Articles tagged with Generative Ai
A Cornell University-led research team found that using a technique called 'cultural prompting' reduces bias in large language models like ChatGPT for over 100 countries. The approach involves specifying a cultural identity directly in prompts, adapting to different cultural norms and values.
A study by OHSU researchers found that generative AI models like ChatGPT outperform human students in knowledge assessments for a biomedical and health informatics course. The results raise concerns about cheating but also highlight the need to assess student learning beyond basic knowledge.
A new study from Mass General Brigham researchers found that large language models demonstrated no racial or gender discrimination in opioid treatment recommendations. The results suggest that these AI models have the potential to reduce bias and improve health equity in pain management, which is a critical area where disparities exist.
Researchers at Purdue University found that autonomous vehicles can interpret and respond to commands from passengers using large language models like ChatGPT. This technology allows the vehicle to personalize its driving to a passenger's satisfaction and take into consideration traffic rules, road conditions, and weather.
Rice University researchers developed ElasticDiffusion, a method that separates local and global signals to create non-square aspect ratio images without visual imperfections. The new approach can improve consistency and realism in AI-generated images, but still requires significant computational power.
A pathbreaking study finds that conversations with AI models can significantly reduce conspiracy theory beliefs, particularly among those deeply entrenched in their views. The study, involving over 2,000 participants, showed a 20% reduction in belief and an additional 1 in 4 disavowing the conspiracy altogether.
Generative AI is revolutionizing oncological imaging by expanding datasets and improving image quality. This technology enables predictive oncology and personalized cancer screening, offering new hope in the fight against cancer.
A machine-learning algorithm called xFakeSci has been developed to detect AI-generated scientific articles, with a success rate of nearly 94%. The tool analyzes word patterns and bigrams to distinguish between real and fake papers, highlighting the need for comprehensive detection methods as AIs become increasingly sophisticated.
The study found that ChatGPT-4 Vision performed well on text-based radiology exam questions but struggled with image-related questions, achieving an accuracy of 65.3%. The model's performance varied across subspecialties and images, with limitations in interpreting certain types of radiologic images.