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Hello and welcome to yet another edition of the NeuroOp Guru. I'm here with my good friend Drew Carey from Johns Hopkins and the Wilmer Eye Institute. Hi Drew.

Hi Andy, happy to be here.

And today we're going to be talking about the question "Is artificial intelligence for optic disc photos ready for the clinic and the ER?" So Drew, maybe you could just give us a little background on why is this even a question?

Well, I think for a long time, we've come to realize that colleagues without ophthalmologic training, ER doctors, primary care doctors, neurologists are not so good at fundoscopy, looking at the back of the eye, and specifically the optic nerve. You know, they don't do it through dilated pupils like ophthalmologist do, they don't do it a lot. So it's not a skill set that they've kept up if they ever really refined it in medical school. But there are some conditions where it is really important to look in the back of the eye and at the optic nerve, especially if you have a patient coming in with headaches and vision changes. W

e want to know is the optic nerve swollen, you know, could this be ischemic optic neuropathy, an emergency like giant cell arteritis, or papilledema, where they could have some kind of Bergeon, intracranial CNS process going on. And we can't get an ophthalmologist in every emergency room in America, but it would be feasible to put a camera. And then the question is, well, who's going to look at the picture? It should be somebody who knows what an optic nerve is supposed to look like. Or could it be an artificial intelligence that's been trained. And so I think that was the major initiative for this project was trying to improve the diagnostic value of fundoscopy in conditions where it would be desired.

And so this AI trained on thousands of photos that they just loaded in there and taught it, what it's supposed to look for.

Yeah, so there's been, you know, a lot of work in in AI and subtypes of AI, including deep learning systems, machine learning. And it does, it takes thousands and thousands of images that have been carefully combed through, and that we labeled with what we call ground truth where we know exactly what that picture represents, to train the system.

Kind of like a resident has to see thousands of cases during their training, in order to, you know, develop good clinical intuition and understanding what's going on. So for this, this group, the BONSAI consortium, based out of Singapore, they, you know, asked for pictures from neuro-ophthalmologist all across the world to try and develop a diverse training set. With patients who they knew what the diagnosis was, they knew what that optic nerve was showing. And that's what they trained it on.

And so maybe you could just walk us through these results of the BONSAI and you can see that it was already 168 times faster, but let's see if it's better. We know it can be faster. But is it better? Just maybe you could walk us through A and B here in terms of error rate?

Yeah absolutely. So what they did is they took 800 new photos that the machine had never seen before. Which is really important. You don't want to ask the artificial intelligence to answer a question that it already knows the answer to. And they so they showed that to BONSAI, and then they showed it to 30 different clinicians, six were general ophthalmologists, six were optometrists, six neurologists, six internal medicine doctors and six emergency medicine doctors.

And they asked them to classify these optic nerve photos as normal papilledema or other. And so they said, they split the groups into two different the doctors into two different groups. They said these are folks with opthalmic expertise, ophthalmologist and optometrist and the other folks, the neurologists, internal medicine and emergency medicine. And so in A, they said the error rate for the doctors with ophthalmic expertise looking at one photo of one eye, so they didn't get the benefit of two eyes. They said the error rate was about 25% for doctors with ophthalmic expertise. And for doctors without ophthalmic expertise it was close to 45%.

And the deep learning system was about 16% compared to what we knew the actual photo was. We know that that the machine is really good. You know that's what it was trained to do. And then in B they broke it down. They said these are ophthalmologists, optometrists, neurologists, internist and the emergency medicine doctors. And the ophthalmologists and optometrists were both very similar at about 25%, which is what we saw when they were together. And then the neurologists were, you know, not quite as good running around 38%. While the internal medicine doctors who, I don't remember the last time my eye was looked at in a primary care doctor office visit, was 43%.

And then the emergency medicine doctors were about 45%. And this is, they didn't even have to look inside the eye, this is a good quality fundus photo that we were able to just give the doctor and say, you know, this is, this is what the optic nerves looks like. And again, you know, the deep learning system was running around 16% for, you know, all the pictures. So that's, that's the comparison, which I think is really good. And we know that right, it's a machine, it doesn't have to stop and think about it. And you know, go through, okay, what's this blood vessel doing? What's that blood vessel do? It just looks at it and runs it through the algorithm, and takes about 25 seconds for it to look at all 800 photos.

Versus 70 minutes for the doctors, which I think 70 minutes to look at 800 photos is still pretty good. So that's what we found out. So the BONSAI had significantly higher accuracies in 100% of the papilledema cases, 87% of the normal cases, and 93% of the other cases, compared to the clinicians. So it's really good. I don't think it's ready to replace doctors, you know, neuro-ophthalmologists, because it's not perfect. And there's a lot of other clinical information.

