Partnering with Anthropic and Google: what it means for patient care

When we announced our technology partnerships with Anthropic and Google, the most common question we received was: “What does this actually mean?” Not in a press release sense — in a practical, what-changes-for-the-doctor-on-Monday-morning sense.

That is a fair question, and it deserves a honest answer.

Why partnerships matter more than models

The AI landscape moves fast enough that any specific model we use today will be outdated within a year. GPT-4 was state of the art twelve months ago. Claude 3 redefined what was possible six months later. Gemini introduced multimodal capabilities that would have seemed like science fiction two years ago. The specific model is not the moat — the integration is.

Our partnerships with Anthropic and Google are not about using their models. Any developer with an API key can use their models. The partnerships are about collaboration on the problems that matter most in clinical AI — safety, reliability, and domain-specific performance.

We are not licensing technology. We are co-developing the standards for what responsible clinical AI looks like.

What Anthropic brings

Anthropic was founded with a specific thesis: that AI safety is not a theoretical concern for the future but a practical engineering problem for today. Their work on Constitutional AI — training models to be helpful, harmless, and honest — aligns directly with what we need in healthcare.

A clinical AI that is “helpful” means it surfaces the right information at the right time. “Harmless” means it never presents uncertain conclusions as definitive. “Honest” means it tells the doctor when it does not know something. These are not abstract principles in a clinic — they are the difference between a tool doctors trust and one they learn to ignore.

Our collaboration with Anthropic focuses on developing clinical guardrails — the rules and boundaries that prevent a general-purpose language model from behaving like a medical authority it is not. When HealNote's AI says “this pattern is consistent with...” instead of “the patient has...”, that careful language is not an accident. It is engineered.

What Google brings

Google's healthcare AI division has spent years building medical knowledge graphs, training models on clinical literature, and developing multimodal systems that can process images, text, and structured data simultaneously. Their Med-PaLM research demonstrated that large language models can, with careful tuning, achieve expert-level performance on medical question answering.

For HealNote, Google's contribution is primarily in infrastructure and multimodal capability. Our diagnostic assistance tools benefit from models that can reason across different types of medical data — lab results alongside symptom descriptions alongside imaging reports. The ability to process a patient's complete clinical picture, not just one slice of it, is what makes the AI genuinely useful rather than narrowly clever.

What this means on Monday morning

For the doctor opening HealNote on a Monday morning, the partnership manifests in ways that are deliberately invisible:

• Clinical briefs that are more accurate and more nuanced than what a single model could produce
• Faster inference times because of optimised infrastructure
• Stronger safety guardrails that prevent the AI from overstepping
• Multilingual support that works for Arabic-speaking patients and English-speaking specialists in the same clinic
• Continuous improvement as both partners advance their underlying technology

The doctor does not see “Powered by Anthropic” or “Built on Google.” They see a system that understands clinical context better than anything they have used before. That is the point.

The bigger picture

Healthcare AI is too important to be built in isolation. No single company — no matter how talented their team — has all the expertise needed to build clinical AI that is safe, effective, and trustworthy. The models need the knowledge of AI researchers. The integration needs the understanding of clinicians. The safety systems need the rigour of compliance experts.

Our partnerships with Anthropic and Google are not competitive advantages to put on a slide deck. They are acknowledgments that this work is too consequential to do alone.

We will continue sharing how these partnerships evolve and what they produce. If you are a healthcare organisation interested in what partnership-grade clinical AI looks like in practice, we invite you to start a conversation.