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

Vision-Language Models (VLMs) have enabled interpretable medical diagnosis by integrating visual perception with linguistic reasoning. Yet, existing medical chain-of-thought (CoT) models lack explicit mechanisms to represent and enforce causal reasoning, leaving them vulnerable to spurious correlations and limiting their clinical reliability. We pinpoint three core challenges in medical CoT reasoning: how to adaptively trigger causal correction, construct high-quality causal-spurious contrastive samples, and maintain causal consistency across reasoning trajectories. To address these challenges, we propose MedCausalX, an end-to-end framework explicitly models causal reasoning chains in medical VLMs. We first introduce the CRMed dataset providing fine-grained anatomical annotations, structured causal reasoning chains, and counterfactual variants that guide the learning of causal relationships beyond superficial correlations. Building upon CRMed, MedCausalX employs a two-stage adaptive reflection architecture equipped with $\langle$causal$\rangle$ and $\langle$verify$\rangle$ tokens, enabling the model to autonomously determine when and how to perform causal analysis and verification. Finally, a trajectory-level causal correction objective optimized through error-attributed reinforcement learning refines the reasoning chain, allowing the model to distinguish genuine causal dependencies from shortcut associations. Extensive experiments on multiple benchmarks show that MedCausalX consistently outperforms state-of-the-art methods, improving diagnostic consistency by +5.4 points, reducing hallucination by over 10 points, and attaining top spatial grounding IoU, thereby setting a new standard for causally grounded medical reasoning. The code and dataset are available at https://github.com/zhcz328/MedCausalX.

Large language models (LLMs) enable waveform-to-text ECG interpretation and interactive clinical questioning, yet most ECG-LLM systems still rely on weak signal-text alignment and retrieval without explicit physiological or causal structure. This limits grounding, temporal reasoning, and counterfactual "what-if" analysis central to clinical decision-making. We propose CARE-ECG, a causally structured ECG-language reasoning framework that unifies representation learning, diagnosis, and explanation in a single pipeline. CARE-ECG encodes multi-lead ECGs into temporally organized latent biomarkers, performs causal graph inference for probabilistic diagnosis, and supports counterfactual assessment via structural causal models. To improve faithfulness, CARE-ECG grounds language outputs through causal retrieval-augmented generation and a modular agentic pipeline that integrates history, diagnosis, and response with verification. Across multiple ECG benchmarks and expert QA settings, CARE-ECG improves diagnostic accuracy and explanation faithfulness while reducing hallucinations (e.g., 0.84 accuracy on Expert-ECG-QA and 0.76 on SCP-mapped PTB-XL under GPT-4). Overall, CARE-ECG provides traceable reasoning by exposing key latent drivers, causal evidence paths, and how alternative physiological states would change outcomes.

Disease classification from patient-reported symptom descriptions is usually approached with lightweight discriminative models or with autoregressive large language models (LLMs), which understand language broadly but generate text token by token and require output parsing. We study decision foundation models, which score a closed set of candidate labels in a single forward pass without generating text. We adapt Clef-Flash, a 9-billion parameter decision model built on a Qwen3.5 backbone, to the 24-class Symptom2Disease benchmark using 4-bit quantized low-rank adaptation (QLoRA) on a stratified split of 960 training and 240 test cases. The fine-tuned model reaches 95.42% accuracy (95% CI 91.98-97.42%) and a macro F1 of 0.9551, up from 65.00% for the unadapted weights (62.92% through the remote API). However, TF-IDF linear SVM and logistic regression baselines trained on the same split reach 98.75% (95% CI 96.39-99.57%) with sub-millisecond CPU inference, so on this benchmark the fine-tuned model offers no accuracy advantage over a well-tuned linear classifier. A Leave-One-Symptom-Out clause-ablation analysis of four illustrative narratives shows predictions depending on distinguishing clauses: in a psoriasis example, removing the clause describing silvery scales lowers the predicted probability from above 99.99% to 0.12% and shifts the prediction to impetigo, while removing other clauses changes it by less than 0.001 percentage points. Decision architectures deliver structured, single-pass predictions from large language backbones, but short, lexically distinctive benchmarks such as Symptom2Disease cannot show an advantage over simple baselines; validation on longer, real clinical text and clinician review are needed.