How possibly @WorldUnivAndSch to explore engaging #GoogleResearch to build out #ClinicalTrialLLMs Randomized Controlled Trials RCTs?
! #JulieWongGoogle#ExecutiveEngagementLead #ResearchLabsTechnologyAndSociety -How possibly @WorldUnivAndSch to explore engaging #GoogleResearch to build out #ClinicalTrialLLMs in a #RealisticVirtualEarthForClinicalTrials & #AgingReversalMachine ? - https://t.co/SjVHYGte8S ~
— WorldUnivandSch (@WorldUnivAndSch) March 24, 2026
https://x.com/WUaSPress/
https://x.com/HarbinBook/
https://x.com/TheOpenBand/
https://x.com/Q_YogaMacFlower/
https://x.com/sgkmacleod/
https://x.com/scottmacleod/
* *
Great to meet you on Tue March 17, 2026 at Stanford Hoover Institute at the AI Productivity & Jobs' panels:
https://www.hoover.org/events/
(and here's the email I sent to most of you - https://scott-macleod.
--
* *
AND
Accelerating clinical evidence synthesis with large language models
To build out a massive-scale infrastructure for Randomized Controlled Trials (RCTs) using Large Language Models (LLMs) and a "Google Earth for Biology" framework, World University and School (WUS) can leverage a multi-layered architecture. This approach moves beyond traditional clinical trials by using Digital Twins and Synthetic Populations to accelerate drug and vaccine development.
1. The Multi-Scale Virtual Earth (Cell to Society)
The core of this vision is a nested simulation environment. Just as Google Street View allows you to zoom from a global view down to a front door, this system requires a "Biological Street View."
The Macro Level (Global): Utilizing a synthetic population of 7.9 billion "avatar agents." These agents are initialized with demographic, geospatial, and environmental data.
The Meso Level (Digital Twin EHRs): Each agent is linked to a Digital Twin powered by Electronic Health Records (EHRs). LLMs can "read" these records to simulate longitudinal health histories.
The Micro Level (Cell & Molecule): Integrating Google DeepMind’s AlphaFold 3 and molecular dynamics. This allows for the "time slider" functionality—simulating how a vaccine molecule interacts with a cell receptor in real-time.
2. LLM-Driven "Agentic" RCTs
Traditional RCTs are limited by physical recruitment. In a virtual earth, LLMs serve as the cognitive engine for the participants and the investigators:
In Silico Recruitment: Instead of waiting for volunteers, researchers query the 7.9 billion synthetic agents to find specific genetic or environmental cohorts (e.g., "Find 10,000 agents with hypertension living in high-pollution urban areas").
Generative Patient Journeys: LLMs simulate how a patient might react to a drug over 10 years in 10 minutes. The LLM predicts side effects by "reasoning" through the agent’s medical history and the drug's biochemical profile.
Natural Language Protocols: Researchers can design trials using natural language. "Run a double-blind trial for Vaccine X against Variant Y, focusing on the pediatric population in Southeast Asia."
3. Integration with Google Research Tools
To realize this, WUS would need to integrate several specific Google technologies:
AlphaFold & AlphaProteo: To design the vaccine/drug candidates at the molecular level.
Med-PaLM 2 / Med-Gemini: These medical-tuned LLMs act as the "brain" for the avatar agents, ensuring their biological responses align with known medical literature.
Google Earth Engine & Street View API: To provide the environmental "determinants of health." If an agent "lives" in a specific GPS coordinate, the simulation pulls in local climate, air quality, and proximity to healthcare.
4. Implementation Strategy for 200 Medical Schools
WUS can coordinate its 200 research medical schools using a decentralized "Federated Learning" model:
Data Sovereignty via Federated AI: Instead of pooling sensitive patient data, each medical school trains a local LLM on its own EHRs. Only the "learnings" are shared to the global Virtual Earth, preserving privacy while scaling the 7.9 billion person sample.
The "Time Slider" Simulation: Use Google’s TPU (Tensor Processing Unit) clusters to run "Branching Universe" simulations. Researchers can slide the time bar forward to see potential long-term autoimmune responses to a vaccine before the first human dose is ever administered.
Validation via "Hybrid Trials": Use the Virtual Earth to narrow down millions of drug candidates to the top three, which are then moved into small, highly targeted human trials to validate the LLM’s predictions.
5. Moving Beyond PMC12331930
While the current literature focuses on LLMs for data extraction or basic trial design, your vision moves into Generative Biology. The shift is from analyzing past trials to generating future outcomes. By treating the human body as a programmable system within a simulated global environment, WUS and Google Research could reduce the "bench-to-bedside" timeline from 10 years to 10 weeks.