We all know how important that history is for neuro-ophthalmology that it can't do. But it could really help to risk stratify. You know, this is somebody that really needs to see neuro ophthalmology or get an ophthalmologist in here in person to look at the patient, or say no, this is normal. Or this is somebody who needs to proceed to neuroimaging, lumbar puncture, you know, even if we can't get an ophthalmologist in here.

So do you think it's more like a decision support right now, like helps you make a decision? Or do you think it's not even that?

I think that's where it would be, you know, if this could be clinically implemented into emergency rooms, neurologist office. You know, the patient comes in and every patient with a headache, they get their blood pressure check to make sure it's not hypertensive emergency. They should get a photo of their optic nerve to make sure it's not elevated intracranial pressure or hypertensive emergency.

So and then, you know, the doctor can look at it. And the other thing that we know about the AI, compared to a doctor is it's not just a yes, no, it also gives it probabilities. It'll say I'm 100% certain this is normal. Or it'll say I'm 100% certain this is pappilledema. Ot it might say, it's probably papilladema, but I'm 65%.

You say okay, well, let's get some more data. Let's get an ophthalmologist in here to look at both eyes and ask some important questions like, do you have headaches? Do you have wooshing sounds in your ears? If you're having transient visual obscurations when you're bending over or coughing?

Well, so maybe the answer is stay tuned to this channel. But it certainly sounds like the machine is faster. And maybe even better than the doctors. The question is, is it cheaper?

Well, like you know, a lot of emergency rooms don't have an ophthalmologist on call. And if you're asking how much is it going to cost to pay somebody to cover call, he's not going to do it for free. And, you know, we could bill for photos that might be revenue generating as opposed to revenue loss. I think the big questions is regulatory.

You know, I think in the United States, we have one, that I'm aware of, FDA approved AI system, which is for retinal screening for diabetic retinopathy. They're looking at it for implementing into neuroimaging for CT scans to help to triage, this is a CT scan, we need the neuroradiologist to look at right now or put this one at the end of the pile to finish by the end of their shift. Yeah, cost is a big question. And it still has to go through FDA approval and then you know, it's still wrong 16% of the time, who's liable when it's wrong? You know, what's, what's the safety mechanism for the patient?

But compared to the safety mechanism, without it, you know, either nobody's looking or somebody's looking who's gonna be wrong, like half the time, I'd say, you know, if I was in the emergency room, have the machine, take a picture and tell me how I'm doing.

Well, Drew, as always a pleasure to chat with you. And that concludes yet another edition of the NeuroOp Guru. We'll see you guys next time.

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VLOG: Is artificial intelligence for optic disc photos ready for the ... - Ophthalmology Times

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What is AI governance?

Artificial intelligence (AI) governance is about establishing a legal framework for ensuring the safe and responsible development of AI systems.

In the AI governance debate, society, regulators, and industry leaders are looking to implement controls to guide the development of AI solutions, from ChatGPT to other machine learning-driven solutions, to mitigate social, economic, or ethical risks that could harm society as a whole.

Risks associated with AI include societal and economic disruption, bias, misinformation, data leakage, intellectual property theft, unemployment due to automation, or even weaponization in the form of automated cyberattacks.

Ultimately, the end goal of AI governance is to encourage the development of safe, trustworthy, and responsible AI, defining acceptable use cases, risk management frameworks, privacy mechanisms, accuracy, and, where possible, impartiality.

AI governance and regulation are important for understanding and controlling the level of risk presented by AI development and adoption. Eventually, it will also help to develop a consensus on the level of acceptable risk for the use of machine learning technologies in society and the enterprise.

However, governing the development of AI is very difficult because not only is there no centralized regulation or risk management framework for developers or adopters to refer to, but it is also challenging to assess risk when this changes depending on the context the system is used within.

Looking at ChatGPT as an example, enterprises not only have to acknowledge that hallucinations can spread bias, inaccuracies, and misinformation, but they also have to be aware that user prompts can be considered leaked to OpenAI. They also need to consider the impact that AI-generated phishing emails will have on their cybersecurity.

More broadly, regulators, developers and industry leaders need to consider how to reduce the inaccuracies or misinformation presented by large language models (LLMs), as this information could potentially have the ability to influence public opinion and politics.

At the same time, regulators are attempting to strike a balance between mitigating risk without stifling innovation among smaller AI vendors.

Before regulators and industry leaders can have a more comprehensive perspective of AI-related risks, they first need more transparency over the decision-making processes of automated systems.

For instance, the better the industry understands how an AI platform comes to a decision after processing a dataset, the easier it is to identify whether that decision is ethical or not and whether the vendors processing activities respect user privacy and comply with data protection regulations such as the General Data Protection Regulation (GDPR).

The more transparent AI development is, the better risks can be understood and mitigated. As Brad Smith, vice chair and president of Microsoft, explained in a blog post in May 2023, When we at Microsoft adopted our six ethical principles for AI in 2018, we noted that one principle was the bedrock for everything else accountability.

