大型语言模型作为医学研究中因果推断的副驾驶

双重因果推断：整合后门调整与工具变量学习用于医疗视觉问答

Dual Causal Inference: Integrating Backdoor Adjustment and Instrumental Variable Learning for Medical VQA

本文提出双重因果推断框架，通过整合后门调整和工具变量学习，解决医疗视觉问答中多模态数据中的内在偏见问题，提升模型的诊断推理可靠性。

Causal-SAM-LLM：大型语言模型作为因果推理器用于稳健的医学分割

Causal-SAM-LLM: Large Language Models as Causal Reasoners for Robust Medical Segmentation

Causal-SAM-LLM利用大型语言模型作为因果推理器，提升医学图像分割的鲁棒性和泛化能力。

CRAFT：医学视觉语言模型中的因果责任与失败追踪

CRAFT: Causal Responsibility and Failure Tracing in Medical Vision Language Models

针对医学视觉语言模型中的仲裁失败和刹车失败，提出CRAFT方法，通过因果头定位与干预，实现精准识别和纠正，提升诊断安全性。

零样本因果图外推：基于大语言模型从文本中提取

Zero-shot Causal Graph Extrapolation from Text via LLMs

本研究评估了大语言模型在零样本条件下从文本推断因果关系的能力，并通过迭代成对查询外推因果图，在生物医学摘要基准上验证了其有效性与应用潜力。

大型语言模型能否构建因果图？

Can large language models build causal graphs?

本文评估大型语言模型能否通过自动评分因果图中的边来辅助因果图构建，从而减轻人工查阅文献和专家讨论的负担。

EHRWorld: 一个以患者为中心的医疗世界模型用于长周期临床轨迹

EHRWorld: A Patient-Centric Medical World Model for Long-Horizon Clinical Trajectories

EHRWorld通过因果序列范式训练，有效解决长周期临床模拟中的误差累积问题，提升医疗世界模型的稳定性与可靠性。

忠实还是只是合理？评估闭源LLM在医学推理中的忠实性

Faithful or Just Plausible? Evaluating the Faithfulness of Closed-Source LLMs in Medical Reasoning

本文通过系统性黑盒评估，揭示闭源LLM在医学推理中的忠实性问题，发现链式推理步骤未必驱动预测，模型易受外部提示影响，强调忠实性比准确性更重要。

应用大语言模型进行非小细胞肺癌因果结构学习

Applying Large Language Models for Causal Structure Learning in Non Small Cell Lung Cancer

本研究探索利用大语言模型预测非小细胞肺癌因果图中边的方向性，在去标识化患者数据上验证，结果表明其性能优于现有最先进方法，助力医学因果发现。

MedEinst：通过反事实差异诊断基准测试医疗大语言模型中的Einstellung效应

MedEinst: Benchmarking the Einstellung Effect in Medical LLMs through Counterfactual Differential Diagnosis

MedEinst通过反事实差异诊断基准测试，揭示医疗大语言模型中的Einstellung效应，并提出ECR-Agent提升循证医学标准。

医疗多模态大语言模型的视点临床意图理解能力基准测试

Benchmarking Egocentric Clinical Intent Understanding Capability for Medical Multimodal Large Language Models

本文提出MedGaze-Bench，首个评估医疗多模态大语言模型视点临床意图理解能力的基准测试，通过三维意图框架和陷阱QA机制，揭示现有模型在手术、急救和诊断任务中对意图理解的不足。

Tri-VQA: 面向多属性分析的三角推理医学视觉问答

Tri-VQA: Triangular Reasoning Medical Visual Question Answering for Multi-Attribute Analysis

针对现有医学视觉问答方法结果可信度不足的问题，提出基于因果效应的Tri-VQA三角推理框架，通过构建反向因果问题优化推理过程，在多中心内镜超声等医学VQA数据集上表现优于现有方法。

Using exoskeletons to assist medical staff during prone positioning of mechanically ventilated COVID-19 patients: a pilot study

We conducted a pilot study to evaluate the potential and feasibility of back-support exoskeletons to help the caregivers in the Intensive Care Unit (ICU) of the University Hospital of Nancy (France) executing Prone Positioning (PP) maneuvers on patients suffering from severe COVID-19-related Acute Respiratory Distress Syndrome. After comparing four commercial exoskeletons, the Laevo passive exoskeleton was selected and used in the ICU in April 2020. The first volunteers using the Laevo reported very positive feedback and reduction of effort, confirmed by EMG and ECG analysis. Laevo has been si…

