# MedDM:LLM-executable clinical guidance tree for clinical decision-making

arXiv: https://arxiv.org/abs/2312.02441 · HTML: https://mlsanity.com/paper/2312.02441/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 5 Dec 2023.

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

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Generalized Planning as Heuristic Search](https://mlsanity.com/paper/2103.14434/) (2021)
- [A Benchmark of PDF Information Extraction Tools using a Multi-Task and Multi-Domain Evaluation Framework for Academic Documents](https://mlsanity.com/paper/2303.09957/) (2023)

## Built on by (most cited first, 19 of 19)

- [Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely](https://mlsanity.com/paper/2409.14924/) (2024)
- [Improving Medical Reasoning through Retrieval and Self-Reflection with Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2401.15269/) (2024)
- [A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions](https://mlsanity.com/paper/2406.03712/) (2024)
- [Explainability in Practice: A Survey of Explainable NLP Across Various Domains](https://mlsanity.com/paper/2502.00837/) (2025)
- [Baichuan-M3: Modeling Clinical Inquiry for Reliable Medical Decision-Making](https://mlsanity.com/paper/2602.06570/) (2026)
- [The Multi-Round Diagnostic RAG Framework for Emulating Clinical Reasoning](https://mlsanity.com/paper/2504.07724/) (2025)
- [Integrating Chain-of-Thought and Retrieval Augmented Generation Enhances Rare Disease Diagnosis from Clinical Notes](https://mlsanity.com/paper/2503.12286/) (2025)
- [Development and Testing of a Novel Large Language Model-Based Clinical Decision Support Systems for Medication Safety in 12 Clinical Specialties](https://mlsanity.com/paper/2402.01741/) (2024)
- [MedGuideX: Internalizing Decision Logic from Executable Guidelines into Large Language Models for Clinical Reasoning](https://mlsanity.com/paper/2605.26567/) (2026)
- [CPGPrompt: Translating Clinical Guidelines into LLM-Executable Decision Support](https://mlsanity.com/paper/2601.03475/) (2026)
- [Critique of Impure Reason: Unveiling the reasoning behaviour of medical Large Language Models](https://mlsanity.com/paper/2412.15748/) (2024)
- [Guideline2Graph: Profile-Aware Multimodal Parsing for Executable Clinical Decision Graphs](https://mlsanity.com/paper/2604.02477/) (2026)
- [AnesSuite: A Comprehensive Benchmark and Dataset Suite for Anesthesiology Reasoning in LLMs](https://mlsanity.com/paper/2504.02404/) (2025)
- [MedGellan: LLM-Generated Medical Guidance to Support Physicians](https://mlsanity.com/paper/2507.04431/) (2025)
- [Analysis of Image-and-Text Uncertainty Propagation in Multimodal Large Language Models with Cardiac MR-Based Applications](https://mlsanity.com/paper/2507.12945/) (2025)
- [GuideSkill: Evolving Executable LLM Agent Skills for Guideline-Grounded Clinical Reasoning](https://mlsanity.com/paper/2607.26160/) (2026)
- [Tipta uzmanlik sinavinda (tus) buyuk dil modelleri insanlardan daha mi basarili?](https://mlsanity.com/paper/2408.12305/) (2024)
- [FloCA: Towards Faithful and Logically Consistent Flowchart Reasoning](https://mlsanity.com/paper/2602.14035/) (2026)
- [From PDF to Evidence: Structure-Aware Retrieval for Clinical Practice Guidelines](https://mlsanity.com/paper/2609.33447/) (2026)

## Abstract (arXiv)

It is becoming increasingly emphasis on the importance of LLM participating in clinical diagnosis decision-making. However, the low specialization refers to that current medical LLMs can not provide specific medical advice, which are more like a medical Q&A. And there is no suitable clinical guidance tree data set that can be used directly with LLM. To address this issue, we first propose LLM-executavle clinical guidance tree(CGT), which can be directly used by large language models, and construct medical diagnostic decision-making dataset (MedDM), from flowcharts in clinical practice guidelines. We propose an approach to screen flowcharts from medical literature, followed by their identification and conversion into standardized diagnostic decision trees. Constructed a knowledge base with 1202 decision trees, which came from 5000 medical literature and covered 12 hospital departments, including internal medicine, surgery, psychiatry, and over 500 diseases.Moreover, we propose a method for reasoning on LLM-executable CGT and a Patient-LLM multi-turn dialogue framework.