Plus: Huntington's disease has been treated successfully for the first time. Wikipedia is the most ambitious multilingual project after the Bible: There are editions in over 340 languages, and a further 400 even more obscure ones are being developed. But many of these smaller editions are being swamped with AI-translated content. Volunteers working on four African languages, for instance, estimated to that between 40% and 60% of articles in their Wikipedia editions were uncorrected machine translations. This is beginning to cause a wicked problem. AI systems learn new languages by scraping huge quantities of text from the internet.

Large language models (LLMs) have performed well across various clinical natural language processing tasks, despite not being directly trained on electronic health record (EHR) data. In this work, we examine how popular open-source LLMs learn clinical information from large mined corpora through two crucial but understudied lenses: (1) their interpretation of clinical jargon, a foundational ability for understanding real-world clinical notes, and (2) their responses to unsupported medical claims. For both use cases, we investigate the frequency of relevant clinical information in their corresponding pretraining corpora, the relationship between pretraining data composition and model outputs, and the sources underlying this data. To isolate clinical jargon understanding, we evaluate LLMs on a new dataset MedLingo . Unsurprisingly, we find that the frequency of clinical jargon mentions across major pretrain-ing corpora correlates with model performance. However, jargon frequently appearing in clinical notes often rarely appears in pretraining corpora, revealing a mismatch between available data and real-world usage. Similarly, we find that a non-negligible portion of documents support disputed claims that can then be parroted by models. Finally, we classified and analyzed the types of online sources in which clinical jargon and unsupported medical claims appear, with implications for future dataset composition.

Evidence-based medicine is the practise of making medical decisions that adhere to the latest, and best known evidence at that time. Currently, the best evidence is often found in the form of documents, such as randomized control trials, meta-analyses and systematic reviews. This research focuses on aligning medical claims made on social media platforms with this medical evidence. By doing so, individuals without medical expertise can more effectively assess the veracity of such medical claims. We study three core tasks: identifying medical claims, extracting medical vocabulary from these claims, and retrieving evidence relevant to those identified medical claims. We propose a novel system that can generate synthetic medical claims to aid each of these core tasks. We additionally introduce a novel dataset produced by our synthetic generator that, when applied to these tasks, demonstrates not only a more flexible and holistic approach, but also an improvement in all comparable metrics. We make our dataset, the Expansive Medical Claim Corpus (EMCC), available at https://zenodo.org/records/8321460. Keywords: Evidenced-based Medicine, PICO, Synthetic Generators, Information Retrieval

Kaia Health, a digital therapeutics startup which uses computer vision technology for real-time posture tracking via the smartphone camera to deliver human-hands-free physiotherapy, has closed a $26 million Series B funding round. The funding was led by Optum Ventures, Idinvest and capital300 with participation from existing investors Balderton Capital and Heartcore Capital, in addition to Symphony Ventures -- the latter in an "investment partnership" with world famous golfer, Rory McIlroy, who knows a thing or two about chronic pain. Back in January 2019, when Kaia announced a $10M Series A, its business ratio was split 80:20 Europe to US. Now, says co-founder and CEO Konstantin Mehl -- speaking to TechCrunch by Zoom chat from New York where he's recently relocated -- it's flipped the other way. Part of the new funding will thus go on building out its commercial team in the US -- now its main market.

On Monday 8th April 2019, the European Commission's High-Level Expert Group on Artificial Intelligence (AI HLEG) revealed ethics guidelines aimed at forming best practices for creating "trustworthy AI." In fact, many argue this issue of trust in the AI system is one of the main hurdles the technology must overcome for more widespread implementation. A Forbes survey found that nearly 42% of respondents "could not cite a single example of AI that they trust"; in another survey, when respondents were asked what emotion best described their feeling towards AI, "Interested" was the most common response (45%), but it was closely followed by "concerned" (40.5%), "skeptical" (40.1%), "unsure" (39.1%), and "suspicious" (29.8%). The Commission's guidelines are a new roadmap for businesses to align their AI systems. While these guidelines are not policy, it is easy to imagine that they will serve as the building blocks for such regulations.