AI: The Good, The Bad, and The Baffling
From superbug sleuths to postcode-based prescriptions. This week, we’re sorting the life-savers from the nonsense.
Handoff #10 | Reading time: 5 minutes
Good morning. Today marks the day Rome was founded (legend has it), and the Loch Ness Monster first made headlines. Well, let’s just say it’s a reminder not everything spotted in the wild is real. In healthcare AI, we’re facing a bit of both, big ambition and the occasional mythical claim. This week, we’re cutting through the fog to bring you what’s real, what’s working, and what still belongs in the realm of legends.
In today's handoff:
Turning Dusty MRIs Into MS Breakthroughs
When AI Listens to Nurses, Lives Are Saved
AI vs. Superbugs, Bias Bloopers, and Smarter Screening for Women’s Health
Starfish Wearables, Brain-to-Voice Breakthroughs, and DIY AI for Kidney Care
Plus, Overfitting Explained
🩺 Quick Assessment
The one story every healthcare pro needs to know this week.
🩻 Turning Dusty MRIs Into MS Breakthroughs
Stacks of old MRI scans, gathering dust. Now imagine AI digging through them like buried treasure.
How AI Helped?
MindGlide, a deep learning tool, pulls brain region and lesion data from just one MRI contrast. No more multi-sequence faff.
The Science
Trained on 4,200+ scans from nearly 3,000 MS patients, MindGlide matched expert labels using routine-care scans.
The Outcome
It spotted treatment effects in trials and flagged lesion changes in everyday care.
Why It Matters?
MS hits over 2.8 million people. MindGlide could speed up trials and unlock new insights from hospital archives.
🚨 Critical Updates
Fresh, impactful news on AI’s real-world applications in healthcare.
🔬 AI vs. Superbugs: Predicting Resistance Before It Strikes
At Chalmers University, researchers have built an AI model that predicts if bacteria will turn antibiotic-resistant. Trained on mountains of genetic data, this AI picks up early warning signs of resistance, flagging trouble before it spreads.
So What? Superbugs are a nightmare for hospitals. This AI could help clinicians outsmart resistance patterns, leading to better antibiotics use and fewer treatment flops.
⚖️ AI Bias Exposed: When Algorithms Play Favourites
Mount Sinai’s stress test of AI models found something alarming: the tech sometimes tailors care based on a patient’s income or background. Higher-income patients got shiny diagnostics like CT scans, while others were nudged away from further tests. Not great.
So What? Mount Sinai’s findings are a wake-up call for everyone using AI tools: check the bias, check it twice, and push for validation before trusting the recs.
🩺 Protect Her: Smarter Screening for Women’s Health
Covera Health’s new platform, Protect Her, scans routine imaging like mammograms and chest X-rays to catch early signs of breast cancer, heart disease, and osteoporosis. No extra scans required.
So What? This could quietly transform women’s health by spotting problems while they’re still treatable. It slips straight into existing workflows, making it an easy win for busy clinics.
📋 Follow-Up Notes
Demystifying tricky AI concepts with simple, relatable explanations.
💡 Overfitting
The Breakdown
Overfitting happens when an AI model learns the training data a bit too well, down to every tiny detail and quirk. It becomes so obsessed with its practice set that it struggles to generalise to new, unseen data. Great at the test, falls flat in real life.
The Analogy
Think of it like a student who memorises every answer in the textbook but panics when the exam throws in a question with a slight twist. Brilliant at reciting facts, but absolutely lost when asked to apply them. Basically, AI’s version of stage fright.
Why It Matters
Spotting and avoiding overfitting keeps AI reliable where it matters most, at the bedside, not just in the sandbox.
🔍 Incidental Findings
The AI twist you didn’t see coming.
📢 When AI Listens to Nurses, Lives Are Saved
The Discovery
Meet CONCERN, the AI early warning system from Columbia University that reads nursing notes like a thriller novel. By analysing daily documentation, it predicts patient deterioration up to 42 hours before traditional methods. Yes, you read that right.
Why It’s Wild?
Forget sensors and flashy dashboards, this AI taps into the subtle, often unspoken clues nurses jot down during routine care. Think of it as turning quiet observations into loud, actionable alerts for the whole team.
The Takeaway
In trials with over 60,000 patients, CONCERN reduced deaths by 35% and cut hospital stays by half a day. For healthcare, it’s proof that AI doesn’t replace clinical instincts, it amplifies them.
📝 Rounds Recap
A quick roundup of key headlines you might’ve missed but should know.
A Surprising Duo: AI is turning mammograms into a two-for-one deal, spotting not just breast cancer but also signs of heart disease by detecting arterial calcifications.
Starfish-Inspired Tech: Inspired by our five-armed sea pals, this starfish-shaped wearable delivers accurate heart monitoring even on the move, with AI scrubbing out noisy signals.
Giving a Voice to the Voiceless: Researchers have built an AI neuroprosthesis that decodes brain signals into real-time speech, giving people with severe paralysis their voice back.
Paradigm Shift: Proprio’s Paradigm just landed FDA clearance, bringing real-time 3D visualisation to spinal surgery.
DIY AI That Delivers: Strive Health built their own ML platform to tackle kidney care, slashing readmissions by 36% and hospitalisations by nearly half.
Eyes on Every Polyp: Medtronic’s GI Genius is helping gastroenterologists catch precancerous lesions during colonoscopies, boosting polyp detection rates, even the tricky flat ones. Early days, but it’s already shaping up to be a lifesaver (pending version 4.0, of course).
Prescription Power-Up: Suki Assistant now stages prescription orders straight from spoken notes, slashing admin time by up to 72%. Clinicians can just speak and go, while Suki handles the coding and clicks.
AI and Alzheimer’s: The ACR and Icometrix have teamed up with Icobrain Aria, an AI tool that automates monitoring of brain abnormalities in Alzheimer’s care. With FDA clearance and training programs underway.
Stanford’s MedHELM puts AI models through real-world clinical tests, not just academic drills. By testing across 120 scenarios, it helps ensure AI tools are street-smart and safe for actual hospital use.
In Case You Missed It!
If you’re just joining or didn’t get to it, here’s what dropped in AI Handoff Insider last week:
📌 Why More Clinicians Are Ditching the Rulebook and Letting AI Take the Lead
Turns out, clinical guidelines aren’t keeping up with the patients in front of us. We dug into how some clinicians are quietly swapping the PDFs for AI tools that actually move at the pace of modern care. Read it here
📌 Don’t Sleep on DeepSeek! Why This Open-Source Lab Might Be Healthcare’s Dark Horse
While the big AI labs are still teasing open models, China’s already using one in real hospitals. DeepSeek’s quietly making moves, writing notes, reading slides, and maybe giving the big players a run for their money. Read it here
🤝 Final Handoff
Funny old thing, this AI business. One moment it’s picking up lifesaving clues from everyday nursing notes, the next it’s confidently making up treatment plans based on your postcode. Brilliant and bonkers, all in the same breath. But hey, at least we’re getting better at spotting the difference.
Thanks for making it to the end of another lap. No AI hallucinations here, just the good stuff.
See you next Monday.