Saving Lives with Deep Learning & Robust Infrastructure – with Idan Bassuk, VP A.I., Aidoc

In this episode of Unboxing AI, I host Idan Bassuk, the VP A.I. at Aidoc, to chat about computer vision, NLP, and AI explainability in the medical field. During the episode, Idan discusses the challenges that should interest anyone who’s building an AI team for scale. For example: what type of roles does he have on his team? Is it engineering first or data first? Does the real world of production resemble the rich academic research in the medical space?


1:52 – Going from terror tunnel detection to tumor detection

4:00 – Aidoc’s medical imaging AI product in a nutshell

9:09 – Main challenges for Aidoc

10:50 – Data variability in the medical field

14:18 – Explainability in medical AI

17:02 – Incorporating SoTA

19:15 – The state of academia

21:30 – Data-centric AI

23:49 – AI org structure at Aidoc

27:08 – Building test sets

29:19 – Using NLP to accelerate algorithm development

31:18 – Finding the right data

35:53 – How choosing the right annotation method affects accuracy

40:43 – Bringing it to production – team coordination

45:11 – The importance of clean code and code review

51:43 – Hiring with “extreme transparency”

54:46 – On the role of AI software engineers

57:08 – CI/CD and reproducibility

58:32 – Working agile in AI

1:01:38 – Planning with uncertainty



Idan Bassuk – LinkedIn:


Stanford cs231n course, mentioned @56:46:

Course Lectures:

Course Homepage:

I’m Gil Elbaz, Co-founder and CTO of Datagen. In this podcast, I speak with interesting computer vision thinkers and practitioners. I ask the big questions that touch on the issues and challenges that ML and CV engineers deal with every day. On the way, I hope you uncover a new subject or gain a different perspective, as well as enjoying engaging conversation. It’s about much more than the technical processes – it’s about people, journeys, and ideas. Turn up the volume, insights inside., the hub of the AI startup ecosystem.

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