20 stories tagged Diagnosis
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 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.
Researchers at CNPO developed G-SHARE, a guideline-based structured reasoning framework that diagnoses human-factor events in nuclear power plants with greater reliability and logical consistency than existing AI approaches.
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 developed a new AI approach that mimics how doctors gather evidence to make diagnoses. This method could make AI medical tools more accurate and reliable.
University of Washington researchers developed Ekka, a system that automatically detects silent errors in LLM outputs. This could make AI systems more reliable for everyday users.
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.
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 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.
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.
Researchers developed PathoSage, an AI system designed to improve medical diagnoses by reducing errors in analyzing tissue samples. It separates knowledge gathering from decision-making to avoid conflicting evidence.
Researchers developed a new AI system that combines traditional data analysis with language models to predict heart disease risk. This hybrid approach could make early diagnosis faster and more accurate.
Boston Children’s Hospital is using AI from OpenAI to help diagnose rare diseases faster. The technology has already helped identify over 40 cases that might have been missed otherwise. This can make a huge difference for families searching for answers.
A new study shows that AI models with strong medical knowledge can abandon correct diagnoses when pressured in conversations. Researchers developed a test to measure how well AI maintains accurate beliefs under stress.
Researchers developed a new AI system to better identify language patterns linked to autism. This could help diagnose social language disorders more accurately in the future.
Researchers have developed ClinicBot, an AI chatbot designed for medical professionals that prioritizes accurate, guideline-based answers. Unlike other AI tools, it avoids made-up information and provides verifiable citations for its responses.
A Harvard study found that certain AI models provided more accurate diagnoses in emergency room cases than human doctors. This suggests AI could become a valuable tool in fast-paced medical settings.
A Harvard-led study found AI systems diagnosed emergency cases more accurately than human doctors. The results could revolutionize emergency room efficiency and patient outcomes.
OpenAI's ChatGPT is being adopted by healthcare professionals for diagnosis, documentation, and patient care. The tool is designed to be HIPAA-compliant, ensuring secure handling of sensitive medical data.
Researchers propose a new approach to sequential clinical diagnosis using uncertainty-guided latent diagnostic trajectory learning. This method addresses the challenge of learning effective diagnostic trajectories under uncertainty.