医疗 × 真正落地的 AIHealthcare × Applied AI

把医疗推广最难的一端,
做成正在跑的 AI 系统。 Healthcare's hardest problem —
now running as AI systems.

医疗营销十余年,专攻处方药(Rx)线上推广这块公认最难的硬骨头——如今用 AI 把它做成规模化、可复制、会自迭代的系统。 A decade inside healthcare marketing's hardest problem — compliant online promotion of prescription (Rx) drugs — now built into AI systems that actually run.

联系合作Get in touch→ 看这些系统See the systems
核心卡位 · The Rx EdgeThe Rx Edge · 核心卡位

处方药线上推广,是医疗营销里公认最难啃的一块——我把它当成主场做了很多年。 Compliant online promotion of prescription drugs is the acknowledged hardest problem in healthcare marketing — and it's the game I've played for years.

它受严格的广告与合规约束,不能像消费品那样直接投放大众。真正的考验是:如何合规地触达专业人群,又如何把声量转化成真实的处方与市场份额。 It's bound by strict advertising and compliance limits — you can't market it to the public like a consumer product. The real test is reaching a professional audience compliantly, then converting attention into real prescriptions and market share.

这是大多数团队最头疼、也最稀缺的能力。我既走通了这条最难的路,又在用 AI 把它规模化——这是我最硬、也最难被复制的差异化。 It's the capability most teams struggle with and few possess. I've walked that hardest path — and I'm scaling it with AI. That's my sharpest, least-copyable edge.

关于About
位置Where I stand
医疗老兵 × 落地 AIHealthcare veteran × applied AI 十余年医疗营销实战底子A decade+ in real healthcare marketing
主场Home turf
处方药线上推广Online Rx promotion 合规触达 + 转化为真实处方Compliant reach → real prescriptions
打法Operating stance
判断先行 · Learn in PublicJudgment-first · Learn in Public 系统只是"说到做到"的证据The systems are just proof I act on it

我在医疗行业做了十余年,一直贴着这个行业最真实的一端做事——内容、营销、推广、商业运营。

这几年我做了一件身边多数同行没做的事:把 AI 从 PPT 里搬出来,真正落地成一套套能跑起来的系统。我不靠写代码为生——这恰恰是重点。AI 在医疗落地的门槛,从来不是纯技术,而是有没有一个真正懂这行规矩、合规红线和推广打法的人,愿意坐下来把它搭出来。懂医疗的多半不懂怎么把 AI 用起来,懂 AI 的又多半不懂医疗这套规矩;这个缝,就是机会。

我站的位置很窄,据我观察也几乎没人占:一个懂医疗的老兵,做出能跑的 AI 系统,而且正好站在医疗 AI 转型里"推广与营销"这一侧——而这一侧,整个行业还没醒过来。大家都在谈 AI 做药物研发、做诊断,却几乎没人去把"医疗如何触达受众、如何跑通商业引擎"这件事工业化。

我更愿意先讲每套系统背后的判断——判断才是真正的资产,跑着的系统只是我说到做到的证据。下面五套,都是已经搭起来、正在跑的东西。

I've spent over a decade inside healthcare — close to how the industry actually markets, promotes, and runs its commercial operations.

Over the last few years I've done what most people in my field have not: I've taken AI out of the slide decks and built it into real, running systems. I don't write production code for a living — and that's part of the point. The barrier to landing AI in healthcare was never raw technical fluency; it was whether someone who genuinely understands the industry's rules, its compliance lines, and its go-to-market reality would actually sit down and build. Most people who understand healthcare can't operationalize AI. Most people who understand AI don't understand healthcare's playbook. That gap is the opportunity.

The place I occupy is narrow and, as far as I can tell, largely uncontested: a healthcare veteran who ships working AI systems, standing exactly on the promotion-and-marketing side of healthcare's AI transformation — the side the industry hasn't woken up to. Everyone talks about AI for drug discovery and diagnostics; almost no one is industrializing how healthcare actually reaches its audiences and runs its commercial engine.

