@udomtheofficial A very timely and forward-looking initiative. 🤖📊 The future of effective development, healthcare, governance, and decision-making will increasingly depend on how well we harness AI and data to generate evidence and turn it into action.
Kudos to UDOM for embracing the future! 👏
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Yes, AI in Healthcare will lead to "de-skilling" of certain skills - but it will also unlock "re-skilling" in completely new domains. The question isn't whether that trade off is happening - it's figuring out what should go in each bucket.
When AI Scribes first came out, there was concern that not learning clinical documentation would negatively impact medical training. If the note helps clinicians learn to think through a clinical problem, organize their thoughts and present a coherent plan - what gets lost if we outsource drafting it?
At the same time, we need to recognize that humans have limited cognitive bandwidth to just keep learning more information and more skills.
Before calculators, humans had to be really good at manual arithmetic. Sure, calculators meant the average human is now worse at doing math in their heads, but look at everything that first calculators and now tools like Excel have unlocked. We developed skills to do advanced stats analysis for medical research or model out complex real-world financial scenarios in spreadsheets - wasn't that trade off worth it?
Or consider how clinicians previously had to be so much more proficient with physical exams - but with medical imaging, they unlocked this brand new skill of reading scans, which ultimately led to breakthroughs in better diagnosis and treatment. Wasn't that skill trade off worth it?
Here's the crazy thing about AI - we're now seeing AI agents that will do the heavy lifting on financial models for you. In the past, you at least needed foundational stats and finance skills to build those spreadsheet models. Now, you can just tell a Claude agent to build the model for you.
With AI clinical co-pilots, you can tell the AI to not only draft the note - but automatically suggest a diagnosis, treatment plan and orders. The question is: how much of this skill stack do we want to outsource to AI and how much do we care to keep?
What we're seeing across industries is that the experienced professional - with decades in medicine, finance, or law pre-AI - has the foundational skillset, judgment and experience to 10x their output with AI. They know what good and bad outputs look like. They can pressure test the AI.
When we think about the next generation of clinicians, it's important that medical trainees still develop the most important clinical skills - they shouldn't outsource all clinical thinking to AI before they develop judgment, taste, etc. But we can't protect that for every skill.
In some ways, it's good that AI adoption in clinical care has real friction. We need early adopters at all stages of training to tinker with AI, figure out where it fits and where it detracts value, and let the profession work it out - as it does with any new technology.
Ultimately we shouldn't just be worried about AI de-skilling - we should be equally optimistic about what new skills AI will unlock, and be thoughtful about exploring those edges to maximize AI for patient care.