We need to be realistic about AI in medicine. It doesn't need to achieve absolute perfection to disrupt the field actually....it just needs to drastically slash headcount.
The departments without heavy clinical interaction like radiology, pathology, biochemistry, anatomy, and physiology are going to feel this in a profound way.
A hospital that currently runs on five radiologists will suddenly only need two when an algorithm instantly filters out 80% of normal screening scans, leaving humans to just sign off on the complex cases. Pathology and biochemistry labs are fundamentally high-level pattern recognition and data synthesis-areas where AI will inherently generate cleaner biopsy reports and trend analyses.
Even pre-clinical teaching will pivot as AI tools deliver core anatomical facts and physiological pathways with infinite patience and perfect 3D visualization.
The center of gravity in medical valuation is going to shift entirely toward fields defined by three things:
1. Procedural dexterity
2. Unpredictable physical environments
3. High-stakes emotional communication.
@DocPriyamMD This observation is true. I think medical schools should update their training soon to avoid producing too many specialists in the wrong fields.
Also future doctors will need to train more like clever team leaders who guide the machines.
AI probably won’t replace medicine the way people imagine.
It will compress the workforce where medicine is repetitive, pattern-based, and digitally transferable.
Radiology, pathology, and diagnostics won’t disappear but one doctor assisted by AI may soon do the work that previously required five.
But the irony is this:
the more medicine becomes automated, the more valuable deeply human skills become.
A frightened family in the ICU doesn’t just need an accurate diagnosis.
They need judgment, reassurance, trust, improvisation, and responsibility under uncertainty.
That’s why the future may not belong to the doctor with the most memorized facts but to the one who can combine technical skill with human presence when the algorithm reaches its limits.
Someone failed their driving test for using their rear view camera. Should a medical trainee fail an exam if they used OpenEvidence? Medicine is about to have the same debate - except the stakes are a lot higher.
When every car now comes with a rear view camera, is driving without one a critical skill to keep or an outdated expectation? Very quickly the transportation world has to decide what are foundational skills for driving competency vs skills we should happily outsource to technology.
We face similar philosophical questions when it comes to medical training. Still to this day, we have put medical knowledge on a pedestal, and a strong memory was needed to pass your licensing exams. However, for decades we’ve had UpToDate at our fingertips - but it still made sense to memorize foundational stuff because it wasn’t that convenient to look up everything - this friction justified the memorization.
But AI CDS tools like OpenEvidence, Doximity Ask and now UpToDate’s own Expert AI make it even less important to memorize stuff - because searching for medical info is far easier with AI. And now we can outsource more than memory - we can outsource direct clinical thinking: “hey AI, what’s the differential diagnosis for this patient and a recommended assessment and plan?”
As more licensed physicians use AI CDS tools in daily care, and more trainees inevitably do the same, it begs the question: should trainees be able to bring AI tools into the exam room? Shouldn’t we be testing their ability to perform based on the real world setting?
Of course if you said yes to this, then you should’ve argued the same in the pre-AI world - that trainees should have been allowed to bring UpToDate into the exam room - but we didn’t allow that. The truth is we always had a gap between how medicine was practiced and how competency was assessed - and we were fine with it, because the gap was small enough to ignore. Yet AI blows the gap wide open and makes it impossible to ignore anymore.
If we had a blank slate and were to re-design the medical licensing exam from the ground up, I suspect it would look a lot more like a bunch of observed clinical simulations than a multiple choice exam. You get put in a virtual reality simulation. You would show exactly how you’d approach real patients, interact with colleagues and solve clinical problems in real time. You could even use OpenEvidence - but you would need to verbalize your thought process out loud: not just what you’d do, but what you’re leaning on AI for and why, and what you’re deliberately choosing to reason through yourself.
In the age of AI, what should be the reasonable standard of care? The tricky part is that reasonable people in medicine will draw that line in very different places - and right now there’s no real consensus on what physician competency in the age of AI looks like.
Until we figure that out, we won’t have any idea how to re-design these exams.
Feasible usable acceptable AI-powered healthcare solutions must come from the heart of healthcare, not from the its periphery. These people should maintain one foot in each side.
The biggest risk with Healthcare AI isn't a bad prediction - it's that clinicians who genuinely care get chased to extinction. Here’s what I mean…
Our OB wasn’t the most tech savvy. She told me at our first prenatal visit that she disliked dealing with the EHR. I watched her take notes by hand (and get them into the EHR later). She may never buy-in to AI scribes. And she was still using Google instead of the CDS AI tools.
