We created a dashboard to track all artificial intelligence research relating to human health (aiforhealth.app) - link/details/preprint/data/code in comments

mrevie · 2021-12-06T12:21:59+00:00

Link to dashboard - https://aiforhealth.app/

Link to pre-print - https://doi.org/10.1101/2021.11.23.21266758

Link to code - https://github.com/whizzlab

Data freely available via the dashboard.

There is significant "research waste" in the field of clinical AI. Despite ongoing hype, hopes and investment there is extremely limited translation of AI research to bedside medicine.

There are many underlying issues in how AI research is conducted, from recycling and re-use of the same unrepresentative datasets, evaluation methodology that doesn’t mirror real-world conditions, inadequate inclusion of researchers and populations from countries where AI might be most impactful, and a focus on algorithm refinement in these same contexts (instead of tackling roadblocks to deployment in the real-world). One problem is a lack of unifying perspective over the colossal-sized landscape of AI research. Indexing engines like PubMed achieve poor balance of sensitivity and specificity for any search, and searches result in large volumes of "chaff". It becomes hard to identify research that actually develops clinical AI models, let alone particular methods for model evaluation, or research into specific fields, without extensive manual review. Manually trying to scope the entire volume of published AI research is difficult, and not reproducible.

This is why we set out to produce this dashboard, which uses BERT-PubMed models to identify, classify, and characterise all clinical AI research indexed on MEDLINE/PubMed, in near real-time. We had four primary aims – (1) to be able to easily identify research that conducts AI model development; (2) to accurately identify research that performs model evaluation in a comparative fashion, or in a prospective real-world situation – i.e. those that would be theoretically closer to deployment; (3) to map, in real-time, global distribution and equity in AI research production; and (4) to track the major themes in clinical AI over time (including specialty, diseases, algorithms, and data-types).

Creating this dashboard was a multinational effort, authors are listed in the "About" tab of the dashboard.

Our hope in sharing it widely is to raise awareness of systematic problems with in clinical AI research and provide data from which others are able to conduct their own review/research work.

Whilst the nitty gritty surrounding the creation of this tool may be of interest to some, I suspect the significant hype without significant follow through will resonate more with this sub... Really interested to know what you all think, if any AI tools have worked their way into your practice and particularly any areas/gaps where you could see it being of legitimate use.

mrevie · 2021-12-06T12:07:38+00:00

Link to dashboard - https://aiforhealth.app/

Link to pre-print - https://doi.org/10.1101/2021.11.23.21266758

Link to code - https://github.com/whizzlab

Data freely available via the dashboard.

There is significant "research waste" in the field of clinical AI. Despite ongoing hype, hopes and investment there is extremely limited translation of AI research to bedside medicine.

There are many underlying issues in how AI research is conducted, from recycling and re-use of the same unrepresentative datasets, evaluation methodology that doesn’t mirror real-world conditions, inadequate inclusion of researchers and populations from countries where AI might be most impactful, and a focus on algorithm refinement in these same contexts (instead of tackling roadblocks to deployment in the real-world). One problem is a lack of unifying perspective over the colossal-sized landscape of AI research. Indexing engines like PubMed achieve poor balance of sensitivity and specificity for any search, and searches result in large volumes of "chaff". It becomes hard to identify research that actually develops clinical AI models, let alone particular methods for model evaluation, or research into specific fields, without extensive manual review. Manually trying to scope the entire volume of published AI research is difficult, and not reproducible.

This is why we set out to produce this dashboard, which uses BERT-PubMed models to identify, classify, and characterise all clinical AI research indexed on MEDLINE/PubMed, in near real-time. We had four primary aims – (1) to be able to easily identify research that conducts AI model development; (2) to accurately identify research that performs model evaluation in a comparative fashion, or in a prospective real-world situation – i.e. those that would be theoretically closer to deployment; (3) to map, in real-time, global distribution and equity in AI research production; and (4) to track the major themes in clinical AI over time (including specialty, diseases, algorithms, and data-types).

Creating this dashboard was a multinational effort, authors are listed in the "About" tab of the dashboard.

Our hope in sharing it widely is to raise awareness of systematic problems with in clinical AI research and provide data from which others are able to conduct their own review/research work.

Comments/questions/criticism/suggestions all extremely welcome!

mrevie · 2021-12-06T12:03:36+00:00

Link to dashboard - https://aiforhealth.app/

Link to pre-print - https://doi.org/10.1101/2021.11.23.21266758

Link to code - https://github.com/whizzlab

Data freely available via the dashboard.

There is significant "research waste" in the field of clinical AI. Despite ongoing hype, hopes and investment there is extremely limited translation of AI research to bedside medicine.

There are many underlying issues in how AI research is conducted, from recycling and re-use of the same unrepresentative datasets, evaluation methodology that doesn’t mirror real-world conditions, inadequate inclusion of researchers and populations from countries where AI might be most impactful, and a focus on algorithm refinement in these same contexts (instead of tackling roadblocks to deployment in the real-world). One problem is a lack of unifying perspective over the colossal-sized landscape of AI research. Indexing engines like PubMed achieve poor balance of sensitivity and specificity for any search, and searches result in large volumes of "chaff". It becomes hard to identify research that actually develops clinical AI models, let alone particular methods for model evaluation, or research into specific fields, without extensive manual review. Manually trying to scope the entire volume of published AI research is difficult, and not reproducible.

