If AI helps make an important decision about your health, you should not have to simply trust that it worked.
AI is starting to help with decisions about people's health, in hospitals, and in pro sports, where teams use data about players' bodies to decide who plays and when. Darwin by Medigram is the system that checks the AI: it scores every decision on six kinds of trustworthiness and keeps a permanent record, so whether the stakes are a patient's care or a player's career, nothing happens unchecked and there's always proof. Designed to work on mobile, where the work happens.
You should be able to expect that a qualified person remained responsible, the technology operated within defined boundaries, someone kept checking whether it worked correctly, and reliable evidence exists if questions arise later.
A plain-language reader path into Building Darwin, Medigram’s full CEO Letter.
Why should patients and families care?
AI can increasingly influence information used in healthcare decisions. Most patients should never need to understand the model, software architecture, cybersecurity controls, or technical standards behind it.
But patients and families should be able to trust the process surrounding an important decision.
What should that process protect?
A healthcare organization should be able to show what technology contributed, what it was permitted to do, who retained authority for the decision, whether important checks were operating, and what information supported the result.
If the technology does not have enough reliable information to support a recommendation, that uncertainty should remain visible rather than disappearing from the record.
Why is this so difficult for organizations?
Healthcare AI crosses medicine, engineering, cybersecurity, research, governance, operations, and evidence. These responsibilities are often divided among different people and organizations.
Each group can perform its own job correctly while important risks remain in the spaces between them. Someone still has to make sure those seams are closed from beginning to end.
Closing the seams, not adding another handoff
Healthcare AI touches medicine, engineering, cybersecurity, research, governance, and record-keeping. Each of those groups can do careful work and a patient can still be affected by a gap between them: a testing question no one owned, a record no one kept. Medigram’s approach exists to close those seams so they don’t become the patient’s problem to discover.
Why Sherri and the Medigram team
Darwin was built by working across those areas together rather than handing requirements from one group to the next. That let a finding in one place (a security question, a testing result) change decisions in the others instead of getting lost at a handoff. The goal now is to build that habit into Medigram’s product and operations, not to depend on any one person doing it by hand.
What does the technical work mean for you?
Standards, cybersecurity, engineering, testing, and governance can sound far removed from a patient. They are not. Together, they determine whether an institution can answer the questions that matter after an important decision.
What happened? Why did it happen? Who was responsible? And can you prove it?
Medigram’s Darwin is designed around preserving that evidence at the time of the decision, rather than asking patients, families, clinicians, and institutions to reconstruct it later from memory, screenshots, emails, or incomplete records.
The goal
AI should help clinicians and institutions make better decisions without making responsibility harder to find. Patients and families should receive the benefit of advanced technology while retaining something fundamental: accountable human judgment and a trustworthy record of consequential decisions.
Want the technical detail?
The full CEO Letter explains the standards, architecture, verification, security, and evidence mechanisms behind this approach.