I lead with the judgment behind each system, because the judgment is the real asset — the working system is just proof I act on what I see. The five below are all built and running now.

旗舰案例Flagship systems

五套已经落地、正在跑的系统Five systems, built and running

不是概念,是每天在跑的东西。每一套都先讲背后的判断,因为判断才是真正的资产。Not concepts — systems that run every day. Each leads with the judgment behind it, because the judgment is the real asset.

01
旗舰Flagship 自动化流水线Automated pipeline ● 运行中● In production

医疗短视频的标准化生产线A standardized production line for AI video

把"脚本 → 配音 → 成片 → 字幕 → 质检 → 合规"整条链路,做成标准化、可复制的生产线,产能不再靠堆人。The full "script → voiceover → footage → captions → QA → compliance" chain, turned into a standardized, repeatable line — so output no longer scales by adding headcount.

远见The foresight

在医疗多年,我很早看清:内容营销真正的瓶颈,从来不是"能不能做出一条视频",而是能不能稳定地、规模化地、且在合规线内做出来。多数同行还把短视频当手工活——量小时裂缝不显,一旦上量,产能、质量、合规就开始互相打架。我的判断是:下一轮竞争不在单条视频多精致,而在谁能把"生产"这件事本身工程化——像工厂一样,有标准、有质检口、把合规焊进产线,而不是最后补。After years in healthcare, I saw it early: the real bottleneck was never whether a team could make a video — it was whether they could make videos reliably, at scale, and inside the compliance lines. Most peers still treat short-video as piecework. At low volume the cracks don't show; the moment you scale, throughput, quality and compliance start fighting each other. My read: the next axis of competition wouldn't be how polished any single video is, but who can engineer production itself — like a factory, with standards, inspection gates, and compliance welded into the line rather than bolted on at the end.

落地与意义Built & what it means

脚本进去,配音、生成画面、逐字字幕对齐、剪辑自动完成,中间设质检与合规闸口,另一头产出可用成片——不合规的内容根本到不了发布这一步。产能从"靠人"变成"靠流程":同样的人力,产能天花板换了一个量级,每条内容默认带合规兜底。任何面对"既要规模化内容、又要扛合规"的药企/医疗营销团队都能直接迁移。这是医疗内容营销必须完成的一跃——从作坊到产线,而多数人还没意识到这一跃已经可能。A script goes in; voiceover, generated visuals, word-level caption alignment and editing happen automatically, with QA and compliance gates in the middle — non-compliant content simply can't reach the publish step. Production shifts from depending on people to depending on process: same headcount, throughput ceiling on a different order of magnitude, every piece carrying a compliance backstop by default. Any pharma or healthcare marketing team facing the same "scale content and carry the regulatory burden at once" problem can lift this directly. It's the leap the field has to make — workshop to industrial line — and most haven't realized the leap is already possible.

02
内容运营Content operations ● 运行中● In production

全自动内容起号闭环A fully automated content-launch loop

一个自跑的 agent,把选题 → 制作 → 发布 → 复盘 → 迭代整条起号闭环跑起来,靠对标数据驱动,不靠个人手感。每天在两大主流内容平台上跑。An agent that runs the whole account-building loop on its own — topic selection, production, publishing, review, iteration — driven by competitor data rather than one operator's gut. Running daily across two dominant platforms.

远见The foresight

几乎所有人做号都是:多发、看数据、凭直觉改。问题在于——这套闭环的核心能力全长在一个操盘手脑子里。人一走,号就死;状态一差,号就飘。公司花钱养号,养出来的却是一种绑在个人身上、不沉淀的能力。我的判断是:起号应该被沉淀成系统,而系统的输入信号不该是"我自己发得怎么样",而是"这个赛道里谁在爆、为什么爆"。Almost everyone builds accounts by posting a lot, watching the numbers, and adjusting on instinct. The problem: the core competence lives entirely inside one operator's head. They leave, the account dies; they have an off week, it drifts. Companies pay to grow accounts and end up with a capability personally attached to an individual that never accumulates. My judgment: account-building should be captured as a system, and its input signal shouldn't be "how did my own posts do" but "who in this lane is breaking out, and why."

