医学大模型流程需重构，以确保数据科学的可预测、可计算与稳定。
Veridical Data Science for Medical Foundation Models
- 从专用模型转向通用大模型，流程分上游下游，多方参与
- 当前流程违背可预测、可计算、稳定原则，影响科研可信度
- 提出新框架，考虑算力与访问限制，重振医学数据科学可信度
医学领域基础模型（FMs）如大语言模型的兴起，推动了数据科学范式变革：从针对特定问题的专用模型转向在海量非结构化数据上预训练的通用模型，再适应多种临床任务。这一转变重塑了医学数据科学流程，形成包含上游与下游阶段的基础模型生命周期（FMLC），计算资源、模型与数据访问权及决策权在多个利益相关方间分配。然而，基础模型本质是统计模型，当前流程偏离了可信数据科学（VDS）的核心原则——可预测性、可计算性与稳定性（PCS），削弱了透明且可复现的科学分析能力。本文基于PCS原则批判性审视医学FMLC，并指出其偏离标准流程之处；最后提出重构方案，扩展并细化PCS原则，纳入基础模型固有的算力与可访问性约束。
原文摘要 · Abstract (English)
The advent of foundation models (FMs) such as large language models (LLMs) has led to a cultural shift in data science, both in medicine and beyond. This shift involves moving away from specialized predictive models trained for specific, well-defined domain questions to generalist FMs pre-trained on vast amounts of unstructured data, which can then be adapted to various clinical tasks and questions. As a result, the standard data science workflow in medicine has been fundamentally altered; the foundation model lifecycle (FMLC) now includes distinct upstream and downstream processes, in which computational resources, model and data access, and decision-making power are distributed among multiple stakeholders. At their core, FMs are fundamentally statistical models, and this new workflow challenges the principles of Veridical Data Science (VDS), hindering the rigorous statistical analysis expected in transparent and scientifically reproducible data science practices. We critically examine the medical FMLC in light of the core principles of VDS: predictability, computability, and stability (PCS), and explain how it deviates from the standard data science workflow. Finally, we propose recommendations for a reimagined medical FMLC that expands and refines the PCS principles for VDS including considering the computational and accessibility constraints inherent to FMs.
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