Industry Workshops
AI Imaging Masterclass For Medtech and Lifescience Companies
Develop future-forward skills in healthcare.
Engage in a transformative, hands-on workshop where your team will master practical AI applications using Roboflow and medical imaging.
Whether you’re new to AI or seeking to enhance your expertise, this masterclass enables your team to seamlessly integrate AI into R&D projects, optimize clinical workflows, and drive business innovation.
AI-Native Workforce
Companies retain talent and build AI expertise internally, without costly external hiring or consultants.
Hands-On Training
Pedagogy focuses on collaboration and project-based learning to drive skills retention.
Transferrable Skills
Transfer AI skillsets gained through Roboflow towards R&D and clinical workflow enhancements, and accelerating business roadmaps.
A high impact experience - fully sponsored for select participants.
This masterclass is valued at $2,500 per participant, based on the real-world tools, frameworks, and outcomes delivered in a single day.
Thanks to state innovation funding, Massachusetts-based particpants can access fully sponsored seats at no cost.
Companies may enroll up to 6 participants for the full-day workshop or 12 participants for a half-day format.
Seats are limited and filled on a rolling basis. We prioritize medtech and life science participants actively prioritizing computer vision / AI into their business roadmaps.
Course Format:
9AM
Introduction and Problem Statement
10AM
AI Platform Setup
11AM
Data Collection and Annotation
1PM
AI Model Training & Testing
2PM
AI Challenge Prep
3-5:30PM
AI Clinical Challenges & Demos
FAQ:
Q: Is there anything I have to prepare for before the workshop?
A: There are no prerequisites or preparation required.
Q: Who is this workshop designed for?
A: This workshop is open to anyone - from business leadership to engineering, marketing, BD, quality, regulatory, etc.
Q: What are AI Clinical Challenges?
A: AI clinical challenges will pressure test participant- built AI models against complex clinical scenarios to ensure they are robust and can account for edge cases, including obscured anatomy, lighting effects, complex pathology, etc.
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