Neo4j’s Post

Did you know that medical error is the 3rd leading cause of death in the USA and drug adverse effects are the 4th? In this session, Roland presents the Physicians’ Brain Digital Twin, a holistic biomedical #KnowledgeGraph in a Neo4j Database constructed by integrating 9 biomedical knowledge graphs. Find out how this Knowledge Graph works to achieve: 0️⃣ zero medical errors 0️⃣zero patient harms 0️⃣zero medical wastes 0️⃣zero medical abuses 0️⃣zero medical frauds. #TBT to a #NODES2023 presentation: https://lnkd.in/dUyBK6wh

🩺 Enhancing Healthcare Diagnostics with Multimodal RAG Systems With Qdrant & Gemini, you can transform the way healthcare professionals approach diagnostics by combining *both* text and image data. 🖼 🔠 In this article, Pragnesh Prajapati shows how to create a high-performance diagnostic system using hybrid search and multimodal embeddings. Highlights include: ➡ Hybrid search with both text (e.g., medical records) and image data (e.g., X-rays, MRIs) using dense and sparse vectors. ➡ Generate multimodal embeddings from text and image data. ➡ Understand more about how the latest advancements in AI are reshaping the future of medical diagnostics. 🔗 Explore the full guide: https://buff.ly/4cTvMag

When work gets fun... Settled in to work on my AAMI Exchange slides on how Clinical Engineers and HTM can assess AI- and ML-enabled technologies through a health equity lens, and here comes the best infographic I've seen lately on how to think about bias throughout the product lifecycle! Man, do I love a good infographic ❤️ Haven't registered yet? Want to hang out at 7:15 on a Saturday morning in Phoenix? Head to https://lnkd.in/ejR9nUBa #clinicalengineers #artificialintelligence

This framework serves to highlight the need for continuous consideration of equity throughout the development and implementation of AI and machine learning-enabled medical devices. The figure presents a framework for the Total Product LifeCycle equity expansion. It details various phases of the lifecycle—such as conception, design, development, validation, access, and monitoring—highlighting the potential for either positive or negative impact on health equity at each stage. ✍🏻 Abràmoff, M.D., Tarver, M.E., Loyo-Berrios, N. et al. Considerations for addressing bias in artificial intelligence for health equity. npj Digit. Med. 6, 170 (2023). DOI: 10.1038/s41746-023-00913-9

A study investigating the impact on scan time reduction and image quality, when combining compressed sensing with the latest deep learning-based image reconstruction approach (CS-SuperRes).” The new approach was able to reduce scan times from about 11:01 minutes down to 4:46. Terzis et al.

Also contained within this article are other advancements in AI applied to health care.

🔬 Doctors are buzzing about the potential of artificial intelligence in revolutionizing the field of medicine! According to the American Medical Association, a majority of physicians believe AI can improve workflow efficiency, enhance patient diagnosis, and reduce administrative burdens. However, there are also concerns about the impact on patient-doctor interactions and privacy. What are your thoughts on AI's role in medicine? Share your thoughts below! American Medical Association, December 2023 #AIinMedicine #FutureofHealthcare 🤖💉🏥

Today Truveta announced the availability of expanded concepts from clinical notes – including family history, medication details, severity of symptoms, and reasons for switching medications – plus millions of de-identified medical images to enable scientifically rigorous research. Go Truveta!   One of the biggest challenges of medical research has been the critical details about a patient’s health that are locked away in unstructured clinician notes. Truveta receives all notes written during a patient’s care, then extracts & cleans the data with our expert-led AI. Combined with the availability of millions of medical images integrated with a patient’s health record, Truveta provides the most complete, timely, and clean electronic health record data.   There is so much potential here for life-saving discoveries and innovation. https://tr.vet/3w9B1DI

In the field of virtual care, the integration of RAG with LLMs is changing medical diagnostics. Today, we're witnessing a great improvement in disease diagnosis efficiency using EHRs. Such an integration is vital, primarily as it addresses previous limitations where encoding physician knowledge into computational rules was error-prone and labor-intensive. The synergy between LLM and RAG effectively reduces the volume of text processed, relieving medical experts of the overwhelming task of sifting through vast amounts of data. This focus on the most relevant and accurate information is driving advancements in health technology. Intrigued by the potential of LLM-based RAG architecture to transform healthcare? Join us as we delve deeper into this topic and explore its implications for the future of medical diagnostics! #AI #GenAI #RAG #RAGsystems #LLM #MachineLearning #DigitalHealth

With the potential benefits AI can bring to healthcare, new challenges and problems could also arise that biomedical technologists and engineers must overcome. Read Jacob Fitzpatrick's new blog on MedWrench to see what those challenges could be: https://bit.ly/3XSbqKi