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279 sats \ 4 replies \ @arcanox 10 Jan 2023 \ on: Jeff Booth bitcoin
As a nuclear radiologist resident who just read your book, what can I do to be better prepared for the jobs of tommorrow? I became very anxious after reading your book, lots of doctor jobs are going to get replaced by AI. Should I start learning CS skills or continue focusing on my career? Lots of talk about AI in medical imaging, but I am not sure if it is viable to get into this niche. From Europe.
Also, big fan of your book. Despite my anxiety, it made me feel optimistic.
Our short term nature of spending our time trying to make enough money to escape the system IS thing giving anxiety. From a different system, the productivity of technology flows to society in the form of lower prices and frees our time.
This is a HUGE transition......that creates disruption and pain....which entrepreneurs solve. Learn about where the opportunities lie in the rebuilding of value in the new system. The companies that Ego Death has funded (and other VCs in the Bitcoin space) will be looking for talent.
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My 2 cents - and I may not be as knowledgeable as Jeff Booth, but I have worked in neuroimaging and now work with AI.
AI has been hyped as replacing radiologists for decades now, but so far it hasn't materialized. More than likely, they'll be a transition period where your expertise is leveraged in conjunction with an AI. I think it's unlikely that you will be replaced completely in the next decade.
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Glad you responded. I am well aware of the AI scare in radiology, and I used to think AI would make our jobs more complex and lesion reporting more accurate. Less time wasted on analysis of chest X-rays and more time analysing brain MRI scans, you know?
Do you recommend picking up any additional computer skills?
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Not necessarily, as I'm assuming you want to practice medicine in your career, not become an AI engineer. Anyone of the free machine learning/AI courses couldn't hurt just to remove some of the mystery around how things are developed. This is one example, but there are a ton of these: https://www.coursera.org/learn/practical-machine-learning
If you're trying to better understand AI and it's application to radiology, trying to understand the math and rationale behind the models is probably a better place to start. That way as it becomes a part of your field, you'll have a command of the concepts, and will also be able to spot the pitfalls (still lots of hype and overselling). It'll also smooth the transition if/when it becomes part of your work.
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