Clinical research, health, and healthcare systems.
Researchers developed an algorithm that analyzes brain networks to help diagnose Alzheimer's and Lewy body dementia, which have similar symptoms but require different treatments.
Researchers created a new Arabic language test called ALPS to check if AI language models truly understand complex linguistic and pragmatic aspects of the Arabic language, beyond just literal translations.
Researchers developed a new AI model to predict survival odds for lung cancer patients using medical scans, gene data, and other records - this could help doctors make more informed treatment decisions.
Researchers evaluated a promising medical imaging technique called Mamba, finding it could offer fast, efficient processing - but its reliability under real-world conditions needs further study.
A new AI ontology helps forensic experts determine the age of individuals based on dental scans, which is crucial for legal decisions involving undocumented minors.
Researchers say current methods for evaluating AI algorithms on ECG data are flawed, risking unreliable progress that may not translate to real-world clinical benefit.
Researchers developed a new AI system that can help diagnose lung diseases more accurately by combining different deep learning models. This could lead to faster and more reliable lung disease screening, which could benefit many patients.
Researchers developed a machine learning system that can make trustworthy decisions using incomplete medical data, which could improve healthcare and diagnosis for patients.
Researchers developed an AI agent that can ask follow-up questions to diagnose medical conditions, which could improve the accuracy and efficiency of clinical decision-making.
Researchers developed a scalable framework to evaluate how well AI language models can analyze health data and provide personalized medical insights, which could improve patient care and public health monitoring.
New AI can accurately spot COVID-19 lung damage in CT scans, which could help doctors diagnose and monitor the disease more quickly.
Researchers have developed a new live benchmark system called LiveClin that aims to provide a more reliable way to evaluate medical language models, by addressing issues like data contamination and knowledge obsolescence that can inflate scores on static benchmarks.
A new AI model can help dentists identify jaw cysts in dental X-rays, which could improve early diagnosis and treatment of this common dental issue. However, the study is preliminary and more research is needed to validate the approach.
Researchers developed a system to automatically detect weaknesses in scientific papers, which could help improve the quality and reliability of published research.
Researchers developed an algorithm that analyzes brain networks to help diagnose Alzheimer's and Lewy body dementia, which have similar symptoms but require different treatments.
Researchers created a new Arabic language test called ALPS to check if AI language models truly understand complex linguistic and pragmatic aspects of the Arabic language, beyond just literal translations.
Researchers developed a new AI model to predict survival odds for lung cancer patients using medical scans, gene data, and other records - this could help doctors make more informed treatment decisions.
Researchers evaluated a promising medical imaging technique called Mamba, finding it could offer fast, efficient processing - but its reliability under real-world conditions needs further study.
A new AI ontology helps forensic experts determine the age of individuals based on dental scans, which is crucial for legal decisions involving undocumented minors.
Researchers say current methods for evaluating AI algorithms on ECG data are flawed, risking unreliable progress that may not translate to real-world clinical benefit.
Researchers developed a new AI system that can help diagnose lung diseases more accurately by combining different deep learning models. This could lead to faster and more reliable lung disease screening, which could benefit many patients.
Researchers developed a machine learning system that can make trustworthy decisions using incomplete medical data, which could improve healthcare and diagnosis for patients.
Researchers developed an AI agent that can ask follow-up questions to diagnose medical conditions, which could improve the accuracy and efficiency of clinical decision-making.
Researchers developed a scalable framework to evaluate how well AI language models can analyze health data and provide personalized medical insights, which could improve patient care and public health monitoring.
New AI can accurately spot COVID-19 lung damage in CT scans, which could help doctors diagnose and monitor the disease more quickly.
Researchers have developed a new live benchmark system called LiveClin that aims to provide a more reliable way to evaluate medical language models, by addressing issues like data contamination and knowledge obsolescence that can inflate scores on static benchmarks.
A new AI model can help dentists identify jaw cysts in dental X-rays, which could improve early diagnosis and treatment of this common dental issue. However, the study is preliminary and more research is needed to validate the approach.
Researchers developed a system to automatically detect weaknesses in scientific papers, which could help improve the quality and reliability of published research.