121 stories tagged Healthcare · page 2 of 6
Google DeepMind's AlphaFold 3 is helping scientists design new medicines faster by predicting protein structures and interactions. This breakthrough could accelerate treatments for Alzheimer's, cancer, and other hard-to-target diseases.
Researchers introduced GraphDx, a multi-agent AI framework that uses a Medical Diagnosis Knowledge Graph to balance diagnostic accuracy with resource costs, reducing unnecessary testing in sequential medical diagnosis.
Researchers introduced Cura 1T, a healthcare-specialized AI model trained via a human-gated self-evolution loop to handle patient consultations, clinical reasoning over text and images, interactive diagnosis, and EHR tool use without degrading performance across tasks.
A new arXiv study proposes converting all patient data—clinical narratives, vital signs, lab values, and comorbidities—into a single natural language sequence and fine-tuning a pretrained LLM, eliminating the need for task-specific fusion architectures in clinical prediction.
Researchers introduced Safe-Psych, a sequential benchmark that evaluates how well large language models handle evolving diagnostic uncertainty in clinical psychiatry. Unlike existing medical benchmarks that assume complete information upfront, Safe-Psych tests whether models request clarification or abstain when data is insufficient.
A new study evaluated agentic LLM systems on 72 real breast cancer cases using 1,147 rubrics generated via Asymmetric Information Rubric Generation (AIRG). Seven pipelines were compared, with results suggesting AI can assist but not replace human oncologists.
Researchers developed a benchmark to evaluate AI summaries of clinical trials, ensuring they are accurate for doctors, patients, and insurers. This helps prevent misleading information in high-stakes medical decisions.
Researchers developed a new AI system that explains medical diagnoses using a structured argumentation model. This approach makes AI recommendations more transparent and trustworthy for doctors.
Researchers introduced CLIR-Bench, a new benchmark to test AI models on answering clinical questions from irregular, sparse medical data. This could lead to better AI tools for doctors and hospitals.
Researchers created MedRealMM, a real-world benchmark for medical AI that includes real patient-doctor conversations and medical images. This could lead to more accurate and reliable AI tools for online healthcare consultations.
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.