落地与意义Built & what it means

我把它做成一个每天自跑的内容 agent:自己选题、生产、定时发布、次日自动复盘数据、调整下一轮打法——从对标的爆款(尤其信号最干净的小号突围)里抓选题,而不是拍脑袋。价值在于:把起号从"靠个人手感的手艺"变成"可交接、可复制、会自我改进的系统"。对药企/医疗机构,这正戳中最大的新媒体痛点——人难招、难留、经验随人走。谁先把它系统化,谁就不再被稀缺的"手感天才"绑架。I turned it into a content agent that runs daily: it selects its own topics, produces, publishes on schedule, reviews the next-day data automatically, and adjusts the following round — pulling topic strategy from competitors' breakouts (especially small-account breakouts, where the signal is cleanest) instead of guessing. The value: turning account-building from a craft that depends on personal feel into a system that can be handed off, replicated, and improves itself. For pharma and healthcare orgs this hits their biggest new-media pain — talent is hard to hire, hard to keep, and takes its experience out the door. Whoever systematizes it first stops being hostage to the rare "instinct genius."

03
内容运营 · 方法论Methodology ● 运行中● In production

医生 KOL 科普短视频的 AI 编导法An AI show-running method for physician-KOL video

把"帮医生 KOL 做出既专业、又能过平台合规审核的科普视频"沉淀成一套可复用的编导方法论。全程脱敏,不碰药名、适应症、真实医生与机构。Turning "help a physician-KOL make science-communication video that is both genuinely expert and able to pass platform review" into a reusable show-running methodology. Fully de-identified.

远见The foresight

医生 KOL 内容里有个多数人忽略的结构性矛盾:平台一手鼓励专家科普,一手用越来越严的自动审核挡"套着科普壳的推广"。结果是大量医生号卡住——太专业没人看,够触达又怕越线被悄悄限流。行业的应对还很原始:靠一个编导的个人手感猜哪句危险。我的判断是:医疗内容合规,必然从"事后挨罚"转向"事前必须内建的能力";谁能让"专业表达"和"平台合规"这两件看似对立的事同时成立,谁就握住了医生 KOL 内容规模化的钥匙。In physician-KOL content there's a structural contradiction most overlook: platforms encourage expert education with one hand and use ever-stricter automated review to block "promotion dressed up as education" with the other. The result: a mass of physician accounts stuck — too expert and no one watches; enough reach and they risk crossing a line and getting quietly throttled. The industry's response is still primitive: rely on one show-runner's instinct for which sentence feels risky. My judgment: healthcare content compliance will inevitably shift from "punished after the fact" to "a capability you must build in beforehand" — and whoever can make expert expression and platform compliance hold true at once holds the key to scaling physician-KOL content.

落地与意义Built & what it means

我把它做成一套完整编导工作流:选题过评分模型;每个专业论点都要能追溯到真实文献(绝不让模型编数据);脚本贴合医生人设;发布前对照官方平台与广告规则逐条做合规自检。意义在于:药企/医疗机构手里常握着一串医生 KOL 资源,却因怕出合规事故不敢放开做内容。这套方法证明了"专业"和"合规"不是二选一——两者可以被工程化到同时成立,从而把大多数机构闲置的"医生 KOL"资产真正激活。I built it into a complete show-running workflow: topics run through a scoring model; every expert claim must trace to real literature (never let the model fabricate data); the script is shaped to the physician's persona; and before publishing it passes a compliance self-check mapped clause-by-clause against official platform and advertising rules. What it means: pharma and healthcare orgs often hold a roster of physician-KOL relationships but don't dare run content freely for fear of an incident. This proves expert and compliant is not either-or — the two can be engineered to hold at once, finally activating the physician-KOL asset most organizations leave dormant.