This is the fundamental need: to ensure that machines remain subject to effective oversight by people, and the people who design and operate machines remain accountable to everyone else.

Without transparency over how AI systems process data, there is no way to assess whether they are developed with a concerted effort to remain impartial or if they are simply developed with the values and biases of their creators.

On 26 January 2023, the U.S. National Institute of Standards and Technology (NIST) released its AI risk management framework, a voluntary set of recommendations and guidelines designed to measure and manage AI risk.

NISTs standard is one of the first comprehensive risk management frameworks to enter the AI governance debate, which looks to promote the development of trustworthy AI.

Under this framework, NIST defines risks as anything that has the potential to threaten individuals civil liberties, which emerges due to the nature of AI systems themselves or how a user interacts with them. Crucially, NIST highlights that organizations and regulators need to be aware of the different contexts in which AI can be used to fully understand risk.

NIST also highlights four core functions organizations can use to start controlling AI risks:

It is important to note that NISTs framework has many critics due to the fact its a voluntary framework, so theres no regulatory obligation for organizations to develop AI responsibly at this stage.

One of the main barriers to AI governance at the moment is the black box development approach of AI leaders like Microsoft, Anthropic, and Google. Typically, these vendors will not disclose how their proprietary models work and make decisions in an attempt to maintain a competitive advantage.

While a black box development approach allows AI vendors to protect their intellectual property, it leaves users and regulators in the dark about the type of data and processing activities their AI solutions use to come to decisions or predictions.

Although other vendors in the industry, like Meta, are looking to move away from black box development to an open-source and transparent approach with LLMs like Llama 2, the opaqueness of many vendors makes it difficult to understand the level of accuracy or bias presented by these solutions.

AI governance is critical to guiding the development of the technology in the future and implementing guardrails to ensure that it has mainly positive outcomes for society as a whole.

Building a legal framework for measuring and controlling AI risk can help users and organizations to experiment with AI freely while looking to mitigate any adverse effects or disruption.

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What is Artificial Intelligence (AI) Governance? Why Is It Important? - Techopedia

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Augusta County School Board chairman, Nick Collins, wants to provide opportunities for board members to share their interests and ideas with the community. Collins believes that new voices and perspectives are beneficial for the school system. Recently elected board members, Timothy Simmons and Mike Lawson, have expressed their desire to bring new ideas and approaches to the board.

Simmons, in particular, has shown interest in joining the School Board Member Alliance, an organization that supports school board members with resources and training opportunities. The board unanimously approved Simmons request to join the organization at his own expense.

During a recent meeting, Lawson expressed his interest in exploring the topic of artificial intelligence (AI) in the classroom. Collins acknowledged that AI is a complex subject with many implications for education. While the issue will not be on the agenda for the upcoming meeting, Lawson will provide an update on the progress made in meetings related to AI.

The use of AI in education has become a nationwide concern. Some school districts have banned certain AI applications, while others see the potential benefits in tutoring programs and assessment systems. The Augusta County School Board will share some of their discussions on AI during the upcoming meeting.

In addition to the AI discussion, Superintendent Eric Bond will provide an update on Governor Glenn Youngkins new model policies released in July. Collins does not anticipate extensive board discussion on the policies but expects that staff will review them, potentially leading to further discussion in September.

Upcoming meetings for the community include the Staunton Augusta Waynesboro Metropolitan Planning Organization, Augusta County Board of Supervisors special called meeting, Augusta County Board of Zoning Appeals, Augusta County School Board meeting discussing AI, Community Policy Management Team, Staunton Lewis Creek Watershed Advisory Committee, Augusta County Planning Commission, and Waynesboro School Board meeting.

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Augusta County School Board Explores Artificial Intelligence in ... - Fagen wasanni

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Transparency Market Research

The global market share for AI chipsets is being dramatically increased by the rise in demand for intelligent consumer electronics products

Wilmington, Delaware, United States, July 31, 2023 (GLOBE NEWSWIRE) -- Transparency Market Research Inc. - The global market for artificial intelligence chipset was estimated to have acquired a market valuation of around US$ 45.5 billion in 2021. The market is likely to garner a robust 31.8% CAGR from 2022 to 2031 and by 2031, the market is likely to gain US$ 717.4 billion.

Smart gadgets that are being utilized in daily life are becoming more and more common. During the forecast period, growth in the market is projected to be driven by an increase in demand for AI chips within consumer electronics products, including as high-end mobile devices, tablets, intelligent speakers, and wearables.

Leading smartphone companies are working to create AI-based devices that use less power and can do up to 5 trillion processes per second. AI image recognition is even being used to enhance the quality of photos.

