The state of physical medicine: AI as a new tool for physicians to treat patients - Medical Economics
<a href="https://news.google.com/rss/articles/CBMiugFBVV95cUxQeGZRZUdQb08zR1BQLVZfM0ZCa1AzOGRKOURiWHpaZFVlUFdock9wY2dVQ0N3ckUzQTh6OS1qZ3dKbXE3T1BtTU92dkxXZGp1NTRfUDlZQjJZdWJTZU9rRFUyMGhXS1h5UWFQNUhoVFhTZmRWQ0hGMjk2YUNpMnQxWWZVdlB2WVhxaEhWRUFQSnJuaEY2YXhGNEFtalY4enhnMlVnTWxMeXBTVF9LZGNPSmNOUlI3aVkwNWc?oc=5" target="_blank">The state of physical medicine: AI as a new tool for physicians to treat patients</a> <font color="#6f6f6f">Medical Economics</font>
Could not retrieve the full article text.
Read on GNews AI healthcare →Sign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Products
The Thiomi Dataset: A Large-Scale Multimodal Corpus for Low-Resource African Languages
arXiv:2603.29244v1 Announce Type: new Abstract: We present the Thiomi Dataset, a large-scale multimodal corpus spanning ten African languages across four language families: Swahili, Kikuyu, Kamba, Kimeru, Luo, Maasai, Kipsigis, Somali (East Africa); Wolof (West Africa); and Fulani (West/Central Africa). The dataset contains over 601,000 approved sentence-level text annotations and over 385,000 audio recordings across nine languages, collected through a dedicated community data collection platform involving over 100 contributors. The Thiomi platform collected data for nine languages; Swahili data was supplemented with existing Common Voice recordings. A multi-tier quality assurance pipeline achieves 86-100% text approval rates for the six primary languages. To validate the dataset's utility
Enhancing Box and Block Test with Computer Vision for Post-Stroke Upper Extremity Motor Evaluation
arXiv:2603.29101v1 Announce Type: new Abstract: Standard clinical assessments of upper-extremity motor function after stroke either rely on ordinal scoring, which lacks sensitivity, or time-based task metrics, which do not capture movement quality. In this work, we present a computer vision-based framework for analysis of upper-extremity movement during the Box and Block Test (BBT) through world-aligned joint angles of fingers, arm, and trunk without depth sensors or calibration objects. We apply this framework to a dataset of 136 BBT recordings collected from 48 healthy individuals and 7 individuals post stroke. Using unsupervised dimensionality reduction of joint-angle features, we analyze movement patterns without relying on expert clinical labels. The resulting embeddings show separati
From Astronomy to Astrology: Testing the Illusion of Zodiac-Based Personality Prediction with Machine Learning
arXiv:2603.29033v1 Announce Type: new Abstract: Astrology has long been used to interpret human personality, estimate compatibility, and guide social decision-making. Zodiac-based systems in particular remain culturally influential across much of the world, including in South Asian societies where astrological reasoning can shape marriage matching, naming conventions, ritual timing, and broader life planning. Despite this persistence, astrology has never established either a physically plausible mechanism or a statistically reliable predictive foundation. In this work, we examine zodiac-based personality prediction using a controlled machine-learning framework. We construct a synthetic dataset in which individuals are assigned zodiac signs and personality labels drawn from a shared pool of
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!