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DoAtlas-1: A Causal Compilation Paradigm for Clinical AI

arXiv:2602.19158v1 Announce Type: new Abstract: Medical foundation models generate narrative explanations but cannot quantify intervention effects, detect evidence conflicts, or validate literature claims, limiting clinical auditability. We propose causal compilation, a paradigm that transforms medical evidence from narrative text into executable code. The paradigm standardizes heterogeneous research evidence into structured estimand objects, each explicitly specifying intervention contrast, effect scale, time horizon, and target population, supporting six executable causal queries: do-calculus, counterfactual reasoning, temporal trajectories, heterogeneous effects, mechanistic decomposition, and joint interventions. We instantiate this paradigm in DoAtlas-1, compiling 1,445 effect kernels from 754 studies through effect standardization, conflict-aware graph construction, and real-world validation (Human Phenotype Project, 10,000 participants). The system achieves 98.5% canonicalizati

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Yulong Li, Jianxu Chen, Xiwei Liu, Chuanyue Suo, Rong Xia, Zhixiang Lu, Yichen Li, Xinlin Zhuang, Niranjana Arun Menon, Yutong Xie, Eran Segal, Imran Razzak · · 1 min read · 3 views
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arXiv:2602.19158v1 Announce Type: new Abstract: Medical foundation models generate narrative explanations but cannot quantify intervention effects, detect evidence conflicts, or validate literature claims, limiting clinical auditability. We propose causal compilation, a paradigm that transforms medical evidence from narrative text into executable code. The paradigm standardizes heterogeneous research evidence into structured estimand objects, each explicitly specifying intervention contrast, effect scale, time horizon, and target population, supporting six executable causal queries: do-calculus, counterfactual reasoning, temporal trajectories, heterogeneous effects, mechanistic decomposition, and joint interventions. We instantiate this paradigm in DoAtlas-1, compiling 1,445 effect kernels from 754 studies through effect standardization, conflict-aware graph construction, and real-world validation (Human Phenotype Project, 10,000 participants). The system achieves 98.5% canonicalization accuracy and 80.5% query executability. This paradigm shifts medical AI from text generation to executable, auditable, and verifiable causal reasoning.

Executive Summary

This article introduces DoAtlas-1, a causal compilation paradigm for clinical AI that transforms medical evidence from narrative text into executable code. The paradigm standardizes heterogeneous research evidence into structured estimand objects, enabling six executable causal queries. Through real-world validation using the Human Phenotype Project, DoAtlas-1 achieves high canonicalization accuracy and query executability. This shift in medical AI from text generation to causal reasoning has significant implications for clinical auditability, evidence-based medicine, and the development of executable clinical guidelines.

Key Points

  • ▸ DoAtlas-1 proposes a causal compilation paradigm for clinical AI, transforming narrative text into executable code.
  • ▸ The paradigm standardizes heterogeneous research evidence into structured estimand objects.
  • ▸ DoAtlas-1 achieves high canonicalization accuracy and query executability through real-world validation.

Merits

Strengths in Causal Reasoning

DoAtlas-1 provides a novel paradigm for causal reasoning in clinical AI, enabling the transformation of narrative text into executable code. This shift in focus from text generation to causal reasoning has significant implications for clinical auditability and evidence-based medicine.

High Accuracy and Executability

Through real-world validation, DoAtlas-1 achieves high canonicalization accuracy and query executability, demonstrating the effectiveness of the paradigm in practical applications.

Scalability and Generalizability

The ability to compile 1,445 effect kernels from 754 studies suggests that DoAtlas-1 is scalable and generalizable to various clinical contexts and domains.

Demerits

Limited Contextual Understanding

While DoAtlas-1 demonstrates high accuracy and executability, the paradigm may not fully capture the nuances and complexities of human contextual understanding, which is essential for effective clinical decision-making.

Dependence on High-Quality Data

The accuracy and effectiveness of DoAtlas-1 rely heavily on high-quality and well-structured research evidence, which may not always be available or easily accessible.

Potential Over-Reliance on Technology

The development and implementation of DoAtlas-1 may lead to an over-reliance on technology, potentially undermining the role of human clinicians and healthcare professionals in clinical decision-making.

Expert Commentary

DoAtlas-1 represents a significant advancement in the field of clinical AI, providing a novel paradigm for causal reasoning that has the potential to transform clinical auditability and evidence-based medicine. While the article highlights the merits of the paradigm, including high accuracy and executability, it is essential to consider the potential demerits, including limited contextual understanding and dependence on high-quality data. The development and implementation of DoAtlas-1 require careful consideration of the potential implications, including the need for ongoing investments in AI research and development, as well as the establishment of robust infrastructure and frameworks for the validation and regulation of AI-based clinical decision-support systems.

Recommendations

  • ✓ Further research is needed to fully explore the potential of DoAtlas-1's paradigm in various clinical contexts and domains.
  • ✓ The development of DoAtlas-1 should be accompanied by ongoing investments in AI research and development, as well as the establishment of robust infrastructure and frameworks for the validation and regulation of AI-based clinical decision-support systems.

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