BrainMRI AI represents a paradigm shift in the diagnosis of brain tumors, combining Vectara's semantic search with the webkitSpeechRecognition API to deliver accurate, voice-activated support. Through this integration, radiologists can dictate their observations, which are then matched against an extensive database of cases and relevant literature, dramatically accelerating the diagnosing process. BrainMRI AI revolutionizes case study reviews by merging an AI-assisted tool's rapid data retrieval with the power of voice. Radiologists' spoken observations trigger an in-depth search, instantly compiling necessary documents and drawing up a list of pertinent cases and literature. This streamlines the diagnostic process and fosters cross-departmental collaboration, making BrainMRI AI a crucial ally in medical imaging and patient care. Key features include: Hands-free speech-to-text, keeping radiologists focused on imaging. Swift, semantic database scans for case-relevant data. Quick access to comparable MRI cases for immediate analysis. A portal to medical research, deepening insights into unique MRI observations. Streamlined case reviews by presenting pertinent literature. A study aid for medical students with access to real MRI case analyses. Our technology stack, including NextJS, TailwindCSS, and Shadcn, creates a responsive and intuitive user interface, while Vectara API's reranking and file storage capabilities ensure that the most pertinent information is always at hand. With the inclusion of a summarization feature that cites relevant sources, our application not only finds but contextualizes critical information, reinforcing its credibility. In bridging the gap between technological innovation and medical expertise, BrainMRI AI is set to become an essential component in the future of radiological diagnosis and patient outcome enhancement.Category tags:
"- Good slides. Does a good job framing the problem, and citing key statistics early on is good. Use case is an important and valuable one - Good live demo in the presentation. - It would be good to have seen which data was used in the application, as that is what determines how useful the tool can be. - I like the approach of providing the audio entry modality. - If you are passionate about this space and about the usage of LLMs and GenAI in healthcare, check out SonoSim. They are very innovative in this area, but with ultrasound vs radiology."
Head of Field Engineering
"Very innovative solution, great use of LLMs and RAG architecture. I would love to see if you can combine this computer vision. Also need to focus on a little on the business side of the solution. "
Machine Learning Engineer