Everyone is talking about
#ChatGPT
and how it could save healthcare.
But is it even possible to use it in
#medicine
, and what would the regulatory compliance journey look like? We've covered it all in our latest blog.
Good work from
@StanfordHAI
showing that GPT 4 is not robust enough for use as a medical co-pilot for the following important reasons:
1) Non-deterministic: They found low similarity and high variability in responses to the same question. Jaccard and cosine similarity
I spent a lot of time debating whether to post this or not, but, do you know what, I'm actually really proud of what I've achieved.
Today marks exactly one year since I started focussing on my health.
So far, I've lost 15kg. Never felt better.
Babylon Health collapsed last night.
I used to work there - it was my first industry job in healthtech 7 years ago. I left after 12 months, frustrated, disillusioned and wanting to do healthtech properly.
What went wrong?...
Congrats to Google, but let’s not forgot the team from NYU who last year published better results, validated on more cases, tested on more readers, and made their code and data available.
They just don’t have the PR machine to raise awareness.
Great news! Our paper "Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening" has been published at
@IEEE_TMI
!
This marks the end of a 3-year long endeavor, at
@NYUDataScience
and
@cai2r
, to demonstrate the potential of CNNs on this task. (1/8)
If you are building ‘ChatGPT’ for medicine, you are likely building a medical device.
That means you need regulatory approval.
And that means you need to qualify any third party providers (e.g OpenAI) to ensure they comply.
Which they don’t.
So you can’t.
Twitter science. RT for exposure.
Hypothesis: No two radiologists will create the same report.
Method: Radiologists, please look at this CXR and type a report as a reply WITHOUT looking to see what others have said.
Hx: 54 yrs Male, non-smoker, 2 weeks SOB, outpatient
I'm getting a lot of journalists asking my opinion on the Amazon/NHS news - asking me will AI replace doctors?
This is not AI! It's a search of approved NHS online information triggered by speech recognition. It's exactly the same as going to and typing.
Me to VC: The ONLY route to market for your healthtech portfolio is regulatory approval & clinical evidence. I can help with both
VC to me: Sounds slow and boring. My investments are disrupters on a fast track. Thanks anyway
6 MONTHS LATER
VC to me: Hey can I pick your brains
My father is a portrait painter, and he painted
@captaintommoore
for his 100th birthday.
If anyone knows how we can donate it to his family or charity so they may auction it, please let me know. It's 30x24 inches, unframed.
AI works best in ‘perfect information’ scenarios (e.g. chess).
Unfortunately, in medicine there are no perfect information scenarios. It’s always incomplete.
Which is why there will always be a need for human oversight given current AI capabilities.
Flight attendant: Is there a doctor on board?
Dad: *nudge* You’re a doctor!
Me: Yes, but I’m a radiologist.
Dad: You went to med school! Go save someone!
Me: Do they have a CT scanner on board the plane by any chance?
Heard from a US radiologist today - they have AI installed at their hospital, but not at their home workstation.
They far prefer reporting from the hospital now - ‘It’s just so much more efficient. I can’t stand working without it.’
AI is inevitable now.
LLMs (eg ChatGPT) intended for medical use will be regulated as medical devices, requiring clinical evidence, quality management and compliance with ISO standards.
From
@MHRAgovuk
To everyone making 3D printed ventilator parts in the UK - there is good news from the regulators!
You can apply for approval to supply a non-compliant medical device on humanitarian grounds during the
#COVID19
pandemic. Decision made in 48 hours.
Advising three startups in health tech.
I urged them all to consider a regulatory/compliance hire as early as possible.
2 didn't. 1 did.
Guess which one now has market traction and revenue?
The day I started I was on the 'clinical AI' team and was shown the 'ground-breaking AI system'. Turns out it was just a bunch of Excel spreadsheets with some decision trees written by junior doctors. It was a disappointing start.
Comparison of the
@GoogleHealth
breast AI paper against the
@radiology_rsna
Editorial Board recommendations for Assessing Radiology Research on Artificial Intelligence
Some say data is the new oil. Others say the algorithm is the gold.
Actually, it’s the labels.
Quality labels are hard to come by, expensive to generate, and without them, safe, accurate algorithmic performance isn’t possible.
Radiation-damaged hand of a radiographer (x-ray tech) back when daily set up involved x-raying yourself to check the machine. We don’t do this anymore, for obvious reasons.
If you ever need a reminder of how far medical radiation safety has come, look no further than the Pedoscope - originally designed to allow shoe shops to X-ray customers’ feet inside shoes to see if they fit! This old model is sitting in the staff room at Guy’s hospital.
Irony isn’t dead.
@babylonhealth
publicly challenges
@DrMurphy11
to “publish the entirety of your work, and let the totality of your data be assessed by any objective expert” despite having no objectively assessed published data themselves...
Newly qualified lawyers to get higher salary than full time NHS consultants after 19 years of service and a level 3 clinical excellence award.
