# LUCAID: Agentic Multimodal AI for Lung Cancer Precision Pathology

arXiv: https://arxiv.org/abs/2608.23803 · HTML: https://mlsanity.com/paper/2608.23803/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 88 references that are in the map.
- First posted on arXiv 24 Aug 2026.

## Builds on (most cited first, 11 of 11)

- [RudolfV: A Foundation Model by Pathologists for Pathologists](https://mlsanity.com/paper/2401.04079/) (2024)
- [Evidence-based diagnostic reasoning with multi-agent copilot for human pathology](https://mlsanity.com/paper/2506.20964/) (2025)
- [Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs](https://mlsanity.com/paper/2403.07743/) (2024)
- [Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charit'e, and Aignostics](https://mlsanity.com/paper/2501.05409/) (2025)
- [Atlas 2 -- Foundation models for clinical deployment](https://mlsanity.com/paper/2601.05148/) (2026)
- [QCAgent: An agentic framework for quality-controllable pathology report generation from whole slide image](https://mlsanity.com/paper/2603.01647/) (2026)
- [PathView-Bench: Can Multimodal Large Language Models Achieve Fine-grained Multiscale Understanding of Pathology Images?](https://mlsanity.com/paper/2607.28318/) (2026)
- [A tissue and cell-level annotated H&E and PD-L1 histopathology image dataset in non-small cell lung cancer](https://mlsanity.com/paper/2507.16855/) (2025)
- [Towards Comprehensive Cellular Characterisation of H&E slides](https://mlsanity.com/paper/2508.09926/) (2025)
- [A Multimodal Agentic Pathology Co-pilot via Evidence Grounded Reasoning](https://mlsanity.com/paper/2606.08093/) (2026)
- [Atlas H&E-TME: Scalable AI-Based Tissue Profiling at Expert Pathologist-Level Accuracy](https://mlsanity.com/paper/2606.12346/) (2026)

## Abstract (arXiv)

Lung cancer tissue diagnostics is complex, as therapy decisions in precision oncology rely on the integration of histomorphological, immunohistochemical, and molecular features. Yet pathological assessment remains largely visual and semi-quantitative and shows interobserver variability, while existing artificial intelligence (AI) tools cover only selected tasks, rarely reach generalizable expert-level performance, and lack prospective clinical validation. To address these challenges, we developed and clinically validated LUCAID, an agentic AI system for precision lung cancer pathology. An integrative agent couples diagnostic reasoning with nine modules that cover the full routine workflow, from quality control, tumor detection and segmentation, histological subtyping, tumor microenvironment profiling, tumor cellularity quantification, and predictive biomarker scoring (PD-L1, MET, TROP-2) to automated structured report generation. LUCAID enables users to interactively query the module outputs and generate reports that contextualize the results. Against large-scale expert ground-truth annotations, the analysis modules achieved F1 scores of 0.82-0.95. In prospective clinical validation, LUCAID reached 93.0% concordance with an expert-panel adjudicated reference standard across clinically actionable decisions, compared with 68.3-81.1% for five experienced thoracic pathologists.