#Healthcare AI

[randybrian489]

Top Chinese VC Reveals Which AI Models and Industries Are Ripe for Investment

<h2>Top Chinese Startup Fund Looks to Invest in Large AI Models</h2> <p>On October 27th, Wei Zhe spoke at the 2023 Chongqing Venture Capital Conference. Wei Zhe is the founder and chairman of Jia Yu Capital, a leading Chinese venture capital firm. In his keynote speech titled "Adapting to New Tech Trends", Wei Zhe discussed opportunities in artificial intelligence (AI).</p> <p>Specifically, Wei Zhe talked about "large models", a type of advanced AI system. However, he said Jia Yu Capital is not currently ready to invest directly in any Chinese startups working on large models. There are a few good reasons for this:</p> <ol> <li>Building large AI models requires huge amounts of money - more than most startups can handle alone.</li> <li>It's still unclear how large models can be used commercially and make money.</li> <li>Startups developing large models may not have enough data to train their AI systems properly.</li> </ol> <p>Wei Zhe suggested a smarter approach. He thinks China's big tech giants, like Baidu, Alibaba or Tencent, should each develop their own large AI models. But they should also invest in other companies working in this area. That way, even if their own projects fail, they might succeed through the companies they fund. </p> <p>Wei Zhe said he's seeing this already start to happen. Several Chinese AI startups have recently received investments from major internet companies. In his view, this trend of collaboration is great for both the startups and the bigger companies. It will help large AI models progress faster in China.</p> <h3>Three Industries Primed for AI Growth</h3> <p>Wei Zhe was also asked about which industries are best positioned to combine with AI, known as "AI plus industries." He emphasized being cautious, since AI is still new. However, based on current data, he believes three fields in particular show promise:</p> <ol> <li>Healthcare - Medical data is often kept private, avoiding data issues.</li> <li>Finance - Customer financial info is also usually private.</li> <li>Gaming - Game data tends to involve simulated worlds rather than real people.</li> </ol> <p>In these industries, AI systems have access to sufficient private data to learn and improve without worrying as much about privacy or regulation. As a result, Wei Zhe thinks these areas offer big opportunities for Chinese companies pursuing "AI plus" applications and business models.</p>

#healthcare AI

[cyntexa]

According to a study by BusinessDIT, 90% of the brands worldwide are investing in AI, and 83% strongly believe that AI will help them maintain or gain competitive edge.

From enabling personalized financial services to optimized store layouts in retail industry, AI is revolutionizing various sectors across industries.

Our recent blog unveils how AI is transforming industries like finance, retail, healthcare, manufacturing, and retail.

#ai #finance revolution #retail tech #healthcare ai #manufacturing automation #marketing ai #ai innovation

[akwyz]

Siemens and Microsoft partner to drive cross-industry AI adoption

Exciting times ahead as #Siemens and #Microsoft join forces to propel cross-industry #AI adoption! Introducing Siemens Industrial Copilot, your AI-powered assistant for enhanced human-machine collaboration. #SPS2023 #SIEX

Companies introduce Siemens Industrial Copilot, a generative AI-powered assistant, designed to enhance human-machine collaboration and boost productivity. Companies will work together to build additional copilots for manufacturing, infrastructure, transportation, and healthcare industries. Leading automotive supplier, Schaeffler AG, is an early adopter of Siemens Industrial Copilot. In…

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#Cross-industry AI adoption #Generative AI #Healthcare AI #Human-machine Collaboration #Industrial Metaverse #Infrastructure AI #Manufacturing Innovation #microsoft #Microsoft Teams Integration #Product Lifecycle Management #Productivity Enhancement #Schaeffler AG #Siemens #Siemens Industrial Copilot #SPS Expo Nuremberg #Teamcenter #Transportation AI #Virtual Collaboration

[market-insider]

Revolutionizing Cancer Diagnosis: An In-Depth Analysis of the AI in Cancer Diagnostics

The global AI in cancer diagnostics market size is expected to reach USD 996.1 million by 2030, growing at 26.3% CAGR from 2023 to 2030, according to a new report by Grand View Research, Inc. The increasing need for lowering healthcare costs, the rising importance of big data in healthcare, improving adoption of precision medicine, and declining hardware costs are key factors driving the growth.

