111 stories tagged Healthcare · page 2 of 5
Researchers created LongMedBench, a new benchmark for AI medical assistants that simulates real-world patient care over time. Built from real EHR data (MIMIC-IV), it evaluates how well AI can handle ongoing treatments, repeated visits, and evolving health conditions.
A new research paper outlines how large language models (LLMs) are being used in healthcare, bridging the gap between clinical needs and AI capabilities. The study proposes a framework to align medical training standards with AI's potential in patient care.
Researchers highlight risks of AI in mental health and propose a new framework to ensure safer, more effective support. The study calls for proactive measures to address long-term harms like dependency and distorted beliefs.
A new survey shows that the majority of nurses don't trust AI to handle patient care. This highlights ongoing concerns about AI's reliability in critical healthcare roles. Nurses worry about potential errors and the lack of human touch in AI-driven care.
Utah has become the first state to allow AI to refill prescriptions without direct doctor approval. Some physicians worry about patient safety and the lack of human oversight, while regulators seek a balance between innovation and caution.
The NHS has added an AI feature to its app that helps patients find the right healthcare services more quickly, potentially reducing wait times and easing pressure on the system.
Midjourney unveiled its dunk-tank ultrasound scanner, aiming to revolutionize medical imaging. However, the company has yet to provide concrete proof that the technology works as advertised.
Researchers are exploring a new approach to AI-generated radiology reports using a diffusion technique. The model, DiffusionGemma-26B, gradually refines text on a 'canvas' rather than writing left-to-right. Initial benchmarks on medical visual question answering tasks show it is competitive with traditional autoregressive models of the same size.
Researchers introduced MedAgentBench-v3, a new benchmark for AI agents in healthcare. It improves on previous versions by addressing issues where agents often did nothing, making it harder to train them effectively.
Researchers introduced FaithMed, a framework that trains large language models to reason faithfully using evidence-based medicine principles. It combines clinician-designed rubrics with reinforcement learning to ensure transparent, evidence-grounded medical reasoning.
Researchers have developed an AI model called RareDxR1 that can diagnose rare diseases by analyzing unstructured patient symptoms. This could make it easier for doctors to identify complex conditions that are often missed.
China is set to launch its first AI-powered cancer vaccine production line in Beijing. The facility uses artificial intelligence to optimize and accelerate vaccine development and production, promising faster, more precise, and personalized cancer treatments.
Researchers developed an AI system that combines official drug data with patient experiences to provide safer, more accurate medication information. This could help people make better decisions about their mental health treatments.
AI systems are now authorized to prescribe medications independently in the U.S. This shift raises critical questions about trust, liability, and how to handle uncertainty in medical decisions. Researchers highlight gaps in current regulations that could impact patient safety.
A new study found that AI chatbots fail to provide consistent safety for most mental health conditions. Only suicide and self-harm have reliable safeguards in place. Researchers call for better standards and transparency.
AI is having an 'Uber moment' in healthcare, becoming as essential as smartphones in hospitals. It's helping doctors diagnose diseases faster and more accurately, potentially making care more accessible and affordable.
Researchers found that large language models (LLMs) often overestimate their confidence when analyzing medical data. The study suggests new methods to help AI recognize what it doesn't know, improving reliability in healthcare applications.
Researchers developed an AI tool called ACIE that better handles complex medical records. It addresses key challenges in retrieving and understanding patient data across multiple documents. This could lead to more accurate and efficient medical diagnoses and treatments.
Researchers developed REVEAL++, an AI method that analyzes retinal fundus images and structured clinical narratives to predict Alzheimer's disease risk. The approach improves upon prior work by introducing differentiable phenotypic grouping for more precise risk stratification.
Researchers have developed a machine-learned comorbidity index that better predicts patient outcomes. Unlike older methods, it captures complex relationships between diseases and health risks.
Midjourney CEO David Holz just showed off the company's first hardware product and plans to build a San Francisco spa. Dubbed The Midjourney Scanner, it's an ultrasound-based full-body scanner that uses a ring of sensors to capture vertical images of the body.
Researchers used an OpenAI reasoning model to help diagnose rare diseases, identifying 18 new diagnoses in previously unsolved cases. This could revolutionize how doctors approach these complex conditions.
Pramaana Labs has secured $27 million to develop AI systems that can be formally verified for accuracy. This technology aims to eliminate errors in fields like law and drug discovery where mistakes can be costly.
Researchers created SpeechDx, a comprehensive benchmark for AI to analyze speech for health conditions. This tool could lead to earlier, more accurate medical diagnoses through simple voice recordings.