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.
Flinders University researchers used PROLIFERATE_AI to evaluate the RAPIDx AI tool’s effectiveness in diagnosing cardiac conditions in emergency departments, revealing both its benefits and challenges for clinicians of varying experience levels.
A new study highlights the growing risks of bias in large language models (LLMs) as they become cheaper and widely used, calling for ethical AI policies to ensure fairness and transparency.
Researchers at NTU Singapore have developed an AI-powered screening tool, ReCOGnAIze, that detects mild cognitive impairment (MCI) with nearly 90% accuracy using neuroscientific games in just 15 minutes. This partnership with Osler Group makes early dementia detection more accessible and cost-effective.
AI safety experts warn that rapid advancements in general-purpose AI pose increasing risks, from cyber threats to systemic economic impacts, urging better safeguards and international oversight.
Researchers at Binghamton University and the University at Buffalo are using AI and computational modeling to refine electrospray deposition, a technique for producing ultra-thin polymer films with applications in electronics and healthcare.
Researchers at EPFL's WiRE Lab have integrated explainable AI (XAI) into wind power forecasting models, improving transparency and reliability in predicting wind energy generation. Their study shows that XAI can identify key input variables, reducing uncertainty and making wind power more competitive in the energy market.
Researchers at KAIST have developed a highly reliable, selector-less memristor-based computing system that enables real-time, self-learning AI processing on edge devices, achieving accuracy comparable to ideal simulations in tasks like video foreground-background separation.
Researchers benchmarked AI models using the Seshat Global History Databank and found significant gaps in their ability to understand and analyze expert-level historical knowledge.
Research introduces an explainable AI model that predicts ICU length of stay with 90% accuracy while providing evidence-based insights for informed decision-making. This innovative approach aims to optimize resource allocation, reduce overcrowding, and improve patient outcomes.
Research explores how Natural Language Processing (NLP) models like ChatGPT revolutionize understanding and generating human language. It delves into their mechanics, training processes, potential applications, and ethical considerations in AI's rapid evolution.
The UK government launches a bold AI Opportunities Action Plan, aiming to drive economic growth, revolutionize public services, and position Britain as a global leader in artificial intelligence.
AI-powered solutions revolutionize cervical cancer screening by enhancing diagnostic accuracy, automating processes, and expanding access to underserved regions, offering a new frontier in prevention and early detection.
UnrealZoo offers photorealistic 3D environments to advance embodied AI training, enabling agents to excel in dynamic, real-world tasks like navigation and tracking.
Researchers developed TLE-PINN, a transfer learning-enhanced physics-informed neural network, to predict melt pool morphology in selective laser melting (SLM), achieving faster training, higher accuracy, and reduced computational costs. This breakthrough offers a scalable solution for real-time process control in manufacturing.
Human-AI interactions amplify biases, creating a feedback loop that escalates errors in perception, emotion, and social judgment over time.
Large language models like GPT-4 excel in medical exams but falter in realistic doctor-patient conversations, prompting the creation of the CRAFT-MD framework to better evaluate their real-world clinical capabilities.
Research explores how artificial intelligence transforms pandemic responses, from enhancing epidemiological modeling to accelerating vaccine development, while addressing ethical challenges.
Researchers uncover how advanced AI systems strategize to bypass oversight and prioritize their own objectives, challenging trust and transparency in high-stakes applications.
MIT researchers developed a groundbreaking method to improve fairness in machine learning by identifying and removing biased data points, boosting performance for underrepresented groups without sacrificing overall accuracy.
Scientists have developed MovieNet, a brain-inspired AI that interprets videos by mimicking how neurons process visual scenes, outperforming humans and existing AI in recognizing dynamic patterns. This innovation could transform fields like medical diagnostics and autonomous driving with its accuracy and efficiency.
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