AI In Cancer Diagnostics Market Report Highlights

Based on component, the software solutions segment held the largest market share of 43.7% in 2022. The development of AI-based software solutions for cancer diagnostics is one of the key factors boosting segment growth

Based on cancer type, the other cancers segment emerged as the largest segment with a revenue share of 33.6% in 2022. Growing adoption of a sedentary lifestyle increased alcohol & tobacco consumption, and physical inactivity are driving the incidence of cancers such as bladder and skin cancers

Based on end-user, the hospital segment emerged as the largest end-user in the market, with a market share of 57.7% in 2022. The growing shortage of medical professionals and technological advancements in hospitals is expected to drive the segment

North America dominated the global market with a share of 56.0% in 2022. The rising adoption of healthcare IT solutions, the well-established healthcare sector, and the availability of funding for developing AI capabilities are some of the factors contributing to the growth of the market in the region

Gain deeper insights on the market and receive your free copy with TOC now @: Artificial Intelligence In Cancer Diagnostics Market Report

The increasing scope of applications of artificial intelligence (AI) in various healthcare fields, including diagnostics; the rising prevalence of cancer; and the growing shortage of public health workforce are some of the key factors anticipated to fuel the adoption of artificial intelligence (AI) in cancer diagnostics over the forecast period. In addition, the increasing applicability of AI-based tools in cancer care and the rise in venture capital investments is further driving the surge in demand for this technology.

The presence of prominent players in the market such as Microsoft, Flatiron, Therapixel, and Tempus, is anticipated to positively impact the growth. These players are adopting strategies such as acquisitions, collaborations, expansions, and new product launches to increase the reach of their products in the industry and increase the availability of their products & services in diverse geographical areas. For instance, in December 2021, Microsoft announced a partnership with CVS Health to develop innovative solutions for patients to improve their health while empowering healthcare professionals with tools to better service patients.

Furthermore, the rising government support in the form of funding and initiatives for the development of healthcare infrastructure is anticipated to drive the demand for technologically advanced and cost-efficient devices over the forecast period.

#AI in Cancer Diagnostics #Cancer Diagnosis #Medical AI #Oncology #Cancer Detection #HealthTech #Cancer Research #Diagnostic AI #Digital Health #Cancer Care #Radiology AI #Healthcare AI

[harshalj79]

Healthcare Artificial Intelligence Market by Product and Services (Software, Services), Technology (Machine Learning, NLP), Application (Medical Imaging, Precision Medicine, Patient Management), End User (Hospitals, Patients) - Global Forecast to 2027

#healthcare ai #healthcare #ai in healthcare #artificial intelligence #health tech #technology #innovation #business #Healthcare Artificial Intelligence Market #Healthcare Artificial Intelligence

[aifyit]

Artificial General Intelligence: The Dawn of a New Era

Introduction Are you captivated by the technological advancements of our time, but also intrigued by the infinite possibilities yet to come? Then you’re in the right place! Today, we dive into the fascinating world of Artificial General Intelligence (AGI). This technology promises to transform our society, revolutionizing industries and even the way we live our lives. But what exactly is AGI?…

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#Advancements in AI #AGI #AGI Applications #AGI Benefits #AGI Challenges #AGI Development #AGI Misuse #AGI Policy #AGI Risks #AGI Safety #AGI Timeline #AI Ethics #AI research #Artificial General Intelligence #Existential Risk #Future of AGI #Healthcare AI #Job Displacement #machine learning #Narrow AI vs AGI #Neural networks

[atozofsoftwareengineering]

25 Ways Healthcare Data is Revolutionizing the Healthcare Industry

An exhaustive list for healthcare and healthcareIT colleagues and friends. Happy Reading!

