Enterprise AI Analysis
CheXthought: A global multimodal dataset of clinical chain-of-thought reasoning and visual attention for chest X-ray interpretation
Chest X-ray interpretation is one of the most frequently performed diagnostic tasks in medicine and a primary target for AI development, yet current vision-language models are primarily trained on datasets of paired images and reports, not the cognitive processes and visual attention that underlie clinical reasoning. Here, we present CheXthought, a global, multimodal resource containing 103,592 chain-of-thought reasoning traces and 6,609,082 synchronized visual attention annotations across 50,312 multi-read chest X-rays from 501 radiologists in 71 countries. Our analysis reveals clinical reasoning patterns in how experts deploy distinct visual search strategies, integrate clinical context, and communicate uncertainty.
Executive Impact Summary
We demonstrate the clinical utility of CheXthought across four dimensions. First, CheXthought reasoning significantly outperforms state-of-the-art vision-language model chain-of-thought in factual accuracy and spatial grounding. Second, visual attention data used as an inference-time hint recovers missed findings and significantly reduces hallucinations. Third, models trained on CheXthought data achieve significantly higher accuracy in pathology classification, visual faithfulness, temporal reasoning and uncertainty communication. Fourth, leveraging CheXthought's multi-reader annotations, we predict both human-human and human-AI disagreement directly from an image, enabling transparent communication of case difficulty, uncertainty and model reliability. These findings establish CheXthought as a resource for advancing multimodal clinical reasoning and the development of more transparent, interpretable vision-language models.
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Reasoning Traces
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Visual Attention Annotations
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Multi-Read CXRs
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Expert Radiologists
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Global Representation
Deep Analysis & Enterprise Applications
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Reasoning Traces
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Visual Attention Coordinates
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Multi-Read CXRs
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Contributing Countries
Diverse Annotator Cohort
CheXthought gathered contributions from 501 radiologists, residents, and fellows across 71 countries, making it the most geographically diverse medical imaging dataset to date. This extensive network ensures a broad spectrum of expertise and clinical perspectives, which is crucial for mitigating systematic biases often introduced by smaller, institution-specific datasets.
The annotator cohort spans various training levels (PGY-2 through Attending/Staff), with attending/staff radiologists contributing the largest share of annotations. This diversity provides a rich foundation for AI models to learn from varied diagnostic approaches and decision-making styles, leading to more robust and generalizable solutions for real-world clinical settings.
Expert Visual Search Strategies
Analysis of visual attention reveals three distinct search-trajectory strategies employed by radiologists, with varying impacts on diagnostic accuracy.
Broad Search (78.2% Accuracy)
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Central Search (73.6% Accuracy)
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Narrow Search (68.3% Accuracy)
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Accuracy increase for Pneumothorax with clinical context.
Integrating clinical context significantly boosts diagnostic accuracy for certain pathologies. For Pneumothorax, context referencing led to a remarkable +16.0 percentage point increase in accuracy. Similarly, for Cardiomegaly, accuracy improved by +5.9 percentage points. This highlights the critical role of patient history and prior information in expert reasoning, influencing diagnostic confidence and outcomes.
| Dimension | CheXthought Data (Human CoT) | GPT 5.2 | MedGemma 1.5 | Claude Opus 4.5 |
|---|---|---|---|---|
| Comprehensiveness of Findings | 4.67 | 4.25 | 4.17 | 4.03 |
| Causal Support | 4.84 | 4.37 | 3.99 | 4.26 |
| Factuality | 4.88 | 4.19 | 4.12 | 3.83 |
| Spatial Localization | 4.88 | 3.79 | 4.34 | 3.50 |
| Overall Score | 4.81 | 4.14 | 4.13 | 3.89 |
Superior Spatial Grounding
CheXthought-VLM demonstrated superior performance in spatial localization, achieving scores of 4.33 on NIH ChestX-ray14 and 4.26 on MIMIC-CXR. This significantly outperforms other VLMs, indicating better visual grounding of pathological findings. Such precise spatial grounding is critical for accurate diagnosis and building clinician trust, as it links the model's reasoning directly to specific, relevant image features.
This enhanced capability helps reduce the risk of 'hallucinations' where models report findings without visual basis, a common pitfall in AI interpretation. The explicit spatial linking in CheXthought's training data fosters a more faithful and interpretable reasoning process.
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CheXthought-VLM's persistence rate in occlusion tests.
In region occlusion experiments, CheXthought-VLM achieved the lowest overall persistence rate of just 4%. This means the model rarely reported findings when the diagnostically relevant region was masked, demonstrating a strong coupling between its predictions and true visual evidence. This significantly reduces hallucinations compared to models like MedGemma 1.5 (47.4% persistence), enhancing reliability in clinical decision-making.
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CheXthought-VLM's temporal trajectory accuracy on correct order.
CheXthought-VLM excels in temporal reasoning, achieving an impressive 95% accuracy on correctly ordered serial images. It robustly tracks disease progression based on visual features, unlike other models that may rely solely on chronological heuristics. This crucial capability reflects real-world radiology practice, where comparing current and prior studies is fundamental for assessing interval change and refining diagnostic confidence.
| Metric | Observed (GT) | Predicted (VLM) | Deviation |
|---|---|---|---|
| Overall Human-Human Disagreement | 31.8% | 36.0% | +4.2% |
| Overall AI-Human Disagreement | 32.3% | 35.1% | +2.8% |
| Pneumothorax HH Disagreement | 37.6% | 35.2% | -2.4% |
| Atelectasis HH Disagreement | 30.8% | 38.2% | +7.4% |
Communicating Uncertainty and Case Difficulty
CheXthought-VLM's ability to predict both human-human and AI-human disagreement directly from images empowers AI systems to communicate inherent case ambiguity and model reliability. High predicted human-human disagreement can flag cases for more experienced review, while high AI-human disagreement can indicate when the model's output should not be a primary decision support signal.
This transparent communication of uncertainty and disagreement is vital. It enables dynamic control over how AI outputs are surfaced, prioritized, or withheld in clinical workflows, reducing automation bias and clinician burden. By stratifying cases based on difficulty and model reliability, CheXthought fosters more accurate, interpretable, and actionable AI for radiologists.
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