💡Exciting Announcement!

We are proud to introduce our next healthcare #WebAI application: Varicose Veins AI Consultant! 🩺

🧠 This custom hybrid AI is powered by a #Tensorflow.js neural network, enhanced with #Gemini Flash and #Deepseek.

🔍 What does it do?
✅ Provides individual risk estimation for developing varicose veins over a lifetime 🏥
✅ Delivers up-to-date insights on diagnosis, treatment, and prevention of the disease 📊💊
✅ Supports patients and healthcare professionals with the latest scientific knowledge 🔬📚

🌐 Try it out now! 👉

#AI #HealthTech #MedTech #MachineLearning #VaricoseVeins #MedicalAI


https://mlhs.ink/varicose/

🅡🅐🅖 𝙝𝙖𝙨 𝙗𝙚𝙚𝙣 𝙤𝙣𝙚 𝙤𝙛 𝙩𝙝𝙚 𝙢𝙤𝙨𝙩 𝙙𝙞𝙨𝙘𝙪𝙨𝙨𝙚𝙙 𝘼𝙄 𝙩𝙤𝙥𝙞𝙘𝙨 𝙞𝙣 𝙧𝙚𝙘𝙚𝙣𝙩 𝙢𝙤𝙣𝙩𝙝𝙨 — 𝙖𝙣𝙙 𝙛𝙤𝙧 𝙖 𝙜𝙤𝙤𝙙 𝙧𝙚𝙖𝙨𝙤𝙣.

At ML in Health Science, we’ve been building advanced retrieval-driven AI since ②⓪②④, focused not on generic documents but on the entire ecosystem of scientific evidence.

While classical 🅡🅐🅖 retrieves text chunks, our scientific multi-layer 🅡🅐🅖 protocol — ☸️𝗦𝗔𝗜𝗠𝗦𝗔𝗥𝗔 — goes further: it acts as a true AI agent that searches PubMed, Europe PMC, and Semantic Scholar, extracts structured data from every relevant study, and performs automated meta-analysis for each clinical question.

𝙏𝙝𝙚 𝙜𝙤𝙖𝙡 𝙞𝙨 𝙨𝙞𝙢𝙥𝙡𝙚:
turn global biomedical knowledge into clear, evidence-based insights that help improve patient outcomes.

We believe this is the next step for AI in healthcare — from retrieving information to understanding science.

Learn more: saimsara.com

#SAIMSARA #OSMA #ScienceArray #RAG #RetrievalAugmentedGeneration #AIinHealthcare #HealthTech #MedTech #EvidenceBasedAI #MetaAnalysis #ClinicalAI #DigitalHealth #MachineLearning

𝗥𝗔𝗚 𝘃𝘀. 𝗟𝗮𝗿𝗴𝗲-𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗟𝗟𝗠𝘀 𝗶𝗻 𝗖𝗹𝗶𝗻𝗶𝗰𝗮𝗹 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗦𝘂𝗽𝗽𝗼𝗿𝘁

How do we balance model precision with patient safety?

𝗥𝗔𝗚 enables the use of very large document corpora and can run on small local models (e.g., Gemma), keeping all data client-side and protecting patient privacy. However, RAG still depends on chunking, which may lose clinical nuance or skip key evidence.

𝗟𝗮𝗿𝗴𝗲-𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗟𝗟𝗠s such as #Gemini Google 2.5 or Grok 4 can ingest entire clinical guidelines directly, preserving full logic and improving precision. But these models run server-side, which introduces potential exposure risks for sensitive patient information.

If you’re exploring how to build your own clinical decision support LLM and are unsure whether RAG or full-context ingestion is the right path, feel free to reach out: mlhs.ink/Service

We help bridge clinical workflows with practical AI development.

#ClinicalAI #DigitalHealth #AIinMedicine #HealthTech