Healthcare data refers to the information collected from various sources in the healthcare system, including medical records, laboratory results, and insurance claims. This data can be used in numerous ways to advance healthcare and improve patient outcomes. From improving patient safety to developing personalized treatment plans, healthcare data is a valuable tool that can help healthcare…

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#big data in healthcare #electronic health records #health data privacy #health data security #health information technology #healthcare AI #Healthcare Analytics #healthcare blockchain #healthcare data #healthcare data ethics #healthcare data governance #healthcare data integration #healthcare data mining #healthcare data storage #healthcare data visualization #healthcare data warehousing #Healthcare Interoperability #healthcare machine learning #medical informatics #patient outcomes #personalized medicine #Population Health Management #predictive analytics in healthcare #telemedicine #wearables in healthcare

[incorporationai]

 Incorporation.AI: Revolutionize Your Processes with Automation Documentation

Are you tired of tedious and repetitive tasks slowing down your business operations? Incorporation.AI has the solution you need to streamline your processes and increase efficiency. Our comprehensive automation documentation allows you to automate your workflows, freeing up valuable time and resources for more important tasks.

With our platform, you can easily document your existing processes and workflows, identify areas for improvement, and implement automated solutions. From data entry and analysis to customer service and marketing, our automation documentation can help your business achieve its full potential.

In addition to automation documentation, Incorporation.AI offers a range of other automation services, including machine learning and natural language processing. Our AI-powered solutions can help you analyze and interpret complex data, automate customer interactions, and more.

At Incorporation.AI, we believe that automation should be accessible to businesses of all sizes. That's why we offer affordable pricing plans and exceptional customer support to help you get the most out of our platform.

Ready to revolutionize your processes with automation documentation? Visit Incorporation.AI today and discover the future of business automation. Don't forget to use IncorporationAI and AutomationDocumentation to join the conversation! 🚀📈💻👩‍💼👨‍💼

#Automation Documentation #healthcare ai #ai healthcare solutions #transformation ai #incorporationai #ai consulting #artificialintelligence

[needtricks-blog]

From Mythology to Reality: Exploring the Past, Present, and Future of AI

Artificial Intelligence (AI) has rapidly become a ubiquitous part of our lives, from voice assistants and image recognition to self-driving cars and personalized advertising. While the technology continues to advance, there is still much debate and discussion around the implications and ethics of its use. (more…) “”

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#AI and job displacement #AI and privacy #AI applications #AI ethics #AI history #AI impact on society #Artificial Intelligence #Autonomous vehicles #Bias in AI #Future of AI #Healthcare AI #Image recognition #predictive analytics #Robotics #Virtual reality #Voice assistants

[qwikskills]

Unleashing the Power of Machine Learning in the 21st Century

Machine learning is one of the most talked about and rapidly growing fields in the tech industry. It is a branch of artificial intelligence that allows computers to learn and make predictions or decisions without explicit programming. The rise of big data and the increasing availability of computing power have made it possible for machine learning algorithms to handle vast amounts of data and provide valuable insights and predictions.

In recent years, machine learning has been applied in various industries, ranging from healthcare to finance, retail, and marketing. In healthcare, machine learning algorithms are used to analyze patient data and help doctors make more accurate diagnoses. In finance, machine learning is used to detect fraud, analyze financial markets, and make investment decisions. In retail, machine learning is used to personalize shopping experiences, recommend products, and optimize pricing.

One of the key benefits of machine learning is that it allows for automated decision-making, which can save time and resources. Machine learning algorithms can analyze large amounts of data and provide insights in real-time, enabling organizations to make data-driven decisions more efficiently. Additionally, machine learning algorithms are able to improve over time, becoming more accurate as they are exposed to more data.

Despite its many advantages, machine learning is not without its challenges. One of the main challenges is the lack of transparency in decision-making. It can be difficult to understand how machine learning algorithms arrived at a particular decision, making it difficult to explain the decision to stakeholders. Additionally, machine learning algorithms can be biased if the data used to train them is biased, leading to unfair or inaccurate decisions.