Something is broken.
Astounding paper in JMIR. Despite making claims on diagnosis, treatment and prevention (making them medical devices):
- 44% of digital health start-ups have zero published research or regulatory approvals
Babylon always claimed that their AI would eventually start to help reduce the costs of delivering remote GP consultations. This was the central fallacy.
Babylon is the most expensive failed experiment on digitising primary care to date.
Unsure about the evidence behind ‘personalised‘ medicine. Start pulling back the veil of hype, you find out it’s not all it seems...
Lecture by
@mendel_random
And a brilliant read in Nature from
@stephensenn
Database of confirmed
#COVID19
CXR and CT cases from Italy.
Useful for radiologists to review features, or for any medical professional at the frontline.
Implementing AI solutions in healthcare requires IT interoperability, but actually more importantly is the rearranging of entire care pathways and breaking down interdepartmental silos. Tough problems to solve, and not solved by tech alone.
This is a genuine radiology report that I was sent. Not by a colleague. Not even from this country.
I have to admit that, deep down inside, I’m jealous. I don’t have the cojones
My piece in
@TheLancet
with
@EricTopol
on AI for improving image acquisition.
AI has the potential to be of use in predicting the appearance of high-resolution scanning from a lower resolution image. However, some argue this is a dangerous task.
This is how you do AI research, without the hype...
Independent evaluators: ✅
Comparison of different models: ✅
Multi-site data at different disease prevalence: ✅
STARD criteria: ✅
Blinding: ✅
Next step: Prospective testing
Only 6% of radiology AI has been prospectively validated on external data. Not surprising - prospective studies take time (at least 3 yrs in the case of breast screening in the UK). Critics take this as evidence AI doesn't work - be patient, it's coming!
Sad day. My grandfather died aged 95 alone in his care home.
It was his time, and he was peaceful, but I regret not being able to see him these past months. The cruelty of the situation is hard.
RIP. Last of a family generation
At the time MRI was invented, there were fears that the technique would obviate the need for radiologists as the images were so clear that people thought anyone could interpret them.
Today MRI is now the largest time sink for radiologists, and still no-one else is reading them.
Dear health tech investors,
Growth and massive valuations are cool and everything, but do you know what’s cooler?
Clinical evidence and strong health economics analyses.
Regards,
Hugh
My morning routine:
10am: Get up when I want except on Weds when I get rudely awakened by the dustmen
11am: Put my trousers on have a cup of tea and I think about leaving the house
3pm: Feed the pigeons, sometimes feed the sparrows too
It gives me a sense of enormous well-being
Lots of people ask me about my morning routine, as I’m up so early, here it is:
0445: Alarm goes off
0500: 10k run
0600: Shower/breakfast
0630: Feed the birds
0635: Leave for work
0715: Blood results/hospital letters/referrals/Med education
0800: Start clinic
A man of routine!
I remember I once gave an internal presentation explaining the importance of regulations, and even had a slide saying that Babylon did not want to be the next Theranos.
That turned out to be fairly prescient...
So what happens next? Not much - Babylon's been broken up and sold for parts. GP at Hand still exists in some form - but for how long?
For now, it joins a list of infamous healthtech companies including Theranos and Pear Therapeutics.
So,
@OpenAI
have released a 175 billion parameter meta-learning natural language AI called GPT-3 that can essentially answer any question you can throw at it, even on topics it’s not trained on.
It’s passing exams, writing poetry and creating code.
1/6
My AI Predictions for
#RSNA19
...
1) Wired: AI to structure data at source, integration to improve workflow, and automation of operational tasks.
2) Tired: Niche image/pattern recognition tasks.
3) Needed: Multi-site prospective validation results to sort wheat from chaff.
I have a keyboard in my office that you can see behind me when I do video conferencing. I get asked a lot if I play it, and what the sheet music is. So here you go.
Lots of radiologists ask me how to get into AI.
There’s enough data scientists already. Instead, focus on these important skills:
Medical informatics
Information theory
Product life-cycle
Quality management
Clinical validation
Medical device regulation
Business development
This paper is making the rounds, so I thought I'd do a thread.
The headline is "AI can't pass the radiology board exam" so let's explore what that actually means, and if it's even the right benchmark to test.
Key takeaways from
#RSNA2023
- Very few new start up entrants to the AI space.
- AI vendors have unilaterally downsized their booths - indication of funding?
- A few new point solutions (CDS or diagnostic) from established vendors but no game changers.
- Talk is all about
@DrMurphy11
was the only person brave enough to talk publicly about Babylon's deception and AI hype at the time.
But there was one thing that did work - and that was the telemedicine part... sort of...
The GP at Hand service actually did let people see a doctor conveniently. The price to pay was the destruction of the NHS services it was replacing. The service as a whole was apparently not cost effective - but patients did like it, and they did use it.
