Our latest research from Google Research and Google DeepMind explores the potential of Project AMIE, our conversational medical AI system, to assist clinicians in both primary and specialty care. In studies simulating complex cardiology and oncology cases with Stanford University School of Medicine and Houston Methodist, AMIE demonstrated promising performance. As a next step in this journey, we are collaborating with Beth Israel Deaconess Medical Center for safe, prospective real-world validation. 🔗 Read the full blog post to learn more: https://goo.gle/4gLFuhd
Great news! Why not collaborate with several sites for the real world validation however is what I'm wondering Even better if arrange it so the varying centers are blind to the other centers using it Doing so would 1) accelerate confidence and conviction with rollout - by having more numbers, more dynamics, more minds more frameworks of doing things - not to mention supports confidence on the whole turf of biases and such things which some take very seriously 2) generate feedback ideas insights especially if frameworks to churn ideas and insights and continuous learning and improvement - for many mechanisms having multiple real world validation implementations as blind as possible to eachother would all but certainly lead to more pearls more robustness more versatility and clarities as rather than one center and subgroup of people brainstorming and with eagle eyes looking for pitfalls shortcomings avenues for improvement so forth have multiple These sorts of things often gain very different feedback and bends etc with such rollouts - so so long as ensuring top scientific acumen wind up with likely a lot of value to integrate and work with wouldn't have otherwise Then do the universal rollouts much stronger on grounds
That’s interesting ! That’s exactly like our KAI clinical and patient assistant we are developing at Kento Health !
Imran Kausar
3w
This is an exciting step forward in the application of AI to specialist healthcare. AMIE’s focus on bridging the gap between generalist AI and domain-specific expertise demonstrates the power of technological convergence in healthcare. Real-world validation, as highlighted here, is crucial to ensuring these innovations deliver meaningful outcomes in diverse and complex environments. The move towards specialist-level capabilities in AI not only expands its potential impact but also raises important considerations about integration into existing care pathways, regulatory oversight, and equitable access. This work sets a strong foundation for what the future of AI in healthcare could look like—delivering precision and scale while remaining patient-centered. Congrats Google Health teams!
Juan Lavieri
4d
Thanks for sharing Our AI powered solution could help reduced patient denials, as we offered a 95% accuracy on medical coding leading to clean codes and higher reimbursement rates. Here is a brief overview overview of MeMu. MeMu (Medical Multimodal) is an innovative SaaS and SDK platform designed to streamline medical documentation and enhance healthcare workflows. MeMu automates key tasks such as voice-to-text transcription, ICD-10/HCPCS coding, medical entities extraction, drug interaction checks & alerts, multilingual summarizations and electronic health record (EHR) updates. By integrating seamlessly into existing systems, MeMu reduces administrative burdens, optimizes revenue cycles, and improves patient safety through advanced AI-powered solutions. Its multilingual capabilities and secure cloud infrastructure make it an ideal choice for healthcare providers worldwide. https://memu.life/site/
Conversations may also be of different types. So, maybe category based development could improve adoption rate for some, and for others it could remain a work in progress.
The future of healthcare might just be about blending expert intuition with AI precision.
How can we try in frontline ?
It's interesting. Thanks for sharing 👍
Bushra Singh
2w
👏👏👍Great initiative 🤗
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3wExcellent Google DeepMind !!. Conversational bots capable of contextualizing and providing interoperable access to clinical data sources will mark a turning point in addressing one of the greatest challenges in healthcare today: the fragmentation of information and hyperspecialization, which can often lead to decontextualization. Incorporating a boosting feature that integrates contextual population data and micro-habit information could provide valuable insights into specific pathologies, such as metabolic disorders, and contribute to the design of targeted interventions. This type of technology represents cutting-edge innovation, enabling the intelligent handling of biomedical and social context data. Congrats!! 🙌🏻