# Doctor-in-the-Loop: An Explainable, Multi-View Deep Learning Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer

arXiv: https://arxiv.org/abs/2502.17503 · HTML: https://mlsanity.com/paper/2502.17503/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 30 references that are in the map.
- First posted on arXiv 21 Feb 2025.

## Builds on (most cited first, 4 of 4)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [RadioTransformer: A Cascaded Global-Focal Transformer for Visual Attention-guided Disease Classification](https://mlsanity.com/paper/2202.11781/) (2022)
- [Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks](https://mlsanity.com/paper/2410.02331/) (2024)
- [Multi-Dataset Multi-Task Learning for COVID-19 Prognosis](https://mlsanity.com/paper/2405.13771/) (2024)

## Built on by (most cited first, 7 of 7)

- [Expert-Guided Explainable Few-Shot Learning for Medical Image Diagnosis](https://mlsanity.com/paper/2509.08007/) (2025)
- [Expert-Guided Explainable Few-Shot Learning with Active Sample Selection for Medical Image Analysis](https://mlsanity.com/paper/2601.02409/) (2026)
- [Multimodal Doctor-in-the-Loop: A Clinically-Guided Explainable Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer](https://mlsanity.com/paper/2505.01390/) (2025)
- [Learning to Select Like Humans: Explainable Active Learning for Medical Imaging](https://mlsanity.com/paper/2602.13308/) (2026)
- [Cross-dataset Multivariate Time-series Model for Parkinson's Diagnosis via Keyboard Dynamics](https://mlsanity.com/paper/2510.15950/) (2025)
- [Longitudinal NSCLC Treatment Progression via Multimodal Generative Models](https://mlsanity.com/paper/2603.06147/) (2026)
- [Learning from Limited and Incomplete Data: A Multimodal Framework for Predicting Pathological Response in NSCLC](https://mlsanity.com/paper/2603.15100/) (2026)

## Abstract (arXiv)

Non-small cell lung cancer (NSCLC) remains a major global health challenge, with high post-surgical recurrence rates underscoring the need for accurate pathological response predictions to guide personalized treatments. Although artificial intelligence models show promise in this domain, their clinical adoption is limited by the lack of medically grounded guidance during training, often resulting in non-explainable intrinsic predictions. To address this, we propose Doctor-in-the-Loop, a novel framework that integrates expert-driven domain knowledge with explainable artificial intelligence techniques, directing the model toward clinically relevant anatomical regions and improving both interpretability and trustworthiness. Our approach employs a gradual multi-view strategy, progressively refining the model's focus from broad contextual features to finer, lesion-specific details. By incorporating domain insights at every stage, we enhance predictive accuracy while ensuring that the model's decision-making process aligns more closely with clinical reasoning. Evaluated on a dataset of NSCLC patients, Doctor-in-the-Loop delivers promising predictive performance and provides transparent, justifiable outputs, representing a significant step toward clinically explainable artificial intelligence in oncology.