04
数据情报Data intelligence ● 运行中● In production

AI 市场情报抓取 + 自生长知识库AI market-intelligence capture + a self-growing knowledge base

一套自动抓取市场、竞品、政策情报,并持续沉淀进一个会自己长大的知识库的系统。A system that automatically captures market, competitor and policy intelligence and continuously deposits it into a knowledge base that keeps growing on its own.

远见The foresight

做得越久我越确信:真正的信息优势,不是"某次恰好查到了什么",而是"有没有一个东西在替你不停盯着市场、并把看到的复利下去"。现实里,多数团队的市场情报是事件驱动的:领导要看竞品,才临时去挖,存在自己电脑里,下次从零开始。政策和平台合规口径的变化——最快、也最要命的信息——往往等你注意到已经来不及反应了。我的判断是:情报能力的分水岭,是从"手动查"升级到"系统自动盯 + 自动沉淀",而底下的知识库必须是活的、在长的。The longer I've worked in healthcare, the more convinced I am that real information advantage isn't what you happened to find once — it's whether something is watching the market for you without pause and compounding what it sees. In reality most teams' market intelligence is event-driven: a leader wants a competitor read, someone scrambles to dig it up, stores it on their laptop, and next time starts from zero. Policy and platform-compliance shifts — the fastest-moving, most consequential information — are usually noticed only when it's already too late. My judgment: the dividing line is the upgrade from "look it up by hand" to "a system watches and deposits automatically," and the knowledge base underneath has to be alive and growing.

落地与意义Built & what it means

我把它做成两套相连的系统:一套跨平台自动情报抓取(搜索 → 采样 → 富化 → 去重 → 沉淀),一套托管在协作平台上、每周自我维护并交叉互链的知识库。所有真实产品、客户、文献细节均已脱敏,绝不对外披露。对药企/医疗机构,这正落在痛点上:竞品监控、政策法规跟踪、平台合规口径变化——这些既要快又要准、又最怕随人流失的场景,都能直接迁移。谁把情报和知识变成会自己长大的系统,谁就在下一轮牌发出来之前先看到了手牌。I built it into two linked systems: a cross-platform automated intelligence capture (search → sample → enrich → de-duplicate → deposit), and a knowledge base hosted on a collaboration platform that maintains itself weekly and cross-links its entries. All real product, client and literature details are de-identified and never disclosed. For pharma and healthcare orgs this sits right on their pain points: competitor monitoring, policy tracking, and shifts in platform-compliance interpretation — fast-and-accurate scenarios they most fear losing to turnover — all transfer directly. Whoever turns intelligence into a system that grows on its own sees the hand before the next round is even dealt.

05
工具能力Tooling capability ● 已交付● Delivered

语音克隆配音能力(通用音色克隆)Voice-cloning capability for voiceover

给一段声音样本加一段文字,就能克隆出任意自然音色做配音——本地运行,把"配音"这个卡规模化的成本与自然度问题一次解掉。Give it one voice sample plus text and it clones any natural timbre for voiceover — runs locally, dissolving the cost-and-naturalness problem that blocks voiceover at scale.

远见The foresight

凡是想规模化做内容的人,最后都会撞上同一堵墙:配音。所有人的注意力都在脚本和画面上,却忽略了配音才是决定一条科普/培训视频"专不专业"的隐形门槛——机械合成音一开口,可信度先掉一半;请真人,成本和周期又撑不起规模。我的判断是:随着内容体量变大,"自然、可控、低成本的配音"会从不起眼的技术细节,变成真正卡产能的咽喉——而解法恰好在此刻成熟:语音克隆已经好到能本地免费跑、又足够自然。Anyone scaling content eventually hits the same wall: voiceover. Everyone's attention goes to script and visuals, and they overlook that voiceover is the hidden threshold deciding whether a science or training video reads as professional. A mechanical synthetic voice drops credibility by half the moment it speaks; a human's cost and turnaround can't support scale. My judgment: as content volume grows, "natural, controllable, low-cost voiceover" turns from an unremarkable detail into the real chokepoint — and the solution matured at exactly the right moment: cloning is now good enough to run locally for free while sounding convincingly natural.

