University Of Pittsburgh

Children of mothers who took newer antiseizure medications during pregnancy showed no worse neurodevelopmental outcomes at age 6, according to a long-term NIH-funded study published in JAMA Neurology. The study found no differences in language scores between children exposed to the medications and those who were not, with most mothers taking lamotrigine, levetiracetam, or a combination. High doses of folate during pregnancy were associated with better cognitive and behavioral outcomes.

NIH researchers developed TrialGPT, an AI framework using large language models to streamline clinical trial matching on ClinicalTrials.gov, achieving nearly the same accuracy as human clinicians. TrialGPT analyzes patient summaries to identify eligible trials, reducing clinician screening time by 40% while maintaining accuracy. The tool relies on OpenAI’s GPT-3.5 and GPT-4 models and will be further assessed for performance and fairness in real-world settings.

33.1% of adults eligible for GLP-1 receptor agonist treatment for weight loss were excluded from clinical trials due to conditions like depression and hypertension, highlighting the need for screening and real-world evidence in these populations.

NIH developed TrialGPT, an AI algorithm matching potential volunteers to relevant clinical trials on ClinicalTrials.gov, aiding clinicians in navigating trial options efficiently. TrialGPT processes patient summaries, identifies eligible trials, and explains eligibility criteria, reducing clinician screening time by 40% while maintaining accuracy. The tool aims to improve clinical trial enrollment and accelerate medical research progress.

NIH developed AI algorithm TrialGPT to match volunteers to relevant clinical trials on ClinicalTrials.gov, streamlining the process and improving enrollment. TrialGPT uses large language models to process patient summaries, identify eligible trials, and explain eligibility criteria, achieving nearly the same accuracy as human clinicians while saving time. The tool aims to enhance clinical trial recruitment and reduce barriers for underrepresented populations.

NIH introduces TrialGPT, an AI algorithm matching patients to clinical trials on ClinicalTrials.gov, aiming to accelerate research and improve enrollment. Developed by NIH's NLM and NCI, TrialGPT uses LLMs to process patient data, exclude ineligible trials, and rank eligible studies. It achieved near-human accuracy in a benchmarking study and demonstrated potential to save clinicians 40% of screening time.

NIH researchers developed TrialGPT, an AI algorithm that efficiently matches potential volunteers to relevant clinical trials on ClinicalTrials.gov, potentially enhancing clinical trial enrollment and medical research advancements.

NIH developed AI algorithm TrialGPT matches potential volunteers to relevant clinical trials on ClinicalTrials.gov, improving enrollment efficiency and accuracy. TrialGPT processes patient summaries, identifies eligible trials, and explains eligibility criteria, saving clinicians 40% screening time. Selected for Director's Challenge Innovation Award to further assess real-world performance.