🧠 One Year Later — This VR Alzheimer’s Project Is Thriving
Exactly one year ago, ML in Health Science published an article on a visionary project led by Jason P. Rothberg and developed by Aboot Studios (Xavatar) — a virtual reality simulation placing users inside the mind of a person with Alzheimer’s disease.

We are pleased to see how this project continues to evolve and gain traction. Its unique approach, combining immersive VR with clinical insights offers medical students an unparalleled educational experience that fosters deep empathy and understanding of cognitive disorders.

A year has passed — the project is alive, growing, and more relevant than ever.


🔗 https://www.kite-uhn.com/news/kite-creates-cohort


#Alzheimers #VRinMedicine #MedicalEducation #DeSci #MLinHealthScience #Geriatrics #Neurodegeneration #DigitalHealth #Xavatar #AbootStudios

𝗣𝗶𝘁𝗳𝗮𝗹𝗹𝘀 𝗼𝗳 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗥𝗔𝗚 𝗮𝗻𝗱 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜
(𝑆𝑢𝑚𝑚𝑎𝑟𝑦 𝑜𝑓 2 𝑦𝑒𝑎𝑟𝑠 𝑜𝑓 ☸️𝑆𝐴𝐼𝑀𝑆𝐴𝑅𝐴 𝑑𝑒𝑣𝑒𝑙𝑜𝑝𝑚𝑒𝑛𝑡)

𝗠𝗼𝘀𝘁 𝗥𝗔𝗚 𝗮𝗻𝗱 𝗮𝗴𝗲𝗻𝘁 𝗲𝗿𝗿𝗼𝗿𝘀 𝗰𝗼𝗺𝗲 𝗳𝗿𝗼𝗺 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 — 𝗻𝗼𝘁 𝗺𝗼𝗱𝗲𝗹𝘀.
1. 𝘏𝘦𝘢𝘷𝘺 𝘱𝘳𝘰𝘮𝘱𝘵𝘴 𝘥𝘳𝘢𝘪𝘯 𝘢𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯
2. 𝘓𝘢𝘳𝘨𝘦 𝘣𝘢𝘵𝘤𝘩𝘦𝘴 𝘢𝘮𝘱𝘭𝘪𝘧𝘺 𝘮𝘪𝘴𝘵𝘢𝘬𝘦𝘴
3. 𝘍𝘢𝘴𝘵 𝘮𝘰𝘥𝘦𝘭𝘴 𝘰𝘧𝘵𝘦𝘯 𝘴𝘬𝘪𝘱 𝘥𝘪𝘴𝘤𝘪𝘱𝘭𝘪𝘯𝘦

These are not bugs — they are part of the learning curve.
This is exactly why ☸️𝐒𝐀𝐈𝐌𝐒𝐀𝐑𝐀 𝐜𝐚𝐧 𝐰𝐨𝐫𝐤 𝐬𝐭𝐚𝐛𝐥𝐲 𝐰𝐢𝐭𝐡:
1. 𝘵𝘩𝘰𝘶𝘴𝘢𝘯𝘥𝘴 𝘰𝘧 𝘳𝘦𝘧𝘦𝘳𝘦𝘯𝘤𝘦𝘴,
2. 𝘮𝘪𝘭𝘭𝘪𝘰𝘯𝘴 𝘰𝘧 𝘵𝘰𝘬𝘦𝘯𝘴,
3. 𝘰𝘳𝘤𝘩𝘦𝘴𝘵𝘳𝘢𝘵𝘦𝘥 𝘴𝘺𝘴𝘵𝘦𝘮 𝘰𝘧 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵𝘴.

Thinking about building your own agentic AI — wisely?
Feel free to contact us. We’re happy to help.

🔗 mlhs.ink/Service/

#RAG #AgenticAI #LLM #AIEngineering #SAIMSARA #MLinHealthScience #AIArchitecture

𝐒𝐲𝐧𝐭𝐡𝐞𝐭𝐢𝐜𝐚 𝐢𝐬 𝐥𝐢𝐯𝐞 — 𝐚𝐮𝐠𝐦𝐞𝐧𝐭 + 𝐜𝐥𝐞𝐚𝐧 𝐦𝐞𝐝𝐢𝐜𝐚𝐥 𝐢𝐦𝐚𝐠𝐞𝐬 𝐢𝐧 𝐨𝐧𝐞 𝐬𝐭𝐞𝐩.

If you build medical computer vision, you know the pain: real-world images often come with overlays, text/IDs, UI frames, borders, and other artefacts that make datasets noisy and expensive to clean.

