Generalized quantifiers (e.g., few, most) are used to indicate the proportions predicates are satisfied (for example, some apples are red). One way to interpret quantifier semantics is to explicitly bind these satisfactions with percentage scopes (e.g., 30%-40% of apples are red). This approach can be helpful for tasks like logic formalization and surface-form quantitative reasoning (Gordon and Schubert, 2010; Roy et al., 2015). However, it remains unclear if recent foundation models possess this ability, as they lack direct training signals. To explore this, we introduce QuRe, a crowd-sourced dataset of human-annotated generalized quantifiers in Wikipedia sentences featuring percentage-equipped predicates. We explore quantifier comprehension in language models using PRESQUE, a framework that combines natural language inference and the Rational Speech Acts framework. Experimental results on the HVD dataset and QuRe illustrate that PRESQUE, employing pragmatic reasoning, performs 20% better than a literal reasoning baseline when predicting quantifier percentage scopes, with no additional training required.

The latest Digital Health news industry round-up includes an award of £1.4m for Mendelian to further develop its AI solutions, a pilot for a state-of-the-art prehabilitation programme at James Paget University Hospitals and the launch of Spex Capital's €100m health tech fund. A 2023 report on the clinical alarm management market by Future Market Insights has named Tutum Medical's Bedside Equipment Alarm Monitoring System (BEAMS) as a recent development in the market to watch. The reports says: "Sheffield Children's Hospital announced a collaboration with Tutum Medical in March 2017 to create a revolutionary design for a Bedside Equipment Alarm Monitoring System (BEAMS). The system provides crucial bedside equipment monitoring to improve nursing staff response times to alarms. The solution is expected to cut alarm response time by up to 95%, considerably lowering alarm fatigue and enhancing staff availability to serve important patients effectively."

There is a lot of excitement in healthcare about the use of artificial intelligence (AI) to improve clinical decision-making. Pioneered by the likes of IBM Watson for Healthcare and DeepMinds Healthcare, AI promises to help specialists diagnose patients more accurately. Two years ago, McKinsey co-produced a report with the European Union's EIT Health to explore the potential for AI in healthcare. Among the key opportunities the report's authors found were in healthcare operations: diagnostics, clinical decision support, triage and diagnosis, care delivery, chronic care management and self-care. "First, solutions are likely to address the low-hanging fruit of routine, repetitive and largely administrative tasks, which absorb significant time of doctors and nurses, optimising healthcare operations and increasing adoption," they wrote.

There is a lot of excitement in healthcare about the use of artificial intelligence (AI) to improve clinical decision-making. Pioneered by the likes of IBM Watson for Healthcare and DeepMinds Healthcare, AI promises to help specialists diagnose patients more accurately. Two years ago, McKinsey co-produced a report with the European Union's EIT Health to explore the potential for AI in healthcare. Among the key opportunities the report's authors found were in healthcare operations: diagnostics, clinical decision support, triage and diagnosis, care delivery, chronic care management and self-care. "First, solutions are likely to address the low-hanging fruit of routine, repetitive and largely administrative tasks, which absorb significant time of doctors and nurses, optimising healthcare operations and increasing adoption," they wrote.

Machine learning has shown significant improvement in healthcare. Researchers have developed models that can diagnose critical conditions like diabetic eye disease or metastatic breast cancer. The computer vision has been even tried for AR assisted surgeries. But why don't we see more AI in healthcare? Challenges are plaguing the ML community.

Alexis May meets Dr Rizwan Malik to discuss Bolton's partnerships with Qure.ai to develop and deploy a COVID-19 detection tool. Dr Malik shares his experience of deployment so far, as well offering advice for others looking to implement AI solutions. You must be logged in to post a comment.

You've already heard that Artificial Intelligence or AI is ubiquitous. In fact, you may not be aware that you use it all the time. It's being used by the digital assistants on your smartphone, it's personalizing your Instagram feed, and those fun Snapchat Filters are also AI-powered. In a nutshell, AI has grown rapidly and is helping transform almost every industry you can think of. Healthcare and medicine are one of the many sectors that are leveraging the benefits of this advanced technology. AI is used to better diagnose diseases and reduce error, as an efficient symptom checker, in fitness bands, as radiology assistants, and a lot more.

Mumbai-based startup Qure uses an AI imaging tool qER to save precious minutes for emergency room staff to take action based on head CT scans. After deployment in India and several other countries, qER is now entering the US where 75 million CT scans are performed every year. A couple of weeks ago, Qure received US FDA 510 (k) clearance for this product. What makes it special is a four-in-one clearance. The tool has been cleared for triaging four critical conditions--intracranial bleeds, mass effect (due to spaces in the brain filling up), midline shift (in the brain's alignment), and cranial fractures.

Mumbai-based Qure.ai uses an artificial intelligence-powered solution to identify 24 different abnormalities in a chest X-ray, including ones indicative of a covid-19 infection. Built on Amazon Web Services (AWS) and trained using machine learning to detect pulmonary problems, including diseases like tuberculosis, the original solution has been repurposed by Qure for the ongoing pandemic. Given the global shortage of test kits, Qure's machine learning solution qXR can very quickly prioritize those who need to be tested immediately and those who need to be self-isolated, thus helping to maximise resource utilization. Since the launch of the covid-19 version in March, qXR has been deployed in over 40 sites globally, including Mumbai. The Municipal Corporation of Greater Mumbai (MCGM) has deployed this cost-effective and scalable solution to assist the front line critical healthcare professionals.

Rizwan Malik had always had an interest in AI. As the lead radiologist at the Royal Bolton Hospital, run by the UK's National Health Service (NHS), he saw its potential to make his job easier. In his hospital, patients often had to wait six hours or more for a specialist to look at their x-rays. If an emergency room doctor could get an initial reading from an AI-based tool, it could dramatically shrink that wait time. A specialist could follow up the AI system's reading with a more thorough diagnosis later.