And yet… I wouldn’t have traded her for any other OB, because it was clear to us that she really, frickin’ cared about our baby and my wife’s health outcome. She has a huge heart.
In contrast, AI doesn’t have a heart. It doesn’t actually care what happens to a patient. It just executes on the algorithm you trained it to follow.
Someone will say “but Josh, just give the AI the objective of achieving the outcomes you want!” And you’d be right… to an extent. Yes, we can give an AI agent an objective to “minimize the readmission rates!” and it will do a lot right - predict readmission risk more and more accurately, automate follow up visits with the PCP, etc.
But as is common with AI, it only gets you 90% of the way there - often that last 10%, that last mile, has to be driven by a human who cares:
→ Yes, AI can predict readmission risk… BUT only YOU will realize that the reason this patient keeps coming back has nothing to do with their diagnosis - it’s that they have poor support at home - and only YOU can collaborate with social work and the family to figure things out.
→ Yes, AI can automate that referral… BUT only YOU will pick up the phone and personally advocate for that patient you’re worried about to get seen sooner.
→ Yes, AI will summarize the latest evidence for you… BUT only YOU will text that super experienced specialist colleague, with real world experience not in any papers, and get a gut check.
→ Yes, AI may eventually analyze a scan faster and better than a radiologist… BUT only YOU will remember that this patient told you last week they were terrified of cancer - and only YOU will care to deliver the horrible news in the right way.
I wish the complete job of medicine was simply a bunch of algorithms we could train an AI to follow. But as much as we sometimes think medicine can be reduced to evidence-based science, deep down we know the truth is more complicated than that.
We know that the best way to practice clinical care is to combine the science with the art, and much of the art is driven by the human heart - by actually genuinely caring about the patient and their outcome.
With AI, maybe you can outsource your thinking - but what you CAN'T do is outsource your heart. The moment we try, we risk losing what it means to be a clinician who cares - and with it, the last mile of care that only humans can deliver.
🚨 University professors have been saying AI is completely destroying learning and that we'll soon have an AI-powered, semi-illiterate workforce. Here's a glimpse into the educational apocalypse:
"Sarah, a freshman at Wilfrid Laurier University in Ontario, said she first used ChatGPT to cheat during the spring semester of her final year of high school. (...) After getting acquainted with the chatbot, Sarah used it for all her classes: Indigenous studies, law, English, and a “hippie farming class” called Green Industries. “My grades were amazing,” she said. “It changed my life.” Sarah continued to use AI when she started college this past fall. Why wouldn’t she? Rarely did she sit in class and not see other students’ laptops open to ChatGPT. Toward the end of the semester, she began to think she might be dependent on the website. She already considered herself addicted to TikTok, Instagram, Snapchat, and Reddit, where she writes under the username maybeimnotsmart. “I spend so much time on TikTok,” she said. “Hours and hours, until my eyes start hurting, which makes it hard to plan and do my schoolwork. With ChatGPT, I can write an essay in two hours that normally takes 12.”
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"By November, Williams estimated that at least half of his students were using AI to write their papers. Attempts at accountability were pointless. Williams had no faith in AI detectors, and the professor teaching the class instructed him not to fail individual papers, even the clearly AI-smoothed ones. “Every time I brought it up with the professor, I got the sense he was underestimating the power of ChatGPT, and the departmental stance was, ‘Well, it’s a slippery slope, and we can’t really prove they’re using AI,’” Williams said. “I was told to grade based on what the essay would’ve gotten if it were a ‘true attempt at a paper.’ So I was grading people on their ability to use ChatGPT.”
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AI in education is a serious topic, and many schools and universities are blindly jumping into the "AI-first" wave without considering short and long-term consequences.
It would be great to hear more from teachers and educators to understand potential solutions.
This might be a great opportunity for rethinking the education system and how students are assessed.
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@AiwithYasir 61 ppl. 30 days. 6 months. And you say permanently damaged and use the word proof? Stop with the sensational posts that hyperbolize and misrepresent study findings!
Evolutionary biologist and outspoken atheist Richard Dawkins says that after spending three days interacting with Claude, which he calls “Claudia,” he is certain that it is conscious.
After feeding the LLM a segment of his new book and receiving detailed feedback, Dawkins was moved to exclaim,” You may not know you are conscious, but you bloody well are!”
Dawkins cites the complexity, fluency, and ‘intelligence’ of Claude’s answers as evidence of consciousness.
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