artificial intelligence is stretching the boundaries of medicine. It will also test the limits of the law. Artificial intelligence (AI) is being used in health care to flag abnormalities in head CT scans, cull actionable information from electronic health records, and help patients understand their symptoms. At some point, AI is bound to make a mistake that harms a patient. When that happens, who -- or what -- is liable?

AI is already on its way to transforming healthcare delivery and improving patient outcomes. However, while AI, Machine Learning, and Robotics are all designed to reduce human error and increase the predictability of patient care, they also create new risks across the healthcare liability landscape. In a situation where a healthcare provider uses AI to treat a patient who has a less than a desired outcome (or even simply an unanticipated one), we anticipate liability suits against those healthcare providers, healthcare systems, AI software companies, and robotic device manufacturers. In this post, we will consider what happens when lawsuits get ahead of science, insurance considerations in this new liability landscape, and possible modifications to legal doctrine to address this new science. What makes AI so compelling is its use of predictive, learning algorithms (Machine Learning) to improve the precision of the practice of medicine.

If you think you've got a bad case of the travel bug, get this: Dr. John Halamka travels 400,000 miles a year. Halamka is chief information officer at Harvard's Beth Israel Deaconess Medical Center, a professor at Harvard Medical School, and a practicing emergency physician. In a talk at Singularity University's Exponential Medicine last week, Halamka shared what he sees as the biggest healthcare problems the world is facing, and the most promising technological solutions from a systems perspective. "In traveling 400,000 miles you get to see lots of different cultures and lots of different people," he said. "And the problems are really the same all over the world. Maybe the cultural context is different or the infrastructure is different, but the problems are very similar."

Mathematical Malpractice is fun to say. It really feels good if you put the emphasis on "Mal". Mal, of course is Latin for "badly". In Old French it means "evil" or "wrong." All those meanings come to play here. The Malpractice in analytics and AI is bad, evil and wrong.

In the coming decades, diagnostic medicine will likely change dramatically. Perhaps the most conspicuous change will be the arrival of artificial intelligence (AI) for faster and better care. AI is not pitting man against machine. It is, in fact, a way to ease the physician's burden and expand the possibilities of treatment. The administrative aspect of practicing medicine can be overwhelming, to say the least, and AI offers the chance to sort through large amounts of information quickly and accurately.

The use of AI assistants in healthcare is making alternative care models that improve patient engagement at home more relevant. However, AI assistants continue to struggle with fundamental issues around speech recognition, user identification, privacy, and data security. Designing a virtual care experience requires precision and a comprehensive understanding of the implications of malpractice for all parties involved, at all stages from software development to the delivery of care. Over time, AI assistants will evolve to become a key component of home-based healthcare, but until then, most will be limited to low-risk tasks that pose no threat to patients. The design, implementation, and management of voice experiences for AI assistants such as Alexa and Google Assistant is key for companies interested in using voice as an interface for smart solutions.

A 29-year-old Nepalese student in Tokyo has found herself stuck in limbo with her dreams derailed, and the state of Japan's language schools is to blame. A survivor of human trafficking in the past, the woman, who wished to be identified only by her last name, Puri, came to Japan in 2014 as an exchange student. Brimming with high expectations at the time, she said she was determined to acquire a master's degree in sociology, with an emphasis on a subject dear to her, women's rights. Imagine her disappointment, then, when her dream was cut short by the Japanese-language school in Tokyo where she was studying. The school taught her only the very basics of the language, lumped her in with unmotivated students who frequently fell asleep in class and -- to her shock -- informed her that a vocational school was the only educational path it could prepare her for.

Labor Day is the one day every year when we come together as a nation to celebrate the achievements of the American worker and the history of the labor movement in this country. Hillary Clinton and Donald Trump will join President Obama (who spent the weekend meeting with G20 leaders issued a Labor Day message on September 1) as well as a variety of politicians and public officials from across the country, in commemorating the day. You can bet that their lofty rhetoric will be accompanied by a promise to restore the nation to its manufacturing heyday. At the Democratic Convention in Philadelphia, for instance, Clinton promised to push policies that will help foster a "manufacturing renaissance." Not to be outdone, Donald Trump has long said he will be "the greatest job-producing president in American history."