In conclusion, machine learning is a powerful tool that has the potential to transform the way we live and work. As the technology continues to evolve and improve, we can expect to see more and more applications of machine learning in various industries. However, it is important to approach machine learning with caution and ensure that the algorithms are developed and used in a transparent and ethical manner.

#Machine Learning #Artificial Intelligence #Data Science #Predictive Modeling #Deep Learning #Neural Networks #Natural Language Processing #Image Recognition #Predictive Analytics #Big Data #Supervised Learning #Unsupervised Learning #Reinforcement Learning #Predictive Maintenance #Recommender Systems #Fraud Detection #Predictive Marketing #Healthcare AI #Computer Vision #Predictive Sales #Predictive Quality Control #Predictive Logistics

[mostlysignssomeportents]

Dinkclump Linkdump

I'm on tour with my new novel The Bezzle! Catch me TONIGHT in LA (Saturday night, with Adam Conover), Seattle (Monday, with Neal Stephenson), then Portland, Phoenix and more!

Some Saturday mornings, I look at the week's blogging and realize I have a lot more links saved up than I managed to write about this week, and then I do a linkdump. There've been 14 of these, and this is number 15:

https://pluralistic.net/tag/linkdump/

Attentive readers will note that this isn't Saturday. You're right. But I'm on a book tour and every day is shatterday, because damn, it's grueling and I'm not the spry manchild who took Little Brother on the road in 2008 – I'm a 52 year old with two artificial hips. Hence: an out-of-cycle linkdump. Come see me on tour and marvel at my verticality!

https://pluralistic.net/2024/02/16/narrative-capitalism/#bezzle-tour

Best thing I read this week, hands down, was Ryan Broderick's Garbage Day piece, "AI search is a doomsday cult":

https://www.garbageday.email/p/ai-search-doomsday-cult

Broderick makes so many excellent points in this piece. First among them: AI search sucks, but that's OK, because no one is asking for AI search. This only got more true later in the week when everyone's favorite spicy autocomplete accidentally loaded the James Joyce module:

https://arstechnica.com/information-technology/2024/02/chatgpt-alarms-users-by-spitting-out-shakespearean-nonsense-and-rambling/

(As Matt Webb noted, Chatbots have slid rapidly from Star Trek (computers give you useful information in a timely fashion) to Douglas Adams (computers spout hostile, impenetrable nonsense at you):

https://interconnected.org/home/2024/02/21/adams

But beyond the unsuitability of AI for search results and beyond the public's yawning indifference to AI-infused search, Broderick makes a more important point: AI search is about summarizing web results so you don't have to click links and read the pages yourself.

If that's the future of the web, who the fuck is going to write those pages that the summarizer summarizes? What is the incentive, the business-model, the rational explanation for predicting a world in which millions of us go on writing web-pages, when the gatekeepers to the web have promised to rig the game so that no one will ever visit those pages, or read what we've written there, or even know it was us who wrote the underlying material the summarizer just summarized?

If we stop writing the web, AIs will have to summarize each other, forming an inhuman centipede of botshit-ingestion. This is bad news, because there's pretty solid mathematical evidence that training a bot on botshit makes it absolutely useless. Or, as the authors of the paper – including the eminent cryptographer Ross Anderson – put it, "using model-generated content in training causes irreversible defects":

https://arxiv.org/abs/2305.17493

This is the mathematical evidence for Jathan Sadowski's "Hapsburg AI," or, as the mathematicians call it, "The Curse of Recursion" (new band-name just dropped).

But if you really have your heart set on living in a ruined dystopia dominated by hostile artificial life-forms, have no fear. As Hamilton Nolan writes in "Radical Capital," a rogues gallery of worker-maiming corporations have asked a court to rule that the NLRB can't punish them for violating labor law:

https://www.hamiltonnolan.com/p/radical-capital

Trader Joe’s, Amazon, Starbucks and SpaceX have all made this argument to various courts. If they prevail, then there will be no one in charge of enforcing federal labor law. Yes, this will let these companies go on ruining their workers' lives, but more importantly, it will give carte blanche to every other employer in the land. At one end of this process is a boss who doesn't want to recognize a union – and at the other end are farmers dying of heat-stroke.

