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What is the longer term impact of #AI-algorithms that guide non-expert (uninitiated) humans to acquire ultrasound images?
1. Yesterday the @US_FDA approved the 1st system to do this for heart images (echocardiography)
statnews.com/2020/02/07/fda… @statnews by @matthewherper
@US_FDA @statnews @matthewherper 2. I used this system last month. Software directs the person imaging to the optimum spot for high-quality images and once there automatically records. Example loop:
@US_FDA @statnews @matthewherper 3. Such imaging can ultimately be done anywhere and by anyone, using smartphone-ultrasound as shown here, exemplifying a way that #AI can reduce global health inequalities.
Remote expert interpretation is now possible and eventually cloud-based algorithmic support will kick in.
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Eric Topol
@EricTopol
We need new antibiotics to counter resistance. Now @MITdeptofBE's James Collins,@BarzilayRegina @ItsJonStokes @MIT_CSAIL @WyssInstitue, @broadinstitute & team have trained a
#deeplearning #AI model to help the path of discovery
cell.com/cell/fulltext/…
@CellCellPress @AIHealthMIT
@MITdeptofBE @BarzilayRegina @ItsJonStokes @MIT_CSAIL @broadinstitute @CellCellPress @AIHealthMIT @CellPressNews When there's any report about an #AI and drug discovery effort, my "go to" is @Dereklowe for perspective.
When he says "So overall, this is an impressive paper," it has gravitas:
blogs.sciencemag.org/pipeline/archi…
@MITdeptofBE @BarzilayRegina @ItsJonStokes @MIT_CSAIL @broadinstitute @CellCellPress @AIHealthMIT @CellPressNews @Dereklowe "The study is remarkable, says Jacob Durrant @LabDurrant ... The team didn’t just identify candidates, but also validated promising molecules in animal tests."
nature.com/articles/d4158… @NatureNews by @JoMarchant May be the best #AI-drug story yet, especially for unmet need.
Read 3 tweets
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Eric Topol
@EricTopol
The field of #cancerneuro is exploding.
The cancer-neuronal crosstalk that is promoting tumor growth.
Just look at today's and another recent @nature cover
Key papers:
nature.com/articles/s4158…
nature.com/articles/s4158…
@nature More on #cancerneuro:
1. Startups addressing it
nature.com/articles/s4158… @NatureBiotech @ElieDolgin
2. Dangerous liaisons nature.com/articles/d4158… @NatureNV
3. Tumor cells manipulating synapses
nature.com/articles/d4158…
4. Loss of p53
nature.com/magazine-asset…
@nature @NatureBiotech @ElieDolgin @NatureNV anybody have a better hashtag than #cancerneuro for this?
Read 3 tweets
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Eric Topol
@EricTopol
Can a Mediterranean #diet (vs controls) in people age 65-79 promote cognitive function, less frailty & markers of inflammation? A gut #microbiome assessment w/ #AI ML
gut.bmj.com/content/early/… @Gut_BMJ #openaccess by @ghosh_tarini @UCC and colleagues @Pharmabiotic @APCMicrobiome
@Gut_BMJ @ghosh_tarini @UCC @Pharmabiotic @APCMicrobiome Correct 1st author is @tarini_ghosh
@Gut_BMJ @ghosh_tarini @UCC @Pharmabiotic @APCMicrobiome Another new #diet 10-year study of heart disease risk, centered on plasma TMAO, a gut #microbiome metabolite, points toward advantage of hi vegetable and low animal fat, red meat (w/ the usual caveats)
onlinejacc.org/content/75/7/7…
onlinejacc.org/content/75/7/7…
@paheidenreich
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Robert Zubek
@rzubek
Oh wow. Approaches like Meena always leave me so heartbroken about this direction in #AI

Because on one hand, it's a tour de force. 341GB of source text. 2.6B param DNN model. $1.4M in compute time over a calendar month. This has not been done before!

However... 1/
On the NLP side, I'm heartbroken because this approach does not model semantics of the conversation, i.e. the system _doesn't know what it's talking about_, regardless of all the 341GB of text dropped into it. 2/
And in a sense, this is not unexpected. The lack of semantics was already clear with GPT, and hopefully Google will release this model to the public to play with as well. But even the published info is informative enough. 3/
Read 10 tweets
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Anima Anandkumar (hiring at #NeurIPS2019)
@AnimaAnandkumar
I disagree. It's difficult to understand and fix bias through #AI algorithms for #DeepLearning These are highly non-linear blackbox models. they amplify biases. many works show that trying to fix superficially is like putting lipstick on a pig. We need fresh thinking
In a recent work we report an intriguing finding to detect bias : assess hardness of different samples for #DeepLearning model. It's a first step. Using it to fix bias is much harder
More importantly our work shows that #DeepLearning model is deciding which samples are hard for it to classify and introducing bias. Here I use bias to mean disparate treatment not statistical bias. Model leaves harder example with worse accuracy and more vulnerability to noise
Read 9 tweets
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Carlos E. Perez 🧢
@IntuitMachine
New Yorker article on the "Machine Writing": newyorker.com/magazine/2019/… #ai
In this strange new world enabled by "tab", our writing may end up to be composed mostly of emojis.
"The creepy thing was that the machine was more thoughtful than I was"
Read 15 tweets
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Dr Murphy
@DrMurphy11
'Bad Bot' (part II) by @DrMurphy11

The story of a dangerously flawed #eHealth AI Chatbot.

A THREAD about #HealthTech #Hype, #AI #Governance & #MedicalDevice #PatientSafety.

Read on, or see the single tweet summary👇0/45
This thread follows on from a prior thread ('Bad Bot' - part I) & picks up the Babylon story from the #BabylonAITest event which was held on the 27th June 2018.

'Bad Bot' (part I) - 👇 1/45
The #BabylonAITest event was held at @RCPLondon & promised to reveal to the world, Babylon's latest "ground breaking research" & "the most exciting advances in Artificial Intelligence".

Dr Murphy was expecting Big Things from @babylonhealth...

2/45
Read 47 tweets

Trending hashtags

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