Why this reading
Reasoning and patient-level computation do not need the same trust boundary.
The prototype uses an external language model for requirement refinement and analytical reasoning while keeping raw patient-level computation inside a local environment.
For SP workflows, the broader architecture is compelling: expose only the minimum metadata needed for reasoning; keep production data, execution and validation inside controlled tools.
Reading order
Your 30-minute plan.
Identify what leaves the local environment and what stays.
Read architecture, requirement refinement, local execution and reproduced analyses.
Connect privacy design to context of use, governance and lifecycle control.
Compare local tools and domain skills with regulated programming workflows.
Design the data boundary for one SP AI assistant.
Open-access sources
Architecture, governance and a clinical-programming comparison.
Brief background
Keep the data local; make the interfaces explicit.
The system separates external LLM-assisted reasoning from local statistical execution. The external model is reported to receive zero row-level patient data.
The workflow includes schema perception, interactive cleaning, requirement refinement, retrieval-grounded statistical guidance, generated R code and controlled local execution.
Functional validation reproduced a published prognostic analysis including Kaplan-Meier estimation, Cox regression, model diagnostics, ROC analysis and publication-ready outputs.
The privacy claim is architectural, not absolute: prompts, metadata, logs and outputs can still expose sensitive information unless they are deliberately controlled.
For SP work, use: approved metadata → LLM reasoning → local SAS/R execution → deterministic validation → human approval → auditable output.
Key vocabulary
Fifteen terms for privacy-preserving clinical AI.
| Term | 中文 | Meaning / use |
|---|---|---|
| privacy-preserving architecture | 隐私保护架构 | A system design that limits exposure of sensitive data while still enabling useful computation. |
| local execution | 本地执行 | Running code and processing row-level data inside a controlled local environment. |
| row-level data | 行级数据 | Individual patient or observation records rather than aggregated summaries. |
| schema perception | 数据结构感知 | Identifying variables, types, labels, missingness and other structural properties of a dataset. |
| requirements refinement | 需求细化 | Clarifying an analytical request before code is generated or executed. |
| controlled command-line interface | 受控命令行接口 | A restricted execution interface that exposes only approved operations. |
| retrieval-augmented generation | 检索增强生成 | Grounding an LLM with selected external knowledge or templates before it responds. |
| multivariable Cox model | 多变量 Cox 模型 | A survival model estimating hazard relationships while adjusting for multiple covariates. |
| Schoenfeld residual | Schoenfeld 残差 | A diagnostic quantity commonly used to assess proportional-hazards assumptions. |
| publication-ready output | 可直接用于发表的输出 | A table or figure formatted to a standard suitable for formal reporting. |
| human-supervised | 人工监督的 | A workflow in which humans retain control over judgment-dependent decisions. |
| data boundary | 数据边界 | The defined point beyond which sensitive data are not allowed to move. |
| execution boundary | 执行边界 | The defined separation between reasoning, code generation and actual computation. |
| audit trail | 审计追踪 | A record of actions, inputs, code, outputs and approvals that allows later reconstruction. |
| context of use | 使用情境 | A precise definition of why, where and for what purpose an AI system is used. |
Useful phrases
Language for an architecture and governance discussion.
- keep raw patient data inside the local environment - The architecture keeps raw patient data inside the local environment.
- separate remote reasoning from local execution - The system separates remote reasoning from local execution.
- send metadata rather than patient-level records - The agent can send metadata rather than patient-level records.
- ground statistical choices in curated guidance - Statistical choices are grounded in curated guidance.
- require human confirmation for judgment-dependent decisions - The workflow requires human confirmation for judgment-dependent decisions.
- execute generated code through a controlled interface - Generated code is executed through a controlled interface.
- reproduce a published analytical workflow - The framework was tested by reproducing a published analytical workflow.
- treat privacy as an architectural constraint - Privacy should be treated as an architectural constraint.
- log every analytical decision and execution step - The system should log every analytical decision and execution step.
- define the model's context of use before deployment - Teams should define the model's context of use before deployment.
Comprehension
Five questions.
- Why separate external reasoning from local execution?
- What information can still leak sensitive content even when raw rows stay local?
- Why should judgment-dependent statistical choices require human confirmation?
- How do FDA/EMA principles extend beyond privacy architecture?
- How could this design transfer to SAS/CDISC statistical programming?
Retelling
Say it three times.
- 30 seconds · Privacy problem → split architecture → reproduced analysis.
- 45 seconds · Requirement → external reasoning → local execution → validation → approval.
- 60 seconds · Explain why "no raw rows in the LLM" is necessary but not sufficient.
5-minute output task
Design the data boundary for one SP AI assistant.
- Minute 1: Choose log review, spec review, TFL QC, code generation, metadata inspection or method recommendation.
- Minutes 2-3: Split information into model-safe, local-only and human-approval categories.
- Minute 4: Add allowlisted tools, local sandboxing, logging, output scanning and deterministic QC.
- Minute 5: State the minimum information the model actually needs.
One sentence to keep
A trustworthy clinical AI workflow should expose the model to the minimum information needed for reasoning, keep sensitive computation inside a controlled execution boundary, and preserve human accountability for judgment-dependent decisions.