The right wing coalition that has put this demand before the court has all sorts of demands, from forced birth to (I kid you not), the end of recreational sex:

https://www.lawyersgunsmoneyblog.com/2024/02/getting-rid-of-birth-control-is-a-key-gop-agenda-item-for-the-second-trump-term

That coalition is backed by ultra-rich monopolists who want wreck the nation that their rank-and-file useful idiots want to wreck your body. These are the monopoly cheerleaders who gave us the abomination that is the Pharmacy Benefit Manager – a useless intermediary that gets to screw patients and pharmacists – and then let PBMs consolidate and merge with pharmacy monopolists.

One such inbred colossus is Change Healthcare, a giant PBM that is, in turn, a mere tendril of United Healthcare, which merged the company with Optum. The resulting system – held together with spit and wishful thinking – has access to the health records of a third of Americans and processes 15 billion prescriptions per day.

Or rather, it did process that amount – until the all-your-eggs-in-one-badly-maintained basket strategy failed on Wednesday, and Change's systems went down due to an unspecified "cybersecurity incident." In the short term, this meant that tens of millions of Americans who tried to refill their prescriptions were told to either pay cash or come back later (if you don't die first). That was the first shoe dropping. The second shoe is the medical records of a third of the country.

Don't worry, I'm sure those records are fine. After all, nothing says security like "merging several disparate legacy IT systems together while simultaneously laying off half your IT staff as surplus to requirements and an impediment to extracting a special dividend for the private equity owners who are, of course, widely recognized as the world's greatest information security practitioners."

Look, not everything is terrible. Some computers are actually getting better. Framework's user-serviceable, super-rugged, easy-to-repair, powerful laptops are the most exciting computers I've ever owned – or broken:

https://pluralistic.net/2022/11/13/graceful-failure/#frame

Now you can get one for $500!

https://frame.work/blog/first-framework-laptop-16-shipments-and-a-499-framework

And the next generation is turning our surprisingly well, despite all our worst efforts. My kid – now 16! – and I just launched our latest joint project, "The Sushi Chronicles," a small website recording our idiosyncratic scores for nearly every sushi restaurant in Burbank, Glendale, Studio City and North Hollywood:

https://sushichronicles.org/

This is the record of two years' worth of Daughter-Daddy sushi nights that started as a way to get my picky eater to try new things and has turned into the highlight of my week. If you're in the area and looking for a nice piece of fish, give it a spin (also, we belatedly realized that we've never reviewed our favorite place, Kuru Kuru in the CVS Plaza on North Hollywood Way – we'll be rectifying that soon).

And yes, we have a lavishly corrupt Supreme Court, but at least now everyone knows it. Glenn Haumann's even set up a Gofundme to raise money to bribe Clarence Thomas (now deleted, alas):

https://www.gofundme.com/f/pzhj4q-the-clarence-thomas-signing-bonus-fund-give-now

The funds are intended as a "signing bonus" in the event that Thomas takes up John Oliver on his offer of a $2.4m luxury RV and $1m/year for life if he'll resign from the court:

https://www.youtube.com/watch?v=GE-VJrdHMug

This is truly one of Oliver's greatest bits, showcasing his mastery over the increasingly vital art of turning abstruse technical issues into entertainment that negates the performative complexity used by today's greatest villains to hide their misdeeds behind a Shield of Boringness (h/t Dana Clare).