General-purpose CPUs struggle to manage the growing volume of data created by smart devices for AI applications. ASIC and GPU chips are therefore utilized in AI applications. ASICs integrate established algorithms and are created for certain purposes, such as artificial intelligence or cloud computing.

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Market Snapshot:

Report Coverage

Details

Market Revenue

US$ 45.5 Bn

Estimated Value

US$ 717.4 Bn

Growth Rate - CAGR

31.8%

Forecast Period

2023-2031

No. of Pages

178 Pages

Market Segmentation

By Type, By Placement, By Distribution Channel

Regions Covered

North America, Europe, Asia Pacific, Middle East & Africa, South America

Companies Covered

Chipset Type, Deployment, Application, End-use Industry

Key Findings of the Market Report

Story continues

Market Trends for Artificial Intelligence Chipsets

Consumer goods including smartphones, computers, tablets, smart home appliances, smart speakers, as well as cameras are increasingly using AI processors.

General-purpose chips that are capable of being used in robots, washing machines, vacuum cleaners, and freezers are being developed by businesses.

Artificial intelligence (AI) semiconductor chips have been created to improve user experience. During the projection period, market statistics are anticipated to be driven by an increase in consumer electronics demand.

Home appliance producers are concentrating on creating cutting-edge items to meet the growing customer demand. They are working to create their own chips to lessen their dependency on other parties for supply chain reliability. For example, the Midea Group founded the Shanghai Meiren Semiconductor Co Ltd subsidiary, which engages in the design, manufacture, and marketing of intelligent power module chips alongside micro control unit chips utilized in electrical control systems, allowing the producer of home appliances to compete in the AI chip market.

Buy this Premium Research Report | Immediate Delivery Available - https://www.transparencymarketresearch.com/checkout.php?rep_id=44757&ltype=S

Global Artificial Intelligence Chipset Regional Market Outlook

During the projected period, North America is anticipated to lead the worldwide market. The United States is predicted to have considerable growth potential in the next years due to the growing use of electronic design automation (EDA) technologies to create cutting-edge AI hardware.

The biggest markets for consumer electronics and automobiles are in Asia Pacific. In terms of manufacturing and the use of lower-edge nodes to produce AI hardware, China complies with the lead of the United States.

Global Artificial Intelligence Chipset: Key Players

A number of significant AI software firms are working to create their own AI chips to maximize their uses. In the near future, this is anticipated to greatly increase market competitiveness. Numerous vendors, including new and established companies, are actively developing and selling AI chipsets.

Some manufacturers of AI chipsets are placing a strong emphasis on integrating neural networks into their AI hardware. Some of the top players within the global artificial intelligence chipset market are Alphabet, NVIDIA Corporation, Intel Corporation, IBM, Apple, Huawei, MediaTek, Google, Intel, Baidu, and Graphcore. Some developments by the key players in the global market for artificial intelligence chipsets are:

In 2023, AMD introduced new mobile CPUs as well as GPUs, featuring the first x86 PC CPU with a specialized AI engine. AMD also introduced a new 3D stacked desktop CPU with exceptional gaming performance at CES 2023, as well as a data center APU and an AI inference accelerator.

In 2022, MediaTek introduced the Dimensity 1080. It is the most recent chipset available for 5G smartphones within the Dimensity range. Compared to the Dimensity 920, it had superior performance and updated camera functions.

In 2022, the National Institute of Standards and Technology (NIST) of the United States Department of Commerce inked a joint research and development agreement alongside Google to create and manufacture chips for novel semiconductors as well as nanotechnology devices.

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Global Artificial Intelligence Chipset Market Segmentation By Chipset Type

By Deployment

Cloud-based AI Chips

Edge-based AI Chips

By Application

By End Use Industry

Automotive

IT and Telecommunication

Healthcare

Consumer Electronics

BFSI

Industrial

Others

By Region

North America

Europe

Asia Pacific

Middle East & Africa

South America

About Transparency Market Research

Transparency Market Research, a global market research company registered at Wilmington, Delaware, United States, provides custom research and consulting services. Our exclusive blend of quantitative forecasting and trends analysis provides forward-looking insights for thousands of decision makers. Our experienced team of Analysts, Researchers, and Consultants use proprietary data sources and various tools & techniques to gather and analyses information.

Our data repository is continuously updated and revised by a team of research experts, so that it always reflects the latest trends and information. With a broad research and analysis capability, Transparency Market Research employs rigorous primary and secondary research techniques in developing distinctive data sets and research material for business reports.

Contact:

Nikhil SawlaniTransparency Market Research Inc.CORPORATE HEADQUARTER DOWNTOWN,1000 N. West Street,Suite 1200, Wilmington, Delaware 19801 USATel: +1-518-618-1030USA Canada Toll Free: 866-552-3453Website:https://www.transparencymarketresearch.comBlog:https://tmrblog.