因果增强的医疗研究筛查AI代理

Causal-Enhanced AI Agents for Medical Research Screening

本文提出一种因果图增强的AI代理，用于医疗研究筛选，通过显式因果推理和双层知识图谱提升系统综述的准确性和可解释性。

Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image Segmentation

This study introduced a novel system, called Gaze2Segment, integrating biological and computer vision techniques to support radiologists' reading experience with an automatic image segmentation task. During diagnostic assessment of lung CT scans, the radiologists' gaze information were used to create a visual attention map. This map was then combined with a computer-derived saliency map, extracted from the gray-scale CT images. The visual attention map was used as an input for indicating roughly the location of a object of interest. With computer-derived saliency information, on the other hand…

利用医学图像中的因果信号：一项带有实证结果的初步研究

Exploiting Causality Signals in Medical Images: A Pilot Study with Empirical Results

提出一种结合卷积神经网络主干和因果因子提取器模块的新方法，通过从医学图像中提取弱因果信号来增强特征图，在前列腺MRI和乳腺癌组织病理学数据集上验证了其能有效提升分类性能与预测鲁棒性。

将医疗大语言模型（LLM）基于因果知识图谱：框架、指标与心血管试点研究

Framework for Grounding Healthcare LLMs in a Causal Knowledge Graph: A Cardiovascular Example Pilot

本研究提出以因果知识图谱为核心的医疗LLM评估框架，在心血管试点中验证其有效性，发现集成条件C4在因果推理相关指标上表现最优，未基于图的C1原始干预准确性最高但缺乏因果与证据基础。

A Review of Causality for Learning Algorithms in Medical Image Analysis

Medical image analysis is a vibrant research area that offers doctors and medical practitioners invaluable insight and the ability to accurately diagnose and monitor disease. Machine learning provides an additional boost for this area. However, machine learning for medical image analysis is particularly vulnerable to natural biases like domain shifts that affect algorithmic performance and robustness. In this paper we analyze machine learning for medical image analysis within the framework of Technology Readiness Levels and review how causal analysis methods can fill a gap when creating robust…

DoAtlas-1：一种用于临床AI的因果编译范式

DoAtlas-1: A Causal Compilation Paradigm for Clinical AI

DoAtlas-1通过因果编译范式将医学证据转化为可执行代码，提升临床AI的可审计性和可验证性。

没有免费的午餐：医学影像机器学习中数据集偏差的因果视角

No Fair Lunch: A Causal Perspective on Dataset Bias in Machine Learning for Medical Imaging

本文从因果视角分析医学影像机器学习中的数据集偏差，提出三类因果偏差机制，并指出当前缓解方法的局限性，进而提供一个实用的三步框架以支持安全公平的AI预测模型开发。

Causality matters in medical imaging

This article discusses how the language of causality can shed new light on the major challenges in machine learning for medical imaging: 1) data scarcity, which is the limited availability of high-quality annotations, and 2) data mismatch, whereby a trained algorithm may fail to generalize in clinical practice. Looking at these challenges through the lens of causality allows decisions about data collection, annotation procedures, and learning strategies to be made (and scrutinized) more transparently. We discuss how causal relationships between images and annotations can not only have profound…

做($x$)还是不做($x$)：用于数据集增强的医学图像反事实

To do($x$) or not to do($x$): Medical Image Counterfactuals for Dataset Augmentation

本研究区分医学图像反事实生成的因果与非因果方法，比较确定性、无向和因果三种条件化策略，发现因果方法能提升数据集增强的下游性能与公平性，为数据生成协议设计提供指导。

缺失模态下的医学多模态表示学习因果去偏

Causal Debiasing Medical Multimodal Representation Learning with Missing Modalities

针对医学多模态学习中模态缺失引发的偏差，提出基于因果分析和后门调整的去偏框架，有效提升模型泛化能力。

GATech at AbjadMed: 双向编码器与因果解码器：来自82类阿拉伯语医学分类的见解

GATech at AbjadMed: Bidirectional Encoders vs. Causal Decoders: Insights from 82-Class Arabic Medical Classification

本文研究了阿拉伯语医学文本在82类分类中的表现，对比了双向编码器与因果解码器，发现前者在捕捉细粒度语义边界方面更优。

量化临床决策中的症状因果关系：基于CausaLM的探索

Quantifying Symptom Causality in Clinical Decision Making: An Exploration Using CausaLM