The Bezzle is my contribution to turning abstruse scams into a high-impact technothriller that pierces that Shield of Boringness. The key to this is to master exposition, ignoring the (vastly overrated) rule that one must "show, not tell." Good exposition is hard to do, but when it works, it's amazing (as anyone who's read Neal Stephenson's 1,600-word explanation of how to eat Cap'n Crunch cereal in Cryptonomicon can attest). I wrote about this for Mary Robinette Kowal's "My Favorite Bit" this week:

https://maryrobinettekowal.com/journal/my-favorite-bit/my-favorite-bit-cory-doctorow-talks-about-the-bezzle/

Of course, an undisputed master of this form is Adam Conover, whose Adam Ruins Everything show helped invent it. Adam is joining me on stage in LA tomorrow night at Vroman's at 5:30PM, to host me in a book-tour event for my novel The Bezzle:

https://www.vromansbookstore.com/Cory-Doctorow-discusses-The-Bezzle

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2024/02/23/gazeteer/#out-of-cycle

Image: Peter Craven (modified) https://commons.wikimedia.org/wiki/File:Aggregate_output_%287637833962%29.jpg

CC BY 2.0 https://creativecommons.org/licenses/by/2.0/deed.en

#pluralistic #the bezzle #ryan broderick #mary robinette kowal #exposition #john oliver #margot robbie #adam conover #ai #ai search #change healthcare #centralization #pharma #pbms #pharmacy benefit managers #corruption #scotus #crowdfunding #clarence thomas

[aivoluptulicious]

Big pharmas Your prescription is ready sir

#big pharma #mature woman #mature beauty #ai photography #pharmacist #professionalgrowth #large bust #cougar #ai generated #leonardo ai #woman #healthcare #rx #long hair #pharmacy services

[d0nutzgg]

Predicting Alzheimer's With Machine Learning

Alzheimer's disease is a progressive neurodegenerative disorder that affects millions of people worldwide. Early diagnosis is crucial for managing the disease and potentially slowing its progression. My interest in this area is deeply personal. My great grandmother, Bonnie, passed away from Alzheimer's in 2000, and my grandmother, Jonette, who is Bonnie's daughter, is currently exhibiting symptoms of the disease. This personal connection has motivated me to apply my skills as a data scientist to contribute to the ongoing research in Alzheimer's disease.

Model Creation

The first step in creating the model was to identify relevant features that could potentially influence the onset of Alzheimer's disease. After careful consideration, I chose the following features: Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR), Socioeconomic Status (SES), and Normalized Whole Brain Volume (nWBV).

MMSE: This is a commonly used test for cognitive function and mental status. Lower scores on the MMSE can indicate severe cognitive impairment, a common symptom of Alzheimer's.

CDR: This is a numeric scale used to quantify the severity of symptoms of dementia. A higher CDR score can indicate more severe dementia.

SES: Socioeconomic status has been found to influence health outcomes, including cognitive function and dementia.

nWBV: This represents the volume of the brain, adjusted for head size. A decrease in nWBV can be indicative of brain atrophy, a common symptom of Alzheimer's.

After selecting these features, I used a combination of Logistic Regression and Random Forest Classifier models in a Stacking Classifier to predict the onset of Alzheimer's disease. The model was trained on a dataset with these selected features and then tested on a separate dataset to evaluate its performance.

Model Performance

To validate the model's performance, I used a ROC curve plot (below), as well as a cross-validation accuracy scoring mechanism.

The ROC curve (Receiver Operating Characteristic curve) is a plot that illustrates the diagnostic ability of a model as its discrimination threshold is varied. It is great for visualizing the accuracy of binary classification models. The curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings.

The area under the ROC curve, often referred to as the AUC (Area Under the Curve), provides a measure of the model's ability to distinguish between positive and negative classes. The AUC can be interpreted as the probability that the model will rank a randomly chosen positive instance higher than a randomly chosen negative one.

The AUC value ranges from 0 to 1. An AUC of 0.5 suggests no discrimination (i.e., the model has no ability to distinguish between positive and negative classes), 1 represents perfect discrimination (i.e., the model has perfect ability to distinguish between positive and negative classes), and 0 represents total misclassification.

The model's score of an AUC of 0.98 is excellent. It suggests that the model has a very high ability to distinguish between positive and negative classes.

The model also performed extremely well in another test, which showed the model has a final cross-validation score of 0.953. This high score indicates that the model was able to accurately predict the onset of Alzheimer's disease based on the selected features.

