Benjamin Senst@bsenst

Unlock a new era of healthcare guidance with GuidelineGuru, an innovative application powered by generative artificial intelligence. Designed to be a comprehensive resource, GuidelineGuru offers access to a vast repository of almost 100 national health guidelines, covering a diverse range of conditions and medical scenarios. Navigating guidelines has become more and more complex as the guideline landscape has grown. Accessing and interpreting national health guidelines is now easy and efficient. The platform streamlines the process, offering quick access to the collection of national health guidelines. Stick to evidence-based practices at your point of care at your own pace. Be aware that the application is in a testing stage and for demonstration purpose only.

Working with medical notes puts a significant strain on healthcare professionals. The volume of findings is constantly increasing, driven by advances in medical technology and an aging population. These notes encompass a wide range of information, including clinical assessments, treatment plans, diagnostic test results, and progress reports. Navigating through this wealth of data demands meticulous attention and consumes valuable time that could be allocated to direct patient care. Medical findings are extensive, diverse, and complex, reflecting the intricacies of human health and the multifaceted nature of medical conditions. The diversity poses a challenge for healthcare workers, who must assimilate and synthesize this information to provide the best possible care. As a result, healthcare professionals spend a significant amount of time searching through these findings. They meticulously comb through records, striving to extract the most relevant information while ensuring nothing critical is overlooked. Yet, despite their best efforts, the sheer volume and complexity of medical notes can lead to instances where crucial details may inadvertently go unnoticed. This is where large language models step in as powerful tools to augment healthcare workflows. With their ability to rapidly process and comprehend vast amounts of text, they can swiftly analyze medical notes and extract pertinent information. By doing so, they assist in streamlining the information retrieval process, allowing healthcare professionals to allocate more time to direct patient interactions and clinical decision-making.

Evernote is used by 250 million people worldwide to collect ideas, document meetings, and much more. It runs as a local application or in the cloud and has preceded comparable applications by the big tech companies. Once the note collection has grown over the years, it can become cumbersome to keep an overview and refer to past notes. Large language models offer help to improve search functions and build knowledge from old notes. With the dawn of open-source large language models such as Falcon by the Technology Innovation Institute, new possibilities have been opened. Since these models can be run on less powerful customer hardware, private Evernote data can now be processed, preserving privacy.

Clinical reasoning is at the center of medical care. History taking and physical examination findings provide the greatest part to identify a preliminary list of diagnoses. But clinical reasoning suffers from bias. Supporting reasoning through large language models might help healthcare providers in diagnostic thinking. As initial step a list of possible diagnoses is created from a symptoms. In the future chained prompts might additionaly produce suggestions for further diagnostic workup based on the list of differential diagnosis or provide key findings that help to differentiate between the preliminary list of diseases.

Build with Phi-2, Microsoft’s new language model powered by impressive NLP which gained recognition from over 300k users 🔥 🕒 24 hours to complete the challenge 💡 Implement any idea that uses Phi-2 in its core 🤝 Take part alone or form a team with other participants 🚀 The final project can be used for your portfolio, and the startup application to the Slingshot accelerator

At this event, you are challenged to create your own product with the Mixtral 8x7B LLM that overperformed GPT 3.5, and Llama 2 70B on most of the benchmarks. 🕒 24 hours to complete the challenge 💡 Implement any idea that uses Mixtral 8x7B in its core 🤝 Take part alone or form a team with other participants 🚀 The final project can be used for your portfolio, and the startup application to the Slingshot accelerator

Step into the future of AI: Experience and innovate with the cutting-edge Gemini AI model. First-hand Experience: Dive into the latest AI technology with Gemini AI, right after its release. Elevate your AI knowledge and coding prowess in a stimulating and supportive atmosphere. Team up with like-minded innovators and tech enthusiasts. You can work solo, or in a team of up to 6 members. Enhance your LLM apps with TruLens: Use sophisticated evaluation and performance tracking tools for superior output quality. Note: Dates may change if the Gemini AI model release is postponed by Google Cloud.