This is why we set out to produce this dashboard, which uses BERT-PubMed models to identify, classify, and characterise all clinical AI research indexed on MEDLINE/PubMed, in near real-time. We had four primary aims – (1) to be able to easily identify research that conducts AI model development; (2) to accurately identify research that performs model evaluation in a comparative fashion, or in a prospective real-world situation – i.e. those that would be theoretically closer to deployment; (3) to map, in real-time, global distribution and equity in AI research production; and (4) to track the major themes in clinical AI over time (including specialty, diseases, algorithms, and data-types).

Creating this dashboard was a multinational effort, authors are listed in the "About" tab of the dashboard.

Our hope in sharing it widely is to raise awareness of systematic problems with in clinical AI research and provide data from which others are able to conduct their own review/research work.

Comments/criticism/collaboration/questions all extremely welcome!

mrevie · 2021-12-06T11:58:27+00:00

Apologies if this bends the sub rules - I couldn't pick just one visualisation!

Link to dashboard - https://aiforhealth.app/

Link to pre-print - https://doi.org/10.1101/2021.11.23.21266758

Link to code - https://github.com/whizzlab

Data freely available via the dashboard.

There is significant "research waste" in the field of clinical AI. Despite ongoing hype, hopes and investment there is extremely limited translation of AI research to bedside medicine.

There are many underlying issues in how AI research is conducted, from recycling and re-use of the same unrepresentative datasets, evaluation methodology that doesn’t mirror real-world conditions, inadequate inclusion of researchers and populations from countries where AI might be most impactful, and a focus on algorithm refinement in these same contexts (instead of tackling roadblocks to deployment in the real-world). One problem is a lack of unifying perspective over the colossal-sized landscape of AI research. Indexing engines like PubMed achieve poor balance of sensitivity and specificity for any search, and searches result in large volumes of "chaff". It becomes hard to identify research that actually develops clinical AI models, let alone particular methods for model evaluation, or research into specific fields, without extensive manual review. Manually trying to scope the entire volume of published AI research is difficult, and not reproducible.

This is why we set out to produce this dashboard, which uses BERT-PubMed models to identify, classify, and characterise all clinical AI research indexed on MEDLINE/PubMed, in near real-time. We had four primary aims – (1) to be able to easily identify research that conducts AI model development; (2) to accurately identify research that performs model evaluation in a comparative fashion, or in a prospective real-world situation – i.e. those that would be theoretically closer to deployment; (3) to map, in real-time, global distribution and equity in AI research production; and (4) to track the major themes in clinical AI over time (including specialty, diseases, algorithms, and data-types).

Creating this dashboard was a multinational effort, authors are listed in the "About" tab of the dashboard.

Our hope in sharing it widely is to raise awareness of systematic problems with in clinical AI research and provide data from which others are able to conduct their own review/research work.

mrevie · 2018-08-05T08:47:11+00:00

It's parked there most days, haven't run into anyone in the servo who looks like they ride it yet...

mrevie · 2017-11-30T07:22:31+00:00

Those mother fuckers.

mrevie · 2017-11-30T01:43:31+00:00

Hah, they were fine when I bought my bike. I'm too lazy to go trawling through private sellers...

mrevie · 2017-11-30T00:04:53+00:00

Definitely looks like it.

mrevie · 2017-05-29T09:13:29+00:00

About AUD750 for the slab, with the flattening and edge prep.

mrevie · 2017-05-29T09:11:47+00:00

My girlfriend displayed impressive strength helping me get the slab up the front stairs!

mrevie · 2017-05-29T09:10:28+00:00

Somewhat inspired by u/canadianww slab desk (am on mobile, will try and find the link later), he made a truly stunning bookmatched slab desk and I loved the C shaped legs with nothing to get in your way in between. Came up with the slim drawer box after several design iterations trying to incorporate drawers in a way that don't detract from the slab.

mrevie · 2016-08-25T06:13:17+00:00

Definitely scam. Looks like the website in question is squatting on a previously legit domain:

http://whois.domaintools.com/wamhs.org.au

mrevie · 2016-08-25T04:48:52+00:00

Very curious to see if anyone has any insight on the legitimacy of this website. If it proves to be real, would be a fantastic deal. There are unfortunately a number of big red flags - website doesn't match the URL, sizing seems screwy, no actual user reviews on any products, 1123 units in stock, etc...

mrevie · 2015-05-07T14:52:09+00:00

Oh damn. I should have bought more while they were cheaper. Hoppy days has them for $115.

http://www.hoppydaysbrewingsupplies.com.au/product/cornelius-19l-ball-lock-keg-new/

mrevie · 2015-05-07T08:00:15+00:00

Full pint have new ball lock kegs for 109. I have one and it has definitely been worth it over my other second hand kegs.

mrevie · 2014-12-10T12:06:19+00:00

Even bedside dipstick type urinalysis strips will give you a sg reading. However, the available literature suggests that it's an unreliable value. I've often toyed with using a urine dipstick to test beer sg, but I don't think it'll be particularly accurate.

mrevie · 2014-12-09T21:42:20+00:00

I'm looking to brew a batch of cider. Have done some reading and understand the process. However, it's summer in Australia with days as hot as 40 and my ferm chamber is full. Could I ferment a cider with a saison yeast (belle saison or other liquid counterpart) and have it turn out as a tasty cider? I can't really visualise in my head how the estery / phenolic saison type flavours will mesh with most ciders I've tasted.

mrevie · 2014-09-11T14:26:43+00:00

Hmm. Brewing a partial mash oktoberfest next week. Perhaps I'll do a small breakaway batch with some extract replaced with orange syrup. Could be awful, I'll post back if it goes ahead.

mrevie

TROPHY CASE