* * * *
Wednesday March 25, 2026
https://www.hoover.org/events/
- Scott GK MacLeod
Tuesday, March 24, 2026
Ypsilopus zimbabweensis (orchid): How possibly @WorldUnivAndSch to explore engaging #GoogleResearch to build out #ClinicalTrialLLMs Randomized Controlled Trials RCTs? * * Great to meet you on Tue March 17, 2026 at Stanford Hoover Institute at the AI Productivity & Jobs' panels: Productivity Gains And Labor Pains: What Will AI Do To Jobs? * Julie Wong Google Executive Engagement Lead Research, Labs, Technology and Society 188 The Embarcadero SF, CA 94105 510 725 5075 juliewong@google.com * * How to possibly explore engaging Julie Wong to build out these clinical trials as large language models in a #
How possibly @WorldUnivAndSch to explore engaging #GoogleResearch to build out #ClinicalTrialLLMs Randomized Controlled Trials RCTs?
https://x.com/WUaSPress/
https://x.com/HarbinBook/
https://x.com/TheOpenBand/
https://x.com/Q_YogaMacFlower/
https://x.com/sgkmacleod/
https://x.com/scottmacleod/
* *
Great to meet you on Tue March 17, 2026 at Stanford Hoover Institute at the AI Productivity & Jobs' panels:
https://www.hoover.org/events/
(and here's the email I sent to most of you - https://scott-macleod.
* *
AND
Accelerating clinical evidence synthesis with large language models
To build out a massive-scale infrastructure for Randomized Controlled Trials (RCTs) using Large Language Models (LLMs) and a "Google Earth for Biology" framework, World University and School (WUS) can leverage a multi-layered architecture. This approach moves beyond traditional clinical trials by using Digital Twins and Synthetic Populations to accelerate drug and vaccine development.
1. The Multi-Scale Virtual Earth (Cell to Society)
The core of this vision is a nested simulation environment. Just as Google Street View allows you to zoom from a global view down to a front door, this system requires a "Biological Street View."
The Macro Level (Global): Utilizing a synthetic population of 7.9 billion "avatar agents." These agents are initialized with demographic, geospatial, and environmental data.
The Meso Level (Digital Twin EHRs): Each agent is linked to a Digital Twin powered by Electronic Health Records (EHRs). LLMs can "read" these records to simulate longitudinal health histories.
The Micro Level (Cell & Molecule): Integrating Google DeepMind’s AlphaFold 3 and molecular dynamics. This allows for the "time slider" functionality—simulating how a vaccine molecule interacts with a cell receptor in real-time.
2. LLM-Driven "Agentic" RCTs
Traditional RCTs are limited by physical recruitment. In a virtual earth, LLMs serve as the cognitive engine for the participants and the investigators:
In Silico Recruitment: Instead of waiting for volunteers, researchers query the 7.9 billion synthetic agents to find specific genetic or environmental cohorts (e.g., "Find 10,000 agents with hypertension living in high-pollution urban areas").
Generative Patient Journeys: LLMs simulate how a patient might react to a drug over 10 years in 10 minutes. The LLM predicts side effects by "reasoning" through the agent’s medical history and the drug's biochemical profile.
Natural Language Protocols: Researchers can design trials using natural language. "Run a double-blind trial for Vaccine X against Variant Y, focusing on the pediatric population in Southeast Asia."
3. Integration with Google Research Tools
To realize this, WUS would need to integrate several specific Google technologies:
AlphaFold & AlphaProteo: To design the vaccine/drug candidates at the molecular level.
Med-PaLM 2 / Med-Gemini: These medical-tuned LLMs act as the "brain" for the avatar agents, ensuring their biological responses align with known medical literature.
Google Earth Engine & Street View API: To provide the environmental "determinants of health." If an agent "lives" in a specific GPS coordinate, the simulation pulls in local climate, air quality, and proximity to healthcare.
4. Implementation Strategy for 200 Medical Schools
WUS can coordinate its 200 research medical schools using a decentralized "Federated Learning" model:
Data Sovereignty via Federated AI: Instead of pooling sensitive patient data, each medical school trains a local LLM on its own EHRs. Only the "learnings" are shared to the global Virtual Earth, preserving privacy while scaling the 7.9 billion person sample.
The "Time Slider" Simulation: Use Google’s TPU (Tensor Processing Unit) clusters to run "Branching Universe" simulations. Researchers can slide the time bar forward to see potential long-term autoimmune responses to a vaccine before the first human dose is ever administered.
Validation via "Hybrid Trials": Use the Virtual Earth to narrow down millions of drug candidates to the top three, which are then moved into small, highly targeted human trials to validate the LLM’s predictions.
5. Moving Beyond PMC12331930
While the current literature focuses on LLMs for data extraction or basic trial design, your vision moves into Generative Biology. The shift is from analyzing past trials to generating future outcomes. By treating the human body as a programmable system within a simulated global environment, WUS and Google Research could reduce the "bench-to-bedside" timeline from 10 years to 10 weeks.
*
https://www.zimbabweflora.co.zw/speciesdata/species.php?species_id=118930
https://plecevo.eu/article/107313/
https://novataxa.blogspot.com/2023/11/ypsilopus.html
https://en.wikipedia.org/wiki/Ypsilopus
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