# PathWISE: Multi-Agent Cancer Pathway Triaging Ontology Learning from Clinical Flowcharts

arXiv: https://arxiv.org/abs/2605.25970 · HTML: https://mlsanity.com/paper/2605.25970/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 10 of its 35 references that are in the map.
- First posted on arXiv 25 May 2026.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 10 of 10)

- [Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding](https://mlsanity.com/paper/2210.03347/) (2022)
- [Draw with Thought: Unleashing Multimodal Reasoning for Scientific Diagram Generation](https://mlsanity.com/paper/2504.09479/) (2025)
- [Beyond End-to-End VLMs: Leveraging Intermediate Text Representations for Superior Flowchart Understanding](https://mlsanity.com/paper/2412.16420/) (2024)
- [MedMMV: A Controllable Multimodal Multi-Agent Framework for Reliable and Verifiable Clinical Reasoning](https://mlsanity.com/paper/2509.24314/) (2025)
- [Leveraging LLM for Automated Ontology Extraction and Knowledge Graph Generation](https://mlsanity.com/paper/2412.00608/) (2024)
- [Flowchart2Mermaid: A Vision-Language Model Powered System for Converting Flowcharts into Editable Diagram Code](https://mlsanity.com/paper/2512.02170/) (2025)
- [StarFlow: Generating Structured Workflow Outputs From Sketch Images](https://mlsanity.com/paper/2503.21889/) (2025)
- [Comprehensive Modeling and Question Answering of Cancer Clinical Practice Guidelines using LLMs](https://mlsanity.com/paper/2501.13984/) (2025)
- [MedObvious: Exposing the Medical Moravec's Paradox in VLMs via Clinical Triage](https://mlsanity.com/paper/2603.23501/) (2026)
- [RE-MCDF: Closed-Loop Multi-Expert LLM Reasoning for Knowledge-Grounded Clinical Diagnosis](https://mlsanity.com/paper/2602.01297/) (2026)

## Abstract (arXiv)

Clinical pathways are disseminated as visual flowcharts where spatial topology, arrow direction, colour coding, and font weight encode critical triage logic that remains inaccessible to computational systems. We present PathWISE, a five-phase pipeline combining four LLM-based agents with a deterministic depth-first search auditor and a Java compiler critic, transforming these non-computable artefacts into validated, executable HL7 Clinical Quality Language (CQL) libraries deployable as FHIR CDS Hooks services. Purpose-built agents extract flowchart structure into a typed directed graph, perform deterministic path enumeration, conduct a structured semantic audit of every node's computability, generate terminology-constrained CQL definitions verified by the official Java CQL-to-ELM compiler, and produce routing logic covering 100% of enumerated patient journeys. Demonstrated across five UK NHS cancer pathways (colorectal, lung, skin, upper GI, and breast), PathWISE audits up to 183 nodes (182 under the Hybrid configuration), identifies 544 structured governance findings across four issue categories, achieves 100% syntactic compilation success, with UNCOMPUTABLE nodes receiving false placeholders that preserve compilability while surfacing governance gaps for clinical review, and produces zero hallucinated terminology codes for dictionary-covered concepts. Critically, PathWISE confines non-deterministic LLM inference to knowledge extraction while deterministic graph mathematics and a standard compiler underpin every verification step.