This document provides advice on how to build a successful radiology AI company. It begins by outlining some "don'ts", such as having no clear focus, making bizarre claims, being unnecessarily secretive, and lacking engagement. It then details several "dos", including identifying a clear problem before building a solution, being laser focused on goals, carefully planning the team needed, building financial runway, forming strategic partnerships, designing a robust data pipeline, engaging with regulators early, producing evidence of effectiveness, integrating systems respectfully, and engendering trust with customers. The overall message is that radiology AI requires a thoughtful, evidence-based approach focused on solving real clinical needs.

1. How to Build a
Radiology AI Company
DR HUGH HARVEY - MANAGING DIRECTOR - HARDIAN HEALTH

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How to build a radiology AI company

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How NOT to build a radiology AI company
No apparent focus
Bizarre claims
Unnecessary secrecy
Lack of engagement

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How NOT to build a radiology AI company

9. Identify a problem
●DON’T build a solution based on the data you
happen to have, hoping to find a problem/fit
●DO find (and verify) a problem first, and then build
the solution
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Silicon Valley vs Health tech
Consumer AI product
Ship Minimally Viable Product
Monitor user churn
Frantically fix bugs
Learn from bugs/churn
Write better code
Find more users
AI health product
Find potential end users
Ask detailed questions
Define problem
Build product
Perform studies + regulation
Ship product

11. Be laser focussed
●DON’T expect to be able to pivot easily in health tech
●DO set a vision (the future) and a mission (how to get
there)
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13. Build a team
●DON’T hire the first person who likes your idea, or
the cheapest, or the richest.
●DO have an organised plan for the skills your team
needs, and how the team will expand over time
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14. Critical early roles
CEO Professional Technical Administrative
Delivery Regulatory Engineering Product
Development Clinical Data Science Operations
Support Finance Dev Ops Marketing
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15. Build a financial runway
●DON’T automatically look for investor money. You
don’t need necessarily need it.
●DO create a business plan, and seek appropriate
funding from the right sources for the stage and time
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19. Form external partnerships
●DON’T think you can do it alone. You can’t.
●DO strategically plan targeted partnerships that add
value e.g. data, influence, services, funding
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20. Start-up
Key opinion
leaders
Press
Target
audience
Experts and
advisors
Clinical
organisations
Government
bodies

21. Design a data pipeline
●DON’T solely use public datasets, or rely on data
from one institution
●DO plan, design and forecast what data you need,
where from, how much, what quality, and which
labels
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22. MIDaR Scale
Level D
1. Inaccessible
2. Unknown format
3. Unverified existence
4. Un-anonymised
Level C
1. Anonymised
2. Ethical clearance
3. Unstructured
4. Noise / gaps
Level B
1. True representation
2. Structured
3. Visualisable
Level A
1. Contextual
2. Annotated
3. Task ready
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Preparing data for AI - MIDaR scale

23. Engage early with regulation
●DON’T underestimate the FDA process (or lie to
them)
●DO define your intended use, check the class of your
device, contact the FDA and start a QMS
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26. Produce evidence of effectiveness
●DON’T make claims you can’t substantiate
●DO engage with the clinical research process, and
publish your results transparently
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28. In review: Proposed Evaluation and Approval Process to Ensure the Safe and
Effective Deployment of Diagnostic Algorithms in Medical Imaging - Langlotz,
Larson, Harvey
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29. Integrate
●DON’T try to replace existing hospital IT
infrastructure
●DO be interoperable, flexible, dynamic, and respect
existing practice
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31. Engender trust
●DON’T over-promise and under-deliver
●DO listen to customers to ensure your company’s
character and competence are aligned with their
expectations
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33. HARDIAN HEALTH
CLINICAL | DIGITAL | CONSULTING
Thanks!
@drhughharvey