𝐒𝐲𝐧𝐭𝐡𝐞𝐭𝐢𝐜𝐚 𝐡𝐞𝐥𝐩𝐬 𝐲𝐨𝐮:
- 𝑅𝑒𝑚𝑜𝑣𝑒 𝑐𝑜𝑚𝑚𝑜𝑛 𝑎𝑐𝑞𝑢𝑖𝑠𝑖𝑡𝑖𝑜𝑛 𝑎𝑟𝑡𝑒𝑓𝑎𝑐𝑡𝑠 (𝑎𝑛𝑛𝑜𝑡𝑎𝑡𝑖𝑜𝑛𝑠/𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑚𝑒𝑛𝑡𝑠, 𝑟𝑒𝑎𝑑𝑎𝑏𝑙𝑒 𝑡𝑒𝑥𝑡, 𝑈𝐼/𝑚𝑜𝑛𝑖𝑡𝑜𝑟 𝑓𝑟𝑎𝑚𝑒𝑠, 𝑏𝑙𝑎𝑐𝑘 𝑏𝑜𝑟𝑑𝑒𝑟𝑠/𝑏𝑎𝑐𝑘𝑔𝑟𝑜𝑢𝑛𝑑)
- 𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑒 𝑟𝑒𝑎𝑙𝑖𝑠𝑡𝑖𝑐 𝑎𝑐𝑞𝑢𝑖𝑠𝑖𝑡𝑖𝑜𝑛-𝑙𝑒𝑣𝑒𝑙 𝑣𝑎𝑟𝑖𝑎𝑛𝑡𝑠 𝑤ℎ𝑖𝑙𝑒 𝑝𝑟𝑒𝑠𝑒𝑟𝑣𝑖𝑛𝑔 𝑡ℎ𝑒 𝑜𝑣𝑒𝑟𝑎𝑙𝑙 𝑣𝑖𝑠𝑢𝑎𝑙 𝑖𝑑𝑒𝑛𝑡𝑖𝑡𝑦 𝑎𝑛𝑑 𝑓𝑖𝑛𝑑𝑖𝑛𝑔𝑠 — 𝑓𝑜𝑟 𝑎𝑢𝑔𝑚𝑒𝑛𝑡𝑎𝑡𝑖𝑜𝑛, 𝑐𝑙𝑎𝑠𝑠 𝑏𝑎𝑙𝑎𝑛𝑐𝑖𝑛𝑔, 𝑎𝑛𝑑 𝑠𝑡𝑟𝑒𝑠𝑠-𝑡𝑒𝑠𝑡𝑖𝑛𝑔 𝑝𝑖𝑝𝑒𝑙𝑖𝑛𝑒𝑠.

𝐒𝐮𝐩𝐩𝐨𝐫𝐭𝐞𝐝 𝐦𝐨𝐝𝐚𝐥𝐢𝐭𝐢𝐞𝐬:
🩻 𝑅𝑎𝑑𝑖𝑜𝑙𝑜𝑔𝑦: 𝑋𝑅, 𝐴𝑛𝑔𝑖𝑜/𝐷𝑆𝐴, 𝑀𝑅𝐼, 𝑈𝑙𝑡𝑟𝑎𝑠𝑜𝑢𝑛𝑑, 𝐶𝑇 (𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦-𝑝𝑟𝑒𝑠𝑒𝑟𝑣𝑖𝑛𝑔)
🔬 𝑀𝑖𝑐𝑟𝑜𝑠𝑐𝑜𝑝𝑦: ℎ𝑖𝑠𝑡𝑜𝑙𝑜𝑔𝑦 / 𝑐𝑦𝑡𝑜𝑙𝑜𝑔𝑦 / 𝑠𝑙𝑖𝑑𝑒𝑠
📷 𝑀𝑎𝑐𝑟𝑜𝑠𝑐𝑜𝑝𝑦: 𝑑𝑒𝑟𝑚𝑎𝑡𝑜𝑙𝑜𝑔𝑦 / 𝑒𝑛𝑑𝑜𝑠𝑐𝑜𝑝𝑦 / 𝑔𝑟𝑜𝑠𝑠 𝑖𝑚𝑎𝑔𝑒𝑠

Built on Gemini 3.0 Image.
Each session creates individual image links + a ZIP + a session page with all outputs.

🎁 Early access is open (free testing + discounted launch access).
Try it: https://mlhs.ink/synthetica/

#MedicalAI #ComputerVision #DatasetCuration #DataAugmentation #SyntheticData #Radiology #Pathology #MLinHealthScience