AI is employed in healthcare for various applications, including medical image analysis, disease diagnosis, personalized treatment planning, and patient monitoring. It utilizes machine learning, natural language processing, and data analytics to improve diagnostic accuracy, optimize treatment outcomes, and enhance healthcare delivery, leading to more efficient and effective patient care.
A novel encryption scheme, BCAES, intertwines Blockchain and Arnold's cat map encryption to fortify medical data storage and transmission in the cloud. By combining chaos theory-based encryption with blockchain's tamper-resistant nature, BCAES ensures data integrity, authenticity, and confidentiality, outperforming traditional methods and offering a promising avenue for secure healthcare data management.
Researchers scrutinize the clinical prowess of cutting-edge language models like GPT-3.5 and GPT-4 alongside Google search, shedding light on their diagnostic and therapeutic capabilities across various medical scenarios. While GPT-4 emerges as a frontrunner, particularly in diagnosing common ailments, challenges persist, emphasizing the imperative for ongoing advancements and regulatory vigilance in integrating artificial intelligence into healthcare.
This study delves into the complex relationship between technology and psychology, examining how individuals perceive androids based on their beliefs about artificial beings. By investigating the impact of labeling human faces as "android," the research illuminates how cognitive processes shape human-robot interaction and social cognition, offering insights for designing more socially acceptable synthetic agents.
The Science and Technology Facilities Council’s (STFC) Hartree Centre and the Mersey Care NHS Foundation Trust have announced a strategic partnership to advance Artificial Intelligence (AI) in healthcare across the Trust to optimise patient outcomes.
Researchers introduced "Cap de Ballon," a virtual village designed to combat social isolation among older individuals. Leveraging virtual reality (VR), artificial intelligence (AI), and visual analysis, the collaborative effort resulted in a comprehensive methodology to develop immersive VR environments, fostering communication, creativity, and positive emotions among seniors. The study's findings underscored seniors' enthusiasm for VR engagement, highlighting the potential of virtual villages to enhance social and emotional well-being in the elderly population.
In a recent paper published in Scientific Reports, researchers addressed the challenges of accurately diagnosing migraine headaches using machine learning (ML) techniques. Leveraging state-of-the-art ML algorithms such as support vector machine (SVM), k-nearest neighbors (KNN), random forest (RF), decision tree (DST), and deep neural networks (DNN), the study demonstrated remarkable effectiveness in classifying seven different types of migraines.
In a groundbreaking article published in Nature, researchers introduced a massive corpus comprising 58,658 machine-annotated incident reports of medication errors, tackling the challenge of unstructured free text. Leveraging Japan's open-access dataset, this initiative aimed to enhance patient safety by facilitating automated analysis through natural language processing (NLP).
Researchers investigated the viability of using photoplethysmography (PPG) signals and one-dimensional convolutional neural networks (1D CNNs) for human activity recognition (HAR). Conducting experiments on 40 participants engaged in various activities, the study demonstrated high accuracy (95.14%) in classifying five common daily activities using PPG data. While promising, limitations include the homogeneity of the participant pool and potential biases in results, underscoring the need for broader studies in diverse populations.
Researchers introduce a hierarchical federated learning framework tailored for large-scale AIoT systems in smart cities. By integrating cloud, edge, and fog computing layers and leveraging the MQTT protocol, the framework addresses data privacy and communication latency challenges, demonstrating enhanced scalability and efficiency. Experimental validation in Docker environments confirms the framework's feasibility and performance improvements, laying the foundation for future optimizations.
Researchers investigate ChatGPT ADA, an extension of GPT-4, for developing ML models in clinical data analysis, showing comparable performance to manual methods. With transparent methodologies and robust performance across diverse clinical trials, ChatGPT ADA presents a promising tool for democratizing ML in medicine, emphasizing its potential alongside specialized training and resources.
Dartmouth researchers develop MoodCapture, an AI-powered smartphone app that detects early symptoms of depression with 75% accuracy using facial-image processing, promising a new tool for mental health monitoring.
Scientists develop a reprogrammable light-based processor to advance quantum computing, promising faster computations, secure communications, and environmental and healthcare monitoring enhancements.
AI predicts energy expenses from passive design, offering a tool for reducing the energy burden on low-income households and advancing energy justice.
A study in Digital Medicine explores ownership, usage, and willingness to share data from smart devices among Duke University Health System (DUHS) patients. Findings reveal widespread smartphone and wearable ownership, with usage focused on health tracking, while demographic variations influence data sharing preferences, highlighting the need for inclusive digital health strategies to address barriers like cost and privacy concerns.
Researchers present an innovative upper-limb exoskeleton system leveraging deep learning (DL) to predict and enhance human strength. Integrating soft wearable sensors and cloud-based DL, the system achieves a remarkable 96.2% accuracy in real-time motion prediction, significantly reducing muscle activities by 3.7 times on average. This user-friendly solution addresses age and stroke-related strength decline, marking a transformative leap in robotic exoskeleton technology for assisting individuals with neuromotor disorders in daily tasks.
"npj Digital Medicine" presents a scoping review on AI applications in home-based virtual rehabilitation (VRehab), showing its effectiveness in stroke, cardiac, and orthopedic rehabilitation. AI-driven VRehab offers personalized feedback, enhances patient outcomes, and overcomes barriers to traditional rehabilitation, heralding a new era in accessible and efficient healthcare delivery. Further research is needed to standardize evaluation methods and ensure privacy while maximizing the potential of AI in personalized rehabilitation programs.
Researchers introduce a pioneering system merging machine learning and knowledge graph technology to streamline medical diagnosis and treatment. Leveraging advanced methodologies like multiple levels refinement and knowledge distillation, the system empowers healthcare professionals with rapid and accurate solutions, offering a transformative tool for navigating complex medical research. Through iterative refinement and interactive exploration, this system provides comprehensive and relevant information, addressing key challenges in healthcare knowledge management.
Researchers from Egypt introduce a groundbreaking system for Human Activity Recognition (HAR) using Wireless Body Area Sensor Networks (WBANs) and Deep Learning. Their innovative approach, combining feature extraction techniques and Convolutional Neural Networks (CNNs), achieves exceptional accuracy in identifying various activities, promising transformative applications in healthcare, sports, and elderly care.
A comprehensive meta-analysis and systematic review assesses AI's diagnostic accuracy in detecting fractures across various data types and imaging modalities. With 66 studies analyzed, the review underscores AI's high accuracy and reliability, especially in utilizing imaging data, while also emphasizing the need for improved transparency in study reporting and validation methods to enhance clinical applicability.
Researchers developed a smart glove integrating tactile sensors and vibrotactile actuators, fabricated via digital embroidery, enabling seamless tactile interaction transfer. They introduced a machine-learning pipeline optimizing haptic feedback based on user responses, showcasing applications in healthcare, augmented reality, and human-robot collaboration. This textile-based interface holds promise for enriching technology-mediated interactions, with potential extensions to other wearable devices and complex tactile sensations.
Terms
While we only use edited and approved content for Azthena
answers, it may on occasions provide incorrect responses.
Please confirm any data provided with the related suppliers or
authors. We do not provide medical advice, if you search for
medical information you must always consult a medical
professional before acting on any information provided.
Your questions, but not your email details will be shared with
OpenAI and retained for 30 days in accordance with their
privacy principles.
Please do not ask questions that use sensitive or confidential
information.
Read the full Terms & Conditions.