Prospective studies of AI will increasingly demonstrate how hard it is to get this technology to work in the real-world. False positives will be a real issue as demonstrated in this paper.
The most bizarre paper I have ever read!
300 women asked detailed sexual history, undergo physical exam, subjectively rated on their looks, and then have surgery to see if women with endometriosis are more attractive than those without.
Just like AI, Prof Hinton is incredibly good at what he does, but fails dramatically when outside of his domain of expertise.
Poorly poised question. Medicine is not binary. AI is not robotics. No such thing as per surgeon cure rate.
Suppose you have cancer and you have to choose between a black box AI surgeon that cannot explain how it works but has a 90% cure rate and a human surgeon with an 80% cure rate. Do you want the AI surgeon to be illegal?
A first for radiology AI -
@WHO
recommends that AI software may be used in place of human readers for screening and triage of TB on adult CXR based on evidence from
@qure_ai
@Lunit_AI
and
@CAD4TB
6 years after I left, the company IPO'd on the NYSE at over $4billion in 'value'.
Today it's market cap is something like $5000.
My only regret is not holding my short position for longer.
Thrilled to announce I’m joining
@EricTopol
et al. as an associate editor of Nature partner journal Digital Medicine! Open-access, peer-review on the cutting edge of healthtech.
@Nature_NPJ
The company did everything to show it was 'safe' - but it wasn't accurate. You can send every query to emergency - that's safe, but entirely wrong.
A demo hastily set up for some BBC filming was jury-rigged to look more accurate than it was.
Making a map of the AI radiology start-ups in the EU - have I missed any?
Join me and
@eranrad
for a
@EuSoMII
webinar on the state of radiology AI in the EU this Monday evening
No-one is selling a continuously learning AI system yet for medical imaging (there are no regulations specifically for such systems, let alone legislation/policies for continuously sharing data), but the theory just became a little more real.
A tiny hospital at the base of the Himalayas has been using AI on the Assamese indigenous population who are at high risk for stroke.
Oh, and no massive digital transformation required...
Well done
@qure_ai
It didn't work. Well, the NLP did to an extent - if someone wanted to ask about a nasal problem, it could recognise they were talking about their nose, but that was about it.
The graph model became a tangled mess of data linkages, overly complex and practically dangerous.
Open source, cloud, deep convolutional neural networks for chest X-ray triage.
Trained in 6 hours on publicly available data.
99.8% positive predictive value.
Ok, this is cool. A portable camera that takes CXRs. From a South Korean company called MiNe. Not much info, they don’t even have a booth. Integrates with AI for automated reads. Perfect for 3rd world healthcare.
#RSNA19
I can highly recommend anyone involved in
#AI
or digital medicine to watch this superb Netflix documentary on medical device reguations: “The Bleeding Edge” on Netflix
It takes about 18 years to train the most advanced neural network in the universe (the human brain) to function at the level of a human adult.
People who say AI will replace us in the next decade are being very optimistic!
The Apple and Android app stores need a separate category for regulatory approved medical apps, clearly differentiated from the 'well-being' and 'tracking' ones.
Literally all radiologists do:
No.
No.
That's not clinically indicated.
No.
The word 'Pain' isn't a clinical history.
No.
There's no such thing as a quick look.
Standing behind me wont make me faster.
No.
Nuclear is a different department.
No.
Yes, I've seen double IVCs before.
Literally all I do as a statistician:
No.
No.
That's not the definition of a p-value.
No.
Trending towards significance is not a thing.
No.
No pie charts!
That only works if data is normal.
No.
That's logistic regression not AI.
No.
Your "novel" method was invented in 1918.
No.
A few months later, the RCP distanced themselves from Babylon and all online references to the event were taken down.
Matt Hancock got involved somehow - we know how that turned out..
Has anyone yet figured out exactly why VCs are so attracted to generative AI that that can produce infinite amounts of bullshit?
Asking for a friend...
As clinicians we were set the task of linking the probabilities of various diseases and symptoms, however random. A Herculean task that quickly became a joke - what is the probability of someone with RUQ pain having rhinitis? You can't answer this question with any accuracy.
1/6 Everyone reading and talking about Google's "breakthrough" lung nodule detection paper in
@NatureMedicine
should also take at look at these, some of which report higher accuracy metrics, on a range of similar tasks.
💥
@KheironMedical
featured in The Times today! Announcing that we have received the UK’s first CE class IIa approval for deep learning tech in radiology, and revealing our clinical partnership with
@emradNHS
. More details coming soon.
More and more hyped up demos came out - AI facial recognition, voice-to-text - anything to jump on the latest AI crazes. Almost all of it was vapourware.
The company raised more and more money, expanding and hiring as if money was unlimited.
The chatbot didn't work. The regulators came knocking and my job became to deal with them. I read the medical device regulations cover to cover, and worked my ass off to get a Class I self certification (allowed at the time). Babylon wanted FDA approval but of course never got it