TechNation的动态

We can learn a lot about patients with #GA through clinical notes, images, and ICD-10 codes. In our latest white paper, read how #ML methods are helping us gain a better understanding of this advanced form of #AMD: https://hubs.la/Q02qpdV50

Med-PaLM, a version of PaLM tuned for the medical domain. Med-PaLM was the first to obtain a “passing score” (>60%) on U.S. medical licensing-style questions. This model not only answered multiple choice and open-ended questions accurately, but also provided rationale and evaluated its own responses. Recently, next iteration, Med-PaLM 2, consistently performed at an “expert” doctor level on medical exam questions, scoring 85%. This is an 18% improvement from Med-PaLM’s previous performance and far surpasses similar AI models.

While our e-Mailing service 'Telli-Browse' is on the point of being launched and while we will do a follow-up with our solution for the storage of big medical data 'Telli Box' and our Operating System 'Cameleon', we have something new up our sleeves. Currently, we are researching possibilities of how assessing, reading, interpreting and storing medical data coming from devices like CTs, mass spectrometers and the like. Medical data coming from analyzation devices are often difficult to assess as they are not per se printed on a paper support. So far, the lack of MRI schedules in Luxembourg is fought against by buying new devices or extending running time. We believe that if we could accelerate the reading process of the data, good will already have been done. We are excited about finding out what the outcome will be. https://lnkd.in/edv2t-hm

The game's changing for medical devices with machine learning.?Regulators are stepping up to ensure patient safety with new guidelines for handling these evolving technologies. This article by MIP’s Marie Rigley breaks down the 5 key principles you need to know about "predetermined change control plans" (PCCPs). https://lnkd.in/gChfSjBv #PCCP #MachineLearning #MedTech #MedicalDevices

Learn how Labelata can help you develop FDA cleared algorithms to analyze medical imaging data. Thanks Yuval Duchin and David Golan from Viz.ai, for the great collaboration! #ComputerVision #MedicalAI #FDA

This work was created to automate the annotation of medical images by pre-segmentation, I think it is time to continue in this line. https://lnkd.in/eCRRuqz3

?? Attention AI/ML developers, manufacturers and RA interested scrollers!!! New IMDRF document on "Good machine learning practice for medical device development - Guiding Principles" is now open for public consultation. We have been trying our very best to ensure that the most essential parts of creating a solid machine learning-enabled medical device are captured, but in case we missed anything, please let us know using the commenting template. Consultation closes on the 30th of August, so make sure to submit your comments before and enjoy the summer. ?? ??

Check out our blog post about "How AI Enhances Data Analysis in Medical Device Safety Systems" https://wix.to/h8rA6SS #newblogpost#Datacreds #Crypta #DrugSafetyDatabase

Transform Healthcare with Computer Vision! Discover how Computer Vision (CV) is revolutionizing healthcare—from precise medication delivery to surgical accuracy and patient monitoring. Interested in integrating cutting-edge Computer Vision technology into your healthcare practice? Book a session with us https://lnkd.in/dRxgv6cF to explore how we can help you innovate and enhance patient care! #HealthTech #ComputerVision #MedicalInnovation

With this providers can choose their preferred speech engine, entity recognition engine, or personalized Large Language Model anytime. This level of transparency puts control directly into the hands of providers. #HealthcareInnovation #PatientCare #CoreMobile"