Drishtant Ghosh (Drix10)
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AI in Healthcare and Science••6 min read•1104 words

🚀 AI in Billing Software

⚡Direct Technical Summary

Billing software used to help teams manage the work. AI can now help execute it. We're excited to announce https:// XY.AI × @CollaborateMD . Together, we're bringing AI-native reve

🚀 AI in Billing Software

Billing software used to help teams manage the work. AI can now help execute it. We're excited to announce https:// XY.AI × @CollaborateMD . Together, we're bringing AI-native revenue cycle operations to billing teams, automating the repetitive work across claims, and improving accuracy.

Key Points:

  • AI-native Revenue Cycle Operations: AI can automate repetitive work across claims, improving accuracy and efficiency in billing teams.

  • Collaboration with CollaborateMD: XY.AI is partnering with CollaborateMD to bring AI-native revenue cycle operations to billing teams.

  • Automation of Repetitive Work: AI can automate tasks such as data entry, claims processing, and follow-up, freeing up staff to focus on higher-value tasks.

🔗 Resources:

  • Original post ↗
  • XY.AI
  • CollaborateMD
  • AI-native revenue cycle operations for billing teams

🤖 The History of GRAIL

Thanks for reviewing the history of GRAIL. As you mentioned, not an easy issue to solve. Not only are you challenged by the nuance of trial design, but the smallest of changes in cancer incidence can have an effect.

Key Points:

  • Challenges in Trial Design: GRAIL's trial design is nuanced, and small changes in cancer incidence can have significant effects.

  • Importance of Nuance: The history of GRAIL highlights the importance of nuance in trial design and the need for careful consideration of all factors.

  • Complexity of Cancer Incidence: Cancer incidence is a complex issue, and small changes can have significant effects.

🔗 Resources:

🚀 The Story of GRAIL

GRAIL has had a strange life as a company. Not because the idea is bad. Quite the opposite. They are trying to build something that could actually help people: a blood test that detects cancer before symptoms appear. That is a very hard problem. GRAIL was spun out of Illumina.

Key Points:

  • GRAIL's Mission: GRAIL is working to build a blood test that can detect cancer before symptoms appear.

  • Spun out of Illumina: GRAIL was spun out of Illumina, a leading genomics company.

  • Difficulty of the Problem: Detecting cancer before symptoms appear is a very hard problem.

🔗 Resources:

🚨 GRAIL's Pivotal Clinical Trial

In its pivotal clinical trial, the GRAIL test to detect early cancer failed its primary endpoint. It detected only 3 early cases per 1,000 people assessed. There were more false negative results (N=196) than true positives (N=133). Reviewed here https:// erictopol.substack.com/p/the-largest- study-of-a-multi-cancer … But you

Key Points:

  • Failure of Primary Endpoint: GRAIL's test failed to meet its primary endpoint in its pivotal clinical trial.

  • Low Detection Rate: The test detected only 3 early cases per 1,000 people assessed.

  • High False Negative Rate: There were more false negative results than true positives.

🔗 Resources:

🚀 A Physician's Journey

Today, 5 years ago, I started my US journey. I can’t overstate the impact this country and DFCI had on my trajectory. I felt welcome, grew tremendously as a physician and scientist. Grateful for the people and trust that shaped these years. Looking forward to what comes next!

Key Points:

  • Impact of US Journey: The physician's US journey had a significant impact on their career and personal growth.

  • Welcome and Support: The physician felt welcome and supported during their journey.

  • Gratitude: The physician is grateful for the people and trust that shaped their journey.

🔗 Resources:

🚀 Biosecurity Benchmarks

Our biosecurity benchmarks show Grok 4.7 is strong at refusing dangerous biological requests while supporting legitimate research. A model to take very seriously for data analysis and practical scientific work. https:// x.ai/news/grok-4-7

Key Points:

  • Biosecurity Benchmarks: Grok 4.7 has strong biosecurity benchmarks, refusing dangerous biological requests while supporting legitimate research.

  • Model for Data Analysis: Grok 4.7 is a model to take seriously for data analysis and practical scientific work.

  • Support for Legitimate Research: Grok 4.7 supports legitimate research while refusing dangerous biological requests.

🔗 Resources:

🚀 Register Now

Register now: https:// hubs.li/Q04xXqB40 Quick registration via LinkedIn: https:// hubs.li/Q04xXqG20 Read the full speaker bio and abstract here: https:// hubs.li/Q04xXqC30

Key Points:

  • Registration Link: Register now for the event at https:// hubs.li/Q04xXqB40.

  • Quick Registration: Register quickly via LinkedIn at https:// hubs.li/Q04xXqG20.

  • Speaker Bio and Abstract: Read the full speaker bio and abstract here: https:// hubs.li/Q04xXqC30.

🔗 Resources:

🚀 First Paper as Co-Senior Author

Excited to share my first paper as a co-senior author, now published in Nature Genetics! @broadinstitute Paper: https:// nature.com/articles/s4158 8-026-02734-4 … #NatureGenetics #AllofUs #PolygenicRiskScores #StatisticalGenetics

Key Points:

  • First Paper as Co-Senior Author: The author is excited to share their first paper as a co-senior author.

  • Publication in Nature Genetics: The paper has been published in Nature Genetics.

  • Collaboration with Broad Institute: The author collaborated with the Broad Institute on the paper.

🔗 Resources:

🚨 Gut Microbiomes and Type 1 Diabetes

Children whose gut microbiomes plateaued early had about three times the risk of type 1 diabetes or its preceding immune attack, though the study cannot establish cause and effect. @MassGenBrigham

Key Points:

  • Gut Microbiomes and Type 1 Diabetes: Children with plateaued gut microbiomes had a higher risk of type 1 diabetes.

  • Risk of Immune Attack: The study found a higher risk of immune attack in children with plateaued gut microbiomes.

  • Limitations of the Study: The study cannot establish cause and effect.

🔗 Resources:

🚀 Hands-on Start to GRCon2026

A hands-on start to #GRCon2026 in Raleigh! Our Director of ML, Raj Bhattacharjea, led the workshop "AI-RAN with OCUDU and GNU Radio." Participants built their own open-source 5G network, connected real smartphones to it, explored GPU acceleration, and streamed live uplink

Key Points:

  • Hands-on Workshop: The workshop was a hands-on experience for participants.

  • AI-RAN with OCUDU and GNU Radio: The workshop focused on AI-RAN with OCUDU and GNU Radio.

  • Building Open-Source 5G Network: Participants built their own open-source 5G network.

🔗 Resources:

📂Source / Implementation:AI in Healthcare and Science / resources-257.md
GitHub Repository↗

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Drishtant Ghosh (Drix10)
Drishtant Ghosh (Drix10)•Author & Engineer

Technical founder and engineer working across AI systems, developer infrastructure, and cybersecurity.

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GitHub: AI in Healthcare and Science/resources-257.md ↗https://blogs.drix10.com/articles/ai-in-healthcare-and-science/ai-in-billing-software-257