---
title: "Unique Friend | Quanzhen Medicine’s Xue Chong: Foundation Models Will Amplify Existing Medical Capacity"
author: "Unique Research"
sourcePublication: "Unique Research (FFCAP English library)"
originalPublishedAt: "2024-01-22T20:05:00+08:00"
canonical: "https://ffcap.cn/en/research/20240122a08wb300"
source: "https://ffcap.cn/en/research/20240122a08wb300"
language: "en"
cnSourceUrl: "https://view.inews.qq.com/a/20240122A08WB300"
---

# Unique Friend | Quanzhen Medicine’s Xue Chong: Foundation Models Will Amplify Existing Medical Capacity

_Unique Research · Insight / Unique Friend Q&A · January 22, 2024 · Guest: Xue Chong, Founder, Quanzhen Medicine (全诊医学)_

Inventory type remains **insight** (Unique Friend series — not partner welcome).

## Guest

**Xue Chong** — M.D. in surgery, Peking Union Medical College; postdoc, Johns Hopkins; distinguished researcher, Beihang Hangzhou Innovation Institute; formerly clinical work at Zhejiang University Second Affiliated Hospital. Founded Quanzhen Medicine in January 2016 for medical AI R&D and application. Core team mixes M.D.s and computer scientists; 40+ invention patents and software copyrights on self-developed products.

## Q&A

**1\. Product and core scenes?**  
**Quanzhen AI** is generative AI for automatic medical text — a doctor’s AI assistant tackling heavy documentation across three scenes: pre-visit patient dialogue → pre-consultation notes; understanding doctor–patient dialogue → outpatient/inpatient notes; doctor↔AI dialogue → ward-round or operative notes. It also offers clinical decision support: diagnostic suggestions and further test/treatment recommendations.

**2\. 2023 productization / market?**  
Product is mature and market-validated — among the largest doctor-user bases and globally leading in its niche. Users span independent clinic doctors and large hospitals. Biggest 2023 challenge and win: productizing for Class-III Grade-A hospitals. Co-developed with Zhejiang Provincial People’s Hospital and Sir Run Run Shaw Hospital (ZJU) — inpatient full-flow AI notes and outpatient AI notes respectively — then to nearly **30** Class-III Grade-A hospitals within a year.

**3\. GenAI / LLM opportunities vs past waves?**  
GenAI meets content-generation demand across industries; LLMs cut AI R&D cost and push computers toward human-like language — the symbol that fuses hearing and vision, enabling AI Agents that perceive and act. Differences vs past: (1) AI startups run leaner — they save customers’ headcount _and_ use AI on their own ops; (2) deep industry natives move faster on real domain opportunities.

**4\. China landing challenges?**  
Weakest link: domestic general LLMs still lag overseas commercial and open-source stacks. Two modes emerge — iOS-like closed OS (OpenAI, Gemini) vs Android-like open (Llama). AIGC startups must pick models by need and vertical.

**5\. Competing with giants’ “dimensionality reduction”?**  
Since GPT-3.5, output capability between giants and startups has leveled. Moats: customer scale + high-quality closed data; hit pain points, productize fast, and recirculate product data into a new barrier.

**6\. Are models still too weak?**  
Depends on the problem and how far you need to solve it. Even if the base model is short, product overlays can reach customer-perceived value people will pay for.

**7\. Human–machine collaboration paradigm?**  
Like Agents, collaboration goes multimodal — ear/mouth (listen/speak) and eye/hand (read/write), the way WeChat chats with real friends. ChatGPT already made AI collaboration feel like messaging a friend. Quanzhen AI lets doctors write notes in multiple modes, not only keyboard typing.

**8\. 2024 tech to watch?**  
(1) Multimodal model progress as the substrate for broad Agents; (2) AI fused with machines — EVs, medical robots — coupling “brain” and “muscle” intelligence.

_1 CN image; attach on ingest. Signup CTA in CN omitted. Title framing: models/AI amplify existing medical resource potential._

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Editorial English edition published on UniqueCapital: https://ffcap.cn/en/research/20240122a08wb300
On-site reading page: https://ffcap.cn/en/research/20240122a08wb300