本研究利用CausaLM框架和TReATE方法，量化“胸痛”症状对临床NLP模型诊断预测的因果影响，超越相关性分析，为更可信、可解释的医疗决策支持工具提供依据。

量子计算在线性非高斯无环模型中的应用：面向新型医学知识发现

Application of quantum computing to a linear non-Gaussian acyclic model for novel medical knowledge discovery

本研究将量子计算应用于线性非高斯无环模型，利用量子核计算 DirectLiNGAM 的独立性度量，提出 qLiNGAM 方法。实验表明，在数据量较少时，qLiNGAM 能正确估计因果结构，并在真实量子硬件上实现，为小数据下的新型医学知识发现提供可能。

Towards Accurate Predictions and Causal 'What-if' Analyses for Planning and Policy-making: A Case Study in Emergency Medical Services Demand

Emergency Medical Services (EMS) demand load has become a considerable burden for many government authorities, and EMS demand is often an early indicator for stress in communities, a warning sign of emerging problems. In this paper, we introduce Deep Planning and Policy Making Net (DeepPPMNet), a Long Short-Term Memory network based, global forecasting and inference framework to forecast the EMS demand, analyse causal relationships, and perform `what-if' analyses for policy-making across multiple local government areas. Unless traditional univariate forecasting techniques, the proposed method …

医疗领域鲁棒多模态表征学习

Robust Multimodal Representation Learning in Healthcare

本文提出双流特征去相关框架，通过结构因果分析处理医疗多模态数据中的系统性偏差，提升模型泛化能力，实验验证在MIMIC-IV、eICU和ADNI数据集上的性能提升。

MIMM-X：解构医学图像分析中的虚假相关性

MIMM-X: Disentangling Spurious Correlations for Medical Image Analysis

MIMM-X通过最小化多重虚假相关性的互信息，解构医学图像分析中的因果特征，提升模型在新环境下的泛化能力。

Is More Data All You Need? A Causal Exploration

Curating a large scale medical imaging dataset for machine learning applications is both time consuming and expensive. Balancing the workload between model development, data collection and annotations is difficult for machine learning practitioners, especially under time constraints. Causal analysis is often used in medicine and economics to gain insights about the effects of actions and policies. In this paper we explore the effect of dataset interventions on the output of image classification models. Through a causal approach we investigate the effects of the quantity and type of data we nee…

人类对齐的深度学习：可解释性、因果性与生物学启发

Human-aligned Deep Learning: Explainability, Causality, and Biological Inspiration

本研究从可解释性、因果性和生物视觉三视角将深度学习与人类推理对齐，提出相关框架与模型，提升图像分类的效率、可解释性与鲁棒性，推动其临床应用。

一种基于扩散的方法用于学习医疗处理的多结局分布

A Diffusion-Based Method for Learning the Multi-Outcome Distribution of Medical Treatments

本文提出基于扩散的 DIME 方法，通过因果掩码和自回归生成学习医疗处理多结局的联合干预分布，支持混合类型结局与不确定性量化，是首个面向该任务的神经方法。

用于医学图像分割的具有自适应Mamba类注意力与因果共振学习的UNet

UNet with Self-Adaptive Mamba-Like Attention and Causal-Resonance Learning for Medical Image Segmentation

提出SAMA-UNet，通过自适应Mamba类注意力与因果共振多尺度模块，在医学图像分割中兼顾效率与精度，并在多个数据集上超越现有方法。

高维因果推断与变分后门调整

High Dimensional Causal Inference with Variational Backdoor Adjustment

本文提出一种基于生成建模和变分推断的后门调整方法，用于高维治疗和混杂因素下的因果效应估计，并在半合成X射线医学数据等场景中验证了其有效性。

基于因果驱动的一次性学习用于前列腺MRI癌症分级

Causality-Driven One-Shot Learning for Prostate Cancer Grading from MRI

本文提出一种因果驱动的一次性学习方法，通过提取特征图间因果关系并结合元学习流程，在低数据场景下提升前列腺MRI癌症分级的可靠性与可解释性。

3D ReX：3D神经影像分类中的因果解释

3D ReX: Causal Explanations in 3D Neuroimaging Classification

本文提出首个基于因果的3D模型事后可解释性工具3D ReX，利用实际因果理论生成责任图，在卒中检测模型上验证其能有效揭示与卒中相关的特征空间分布。