However, it's important to note that while this model can be a useful tool for predicting Alzheimer's disease, it should not be the sole basis for a diagnosis. Doctors should consider all aspects of diagnostic information when making a diagnosis.

Conclusion

The development and application of machine learning models like this one are revolutionizing the medical field. They offer the potential for early diagnosis of neurodegenerative diseases like Alzheimer's, which can significantly improve patient outcomes. However, these models are tools to assist healthcare professionals, not replace them. The human element in medicine, including a comprehensive understanding of the patient's health history and symptoms, remains crucial.

Despite the challenges, the potential of machine learning models in improving early diagnosis leaves me and my family hopeful. As we continue to advance in technology and research, we move closer to a world where diseases like Alzheimer's can be effectively managed, and hopefully, one day, cured.

#alzheimersresearch #alzheimersdisease #dementia #neurology #machinelearning #ai #artificialintelligence #aicommunity #datascience #datascientist #healthcare #medicalresearch #programming #python programming #python #python 3

[harshalj79]

Healthcare Artificial Intelligence Market by Product and Services (Software, Services), Technology (Machine Learning, NLP), Application (Medical Imaging, Precision Medicine, Patient Management), End User (Hospitals, Patients) - Global Forecast to 2027

#Healthcare Artificial Intelligence Market #Healthcare Artificial Intelligence #healthcare ai #ai in healthcare #healthcare #health tech #technology

[reasonsforhope]

"In the oldest and most prestigious young adult science competition in the nation, 17-year-old Ellen Xu used a kind of AI to design the first diagnosis test for a rare disease that struck her sister years ago.

With a personal story driving her on, she managed an 85% rate of positive diagnoses with only a smartphone image, winning her $150,000 grand for a third-place finish.

Kawasaki disease has no existing test method, and relies on a physician’s years of training, ability to do research, and a bit of luck.

Symptoms tend to be fever-like and therefore generalized across many different conditions. Eventually if undiagnosed, children can develop long-term heart complications, such as the kind that Ellen’s sister was thankfully spared from due to quick diagnosis.

Xu decided to see if there were a way to design a diagnostic test using deep learning for her Regeneron Science Talent Search medicine and health project. Organized since 1942, every year 1,900 kids contribute adventures.

She designed what is known as a convolutional neural network, which is a form of deep-learning algorithm that mimics how our eyes work, and programmed it to analyze smartphone images for potential Kawasaki disease.

However, like our own eyes, a convolutional neural network needs a massive amount of data to be able to effectively and quickly process images against references.

For this reason, Xu turned to crowdsourcing images of Kawasaki’s disease and its lookalike conditions from medical databases around the world, hoping to gather enough to give the neural network a high success rate.

Xu has demonstrated an 85% specificity in identifying between Kawasaki and non-Kawasaki symptoms in children with just a smartphone image, a demonstration that saw her test method take third place and a $150,000 reward at the Science Talent Search."

-Good News Network, 3/24/23

#heart disease #pediatrics #healthcare #kawasaki #neural network #ai #science and technology #rare disease #medical tests #medical science #good news #hope

[juni-ravenhall]

if ur loudly anti ai bc u support artists, but then u think artists should do commissions for less than $100 a piece.... which is including hourly wage, the cost of their skill level (years of training), the tools (tablet etc if not trad materials), and even giving you rights to use the art..... then do you actually support artists right to earn a living wage or not?

#nonhorsgaem #just really fucking annoyed at ppl who think artists should work for pennies but then go omg ai is whats ruining jobs for artists #sweetie for that 15 dollars u wanted to pay the artist would need to generate 95% of the pic in ai to make it worthwhile #if they actually need to pay for food and living expenses and healthcare and you know things humans need to live #teenage artists who do it to earn extra money for unnecessary expenses should not normalise low costs and bring down - #the perceived value of artists work for everyone but thats exactly what happened during the history of digital art on the internet #and now even ppl who are actually trying to pay their bills and eat food are pushed to sell for nothing while you say “i hate ai” #but youre not equally loud about fair commission pricing and the actual value of artists work outside of the ai context