# I-AI: A Controllable & Interpretable AI System for Decoding Radiologists' Intense Focus for Accurate CXR Diagnoses

arXiv: https://arxiv.org/abs/2309.13550 · HTML: https://mlsanity.com/paper/2309.13550/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 56 references that are in the map.
- First posted on arXiv 24 Sep 2023.

## Builds on (most cited first, 30 of 40)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)](https://mlsanity.com/paper/1711.11279/) (2017)
- [Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks](https://mlsanity.com/paper/1710.11063/) (2017)
- [This Looks Like That: Deep Learning for Interpretable Image Recognition](https://mlsanity.com/paper/1806.10574/) (2018)
- [Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks](https://mlsanity.com/paper/1910.01279/) (2019)
- [CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning](https://mlsanity.com/paper/1711.05225/) (2017)
- [Big Self-Supervised Models Advance Medical Image Classification](https://mlsanity.com/paper/2101.05224/) (2021)
- [Side Adapter Network for Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2302.12242/) (2023)
- [Customized Segment Anything Model for Medical Image Segmentation](https://mlsanity.com/paper/2304.13785/) (2023)
- [Neural Prototype Trees for Interpretable Fine-grained Image Recognition](https://mlsanity.com/paper/2012.02046/) (2020)
- [Interpretable Image Classification with Differentiable Prototypes Assignment](https://mlsanity.com/paper/2112.02902/) (2021)
- [ProtoPShare: Prototype Sharing for Interpretable Image Classification and Similarity Discovery](https://mlsanity.com/paper/2011.14340/) (2020)
- [Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model](https://mlsanity.com/paper/2005.07377/) (2020)
- [Creation and Validation of a Chest X-Ray Dataset with Eye-tracking and Report Dictation for AI Development](https://mlsanity.com/paper/2009.07386/) (2020)
- [Thoracic Disease Identification and Localization with Limited Supervision](https://mlsanity.com/paper/1711.06373/) (2017)
- [A scoping review of transfer learning research on medical image analysis using ImageNet](https://mlsanity.com/paper/2004.13175/) (2020)
- [Deep reinforcement learning in medical imaging: A literature review](https://mlsanity.com/paper/2103.05115/) (2021)
- [REFLACX, a dataset of reports and eye-tracking data for localization of abnormalities in chest x-rays](https://mlsanity.com/paper/2109.14187/) (2021)
- [SAM3D: Segment Anything Model in Volumetric Medical Images](https://mlsanity.com/paper/2309.03493/) (2023)
- [Learning Hierarchical Attention for Weakly-supervised Chest X-Ray Abnormality Localization and Diagnosis](https://mlsanity.com/paper/2112.12349/) (2021)
- [Weakly Supervised Medical Diagnosis and Localization from Multiple Resolutions](https://mlsanity.com/paper/1803.07703/) (2018)
- [Weakly Supervised Deep Learning for Thoracic Disease Classification and Localization on Chest X-rays](https://mlsanity.com/paper/1807.06067/) (2018)
- [Integrated Grad-CAM: Sensitivity-Aware Visual Explanation of Deep Convolutional Networks via Integrated Gradient-Based Scoring](https://mlsanity.com/paper/2102.07805/) (2021)

## Built on by (most cited first, 10 of 10)

- [CLIP in Medical Imaging: A Survey](https://mlsanity.com/paper/2312.07353/) (2023)
- [Foundation Models in Medical Imaging: A Review and Outlook](https://mlsanity.com/paper/2506.09095/) (2025)
- [Vision-Aware Text Features in Referring Image Segmentation: From Object Understanding to Context Understanding](https://mlsanity.com/paper/2404.08590/) (2024)
- [MENTOR: Human Perception-Guided Pretraining for Increased Generalization](https://mlsanity.com/paper/2310.19545/) (2023)
- [FG-CXR: A Radiologist-Aligned Gaze Dataset for Enhancing Interpretability in Chest X-Ray Report Generation](https://mlsanity.com/paper/2411.15413/) (2024)
- [MedPatch: Confidence-Guided Multi-Stage Fusion for Multimodal Clinical Data](https://mlsanity.com/paper/2508.09182/) (2025)
- [CarcassFormer: An End-to-end Transformer-based Framework for Simultaneous Localization, Segmentation and Classification of Poultry Carcass Defect](https://mlsanity.com/paper/2404.11429/) (2024)
- [GazeSearch: Radiology Findings Search Benchmark](https://mlsanity.com/paper/2411.05780/) (2024)
- [Explainable Artificial Intelligence in Biomedical Image Analysis: A Comprehensive Survey](https://mlsanity.com/paper/2507.07148/) (2025)
- [Aligning Characteristic Descriptors with Images for Human-Expert-like Explainability](https://mlsanity.com/paper/2411.04008/) (2024)

## Abstract (arXiv)

In the field of chest X-ray (CXR) diagnosis, existing works often focus solely on determining where a radiologist looks, typically through tasks such as detection, segmentation, or classification. However, these approaches are often designed as black-box models, lacking interpretability. In this paper, we introduce Interpretable Artificial Intelligence (I-AI) a novel and unified controllable interpretable pipeline for decoding the intense focus of radiologists in CXR diagnosis. Our I-AI addresses three key questions: where a radiologist looks, how long they focus on specific areas, and what findings they diagnose. By capturing the intensity of the radiologist's gaze, we provide a unified solution that offers insights into the cognitive process underlying radiological interpretation. Unlike current methods that rely on black-box machine learning models, which can be prone to extracting erroneous information from the entire input image during the diagnosis process, we tackle this issue by effectively masking out irrelevant information. Our proposed I-AI leverages a vision-language model, allowing for precise control over the interpretation process while ensuring the exclusion of irrelevant features. To train our I-AI model, we utilize an eye gaze dataset to extract anatomical gaze information and generate ground truth heatmaps. Through extensive experimentation, we demonstrate the efficacy of our method. We showcase that the attention heatmaps, designed to mimic radiologists' focus, encode sufficient and relevant information, enabling accurate classification tasks using only a portion of CXR. The code, checkpoints, and data are at https://github.com/UARK-AICV/IAI