落地与意义Built & what it means

我把它做成一个通用音色克隆能力:丢进一段声音样本和对应文字,就能用那个音色读任何脚本,跑在本地 GPU 上,不依赖付费云服务。它把配音从"外包、受制于人"变成"可控、随取随用的第一方能力",并无缝接进第 1 套的视频产线,补上了内容工业化的最后一块缺口——无论是大批量科普口播、内部培训片,还是多音色产品讲解,都能低成本、稳定、成规模地产出。别人还卡在"配音假、真人贵",这个环节对我已经不是问题。I built it into a general-purpose timbre-cloning capability: drop in a voice sample and its matching text, and it clones that timbre to read any script, running on a local GPU with no dependence on paid cloud services. It shifts voiceover from "outsourced and at someone else's mercy" to a first-party capability that's controllable and available on demand, and plugs seamlessly into the video line in Case 1 — closing the last gap in industrializing content. High-volume narration, internal training video, multi-voice product explainers: all producible at low cost, reliably, at scale. While others are stuck on "the voiceover sounds fake and real actors are too expensive," this link is no longer a problem for me.

方法Method

我不写代码,却把想法变成能跑的系统——靠的是一套打法I don't write code, yet I ship working systems — here's the operating method

代码对我是自我表达的媒介,不是门槛。真正让 AI 在医疗落地的,是判断、规矩,和"明着做出来给人看"。Code is a medium of expression for me, not a barrier. What actually lands AI in healthcare is judgment, domain rules, and building it in the open.

01

判断先行Judgment first

先看清行业往哪走,再动手。系统只是"我说到做到"的证据,判断才是真正的资产。See where the industry is heading, then build. The system is proof I act on what I see; the judgment is the asset.

02

合规内建Compliance welded in

不做事后检查,把合规风控前置焊进流程——让"专业表达"和"平台过审"同时成立。Not a final human check — compliance is welded into the pipeline, so expert expression and passing review hold true at once.

03

从手艺到系统Craft → system

把原本靠堆人、靠个人手感的活,做成标准化、可复制、会自我迭代的系统。Turn work that depended on headcount and personal feel into standardized, repeatable, self-iterating systems.

04

Learn in PublicLearn in Public

学会了没人知道,等于没学会。所以我明着学、明着做,过程本身就是最难被复制的证据。Learning no one sees is not learning. So I learn and build in the open — the process itself is the least-copyable proof.

能力标签Capabilities
处方药线上推广Rx digital promotion 医疗内容营销Healthcare marketing 合规内容风控Regulatory-compliant content AI 落地应用Applied AI AI Agent / 流程自动化AI agents / workflow automation AIGC 短视频营销Short-video marketing 内容运营Content operations 市场情报Market intelligence 医生 KOL / IP 运营KOL / physician-IP ops 语音克隆 / TTSVoice cloning / TTS 团队管理Team leadership
联系Contact

在医疗 AI 落地这条路上找人?
直接找我。Looking for someone on the healthcare-AI path?
Reach out.

如果你在医疗、药企的数字化、内容营销、商业化转型上正遇到这些问题,或在为相关岗位、合作找人——欢迎连接、私信交流。If you're wrestling with digitalization, content marketing, or commercial transformation in healthcare and pharma — or hiring for it — let's connect.

领英 · 私信Connect on LinkedIn 邮箱Email X 主页X (Twitter)

最好的联系方式是领英私信。全部案例均已脱敏,不涉及任何真实客户、机构、医生或产品。Best reached via LinkedIn DM. All cases are de-identified — no real clients, institutions, physicians or products.