Clinical Cofounder

Like the technical cofounder, the clinical cofounder wants to create a product or service and provide value.

Medical training however can be as much as a hindrance as it helps.

In medical school, and subsequent residency, we are drilled on careful attention to detail for each patient. Unless you are in a leadership role, public health or preventive medicine, you rarely think of treating a group of people.


This is the opposite of scalable and this is the opposite of value added to a growing company, mostly.

By using skills learned while caring for one patient at a time, the forward thinking clinicians can lift his or her head above the noise to see trends. We can identify pain points in our workflow and recognize them in our colleagues. We can make changes in workflow and assess efficiency. We can measure diagnosis per patients per day.

These front line observations are critical for a founding team to create the first version of a product that clinicians will find valuable.

The medical tools created and given to us can be flexed, and broken if needed. Electronic health records can be compared to banking, travel or social user experiences. Why can Expedia predict where I am going but the radiology system cannot? This juxtaposition is how a clinician can help the user experience developer tailor an intuitive interface.

Physicians are great at continuing education but the majority are not great at, or care to, broaden their knowledge base. We live in a time where on-demand classes exist to learn about machine learning and cognitive neural networks. Not that we would perform these function but in the context of the founding team we can converse efficiently with the technical team to best create a system.

We can provide insightful questions for the machine learning engineer to answer; provide supervised learning guidance throughout the process as well.

If my family member needs care today, the physician who couldn’t care less about all of this is the right person for us.

When we need care in 5 years, I am betting on significant improvement in healthcare delivery as a result of the clinical cofounder.


Leigh and I wrote this book to help radiologists.

With all of the news of artificial intelligence and machine learning it can be daunting to find a place to start.

You will need no computer background to read this book.

Program directors or professors may use this a tool to introduce AI and ML to trainees.

The book will present the difference between artificial intelligence, machine learning and neural networks. You will learn that a neural network is similar to human brains and ‘layers’ are similar to synapses.

Just like the first few years of medical school presented new vocabulary, ML and AI have some particular words that are described simply.

There are some similarities between residency training and ‘training an algorithm’ which will be explained.

After reading this book, you will be prepared to read radiology journal articles that showcase AI and ML applications.

Ty Vachon, MD ML Machine Learning Artificial Intelligence AI Radiology

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