View PDF ↗
PDF Viewer
Open Full PDF

BUILDER'S SANDBOX

Build This Paper

Use an AI coding agent to implement this research.

OpenAI Codex
OpenAI CodexAI Agent

Lightweight coding agent in your terminal.

Claude Code
Claude CodeAI Agent

Agentic coding tool for terminal workflows.

AntiGravity IDE
AntiGravity IDEScaffolding

AI agent mindset installer and workflow scaffolder.

Cursor
CursorIDE

AI-first code editor built on VS Code.

VS Code
VS CodeIDE

Free, open-source editor by Microsoft.

MVP Investment

$9K - $12K
6-10 weeks
Engineering
$8,000
Cloud Hosting
$240
SaaS Stack
$300
Domain & Legal
$100

6mo ROI

2-4x

3yr ROI

10-20x

Lightweight AI tools can reach profitability quickly. At $500/mo average contract, 20 customers = $10K MRR by 6mo, 200+ by 3yr.

Talent Scout

J

Jianmin Li

Macao Polytechnic University

Y

Ying Chang

Zhejiang Chinese Medical University

S

Su-Kit Tang

Macao Polytechnic University

Y

Yujia Liu

Zhejiang Chinese Medical University

Find Similar Experts

Medical experts on LinkedIn & GitHub

References (76)

[1]
Dual retrieving and ranking medical large language model with retrieval augmented generation
2025Qimin Yang, Huan Zuo et al.
[2]
Retrieval augmented generation for 10 large language models and its generalizability in assessing medical fitness
2025Yuhe Ke, Liyuan Jin et al.
[3]
MedReason: Eliciting Factual Medical Reasoning Steps in LLMs via Knowledge Graphs
2025Juncheng Wu, Wenlong Deng et al.
[4]
Medical large language models are easily distracted
2025Krithik Vishwanath, Anton Alyakin et al.
[5]
ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning
2025Mingyang Chen, Tianpeng Li et al.
[6]
R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning
2025Huatong Song, Jinhao Jiang et al.
[7]
MedRAG: Enhancing Retrieval-augmented Generation with Knowledge Graph-Elicited Reasoning for Healthcare Copilot
2025Xuejiao Zhao, Siyan Liu et al.
[8]
DeepRAG: Thinking to Retrieve Step by Step for Large Language Models
2025Xinyan Guan, Jiali Zeng et al.
[9]
Current applications and challenges in large language models for patient care: a systematic review
2025Felix Busch, Lena Hoffmann et al.
[10]
Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG
2025Aditi Singh, Abul Ehtesham et al.
[11]
Improving large language model applications in biomedicine with retrieval-augmented generation: a systematic review, meta-analysis, and clinical development guidelines
2025Siru Liu, Allison B. McCoy et al.
[12]
Toward expert-level medical question answering with large language models
2025Karan Singhal, Tao Tu et al.
[13]
Tree-based RAG-Agent Recommendation System: A Case Study in Medical Test Data
2025Yahe Yang, Chengyue Huang
[14]
Medical Graph RAG: Evidence-based Medical Large Language Model via Graph Retrieval-Augmented Generation
2025Junde Wu, Jiayuan Zhu et al.
[15]
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
2025Adam Suma, Samuel Dauncey
[16]
TCM MLKG-RAG: Traditional Chinese Medicine Intelligent Diagnosis Based on Multi-Layer Knowledge Graph Retrieval-Augmented Generation
2024Qi Chen, Lin Ni
[17]
Evaluation of the integration of retrieval-augmented generation in large language model for breast cancer nursing care responses
2024Ruiyu Xu, Ying Hong et al.
[18]
Custom Large Language Models Improve Accuracy: Comparing Retrieval Augmented Generation and Artificial Intelligence Agents to Non-Custom Models for Evidence-Based Medicine.
2024Joshua J. Woo, A. Yang et al.
[19]
Simple is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation
2024Mufei Li, Siqi Miao et al.
[20]
MinerU: An Open-Source Solution for Precise Document Content Extraction
2024Bin Wang, Chaochao Xu et al.

Showing 20 of 76 references

Founder's Pitch

"Develop an AI tool for personalized syndrome differentiation in Traditional Chinese Medicine using advanced reasoning techniques."

Medical AIScore: 7View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

2/4 signals

5

Quick Build

3/4 signals

7.5

Series A Potential

4/4 signals

10

Sources used for this analysis

arXiv Paper

Full-text PDF analysis of the research paper

GitHub Repository

Code availability, stars, and contributor activity

Citation Network

Semantic Scholar citations and co-citation patterns

Community Predictions

Crowd-sourced unicorn probability assessments

Analysis model: GPT-4o · Last scored: 2/26/2026

🔭 Research Neighborhood

Generating constellation...

~3-8 seconds

Why It Matters

Without this approach, personalized diagnosis in Traditional Chinese Medicine (TCM) remains difficult, due to diverse clinical schools and reasoning methods, resulting in varied treatment effectiveness.

Product Angle

To productize this, a software tool could be developed that sits as a decision support system for TCM clinics, helping practitioners by providing differential diagnosis and treatment suggestions tailored to specific schools of thought within TCM.

Disruption

This technology can replace manual and subjective diagnosis methods traditionally used in TCM, providing a more standardized and evidence-based approach to patient care.

Product Opportunity

With a growing global interest in TCM, especially in regions like China and the rest of Asia, there is a large market for diagnostic tools and supporting software. Clinics and hospitals that practice TCM could be potential customers.

Use Case Idea

Develop a medical decision support tool that offers personalized diagnosis recommendations for TCM practitioners based on specific patient symptoms and clinician's school of thought.

Science

The study introduces a framework called TCM-DiffRAG, which combines knowledge graphs and chain of thought reasoning to bolster retrieval-augmented generation for TCM syndrome differentiation. This approach allows for personalized diagnosis by constructing both general and individual knowledge graphs based on TCM schools.

Method & Eval

The framework was tested on TCM datasets, showing improved performance over existing models and methods by enhancing the accuracy of reasoning and diagnosis. Benchmark tests indicated significant accuracy improvements.

Caveats

The complexity of integrating varied reasoning systems might increase development time and cost. Additionally, there is a need for continual knowledge graph updates to ensure accuracy which could be challenging.

Author Intelligence

Jianmin Li

Macao Polytechnic University

Ying Chang

Zhejiang Chinese Medical University

Su-Kit Tang

Macao Polytechnic University

Yujia Liu

Zhejiang Chinese Medical University

Yanwen Wang

Hangzhou Ganzhicao Technology Co., Ltd

Shuyuan Lin

Zhejiang Chinese Medical University

Binkai Ou

Guangdong Institute of Intelligence Science and Technology