@aigclink: Recently participated in training a vertical medical model in the laboratory department of a top domestic hospital. Sharing some personal views and advice for those working in AI+Healthcare (take it if useful, leave comments if not):

Recently participated in training a vertically specialized medical model for the laboratory department of a leading domestic hospital. Sharing some personal insights and suggestions for practitioners in the AI + healthcare field (take it if you find it useful, leave a comment below to roast if you don’t):

1. Data is the core of the medical field. In China, public and top-tier (Tier 3A) hospitals basically keep their data within the hospital, so there are tons of opportunities for AI to land in niche areas—almost every department can train a small model to replace relevant scenarios.

2. For general medical models, Baichuan’s model has already done a great job domestically. Basic medical initial consultations are pretty much no longer a big issue, and it’s gradually approaching intermediate level. What everyone values most is the evidence chain. This is feedback from doctors at many leading hospitals. I tried it myself, and it’s indeed pretty good—better than local hospital doctors on apps like Haodf and Dxy for chronic conditions and various minor medical issues, at least in terms of comprehensive judgment, the effect is stronger than average doctors.

3. The biggest barrier to hospitals using large models is the localization cost issue. Since data can’t leave the hospital, cloud services can’t be used, so localization involves hardware costs—this is the core reason hindering many hospitals from deeply adopting large models.

4. In the short term, hospitals have very weak willingness to pay for software alone. Pairing with hardware offers a better chance to get direct payments from hospitals, so binding with medical hardware vendors is one viable path for AI software companies.

5. There are tons of directions to explore in medical AI. Data ethics is the biggest threshold; once you’re in, data annotation is the biggest cost. This kind of data requires professional doctors for annotation—annotations by regular people are pretty much useless. So getting from 0 to 1 is crucial; there are almost no shortcuts.

#ai医疗 #百川医疗

Feedback from some doctors, of course it doesn’t represent all doctors:

Yes, ethics is the first threshold.

Most HIS systems are such a mess that the hospitals themselves don’t even know what’s going on, and they can’t provide APIs at all.

Someone has packaged an entire marketing team—CRO, copywriting, SEO, growth, pricing, GTM, strategy—into 47 AI skills. Once you load them up, your Claude Code can act as half a CMO.

The foundation of all skills is product-marketing: before any other skill gets to work, it reads that first to get “your product, audience, positioning.” Skills reference each other: copywriting → CRO → A/B testing, customer research → copywriting / CRO / competitors. This effectively replicates the “shared context + division of labor SOP” of a real marketing team into the agent. At the same time, each skill comes with a references/ (method library) and evals/ (actual evaluations)—production-grade, not just a pile of prompts.

A few standout skills:

1. marketing-council: simulates an “advisory board,” giving you multiple expert perspectives;
2. marketing-loops: allows the agent to run automated cyclical marketing workflows (the loop / self-running approach again);
3. ai-seo: specifically optimized for “being cited by LLMs and appearing in AI-generated answers”—very timely;
4. programmatic-seo: templates + batch page generation from data.

“Use this set of skills as your autonomous AI CMO.” Once the agent is equipped with these skills, it can replace half of outsourced marketing work.

Combined with last week’s talk about whether “design taste can be packaged as a skill” (Hallmark, Emil Kowalski), this week the entire marketing function is also systematically embedded into the agent. The two together can quickly run small MVPs.

#MarketingSkills #ClaudeCode #AI营销 #AgentSkills #增长