This whitepaper is about trust, real workflows, and what actually gets AI tools adopted in healthcare.

Download the whitepaper here

Four in five physicians use AI in their practice. Only 16% use it to support clinical decisions. The distance between those two numbers is what this whitepaper is about. It comes from conversations with frontline clinicians across specialties: what they use, what they don't, and why.

What’s inside 

• Trust has to be earned. The FDA stamp is a requirement for buyers, but for clinicians, it falls short. They demand proof that the system works for their patients, and proof has to be designed into the system from day one: physicians need to be able to trace the origin of an AI decision and be certain that the system will flag when it’s unsure. That level of trust only happens when it is built by design, following an AI governance and trust framework.

  
  

• The workflow already exists, get into it. Clinicians don’t need a new way to work. Tools that ask them to learn a new task from scratch rarely get used. That's why the AI tools that do get adopted are the ones that embed into something clinicians are already doing. To achieve adoption, you have to map the bottlenecks of the real workflow and listen to what clinicians actually need.

  
  

• There’s no generic user in healthcare. Physicians across different disciplines have different workflows, different evidence requirements, and different interfaces they can trust. Designing AI for a generic user means designing it for no one. The way around this is co-design with the real users: real clinicians shaping the tool as it’s being built, beyond interviews and personas.

  
  

Get  the whitepaper here

AI in healthcare is being built for buyers instead of for the people who use it. We build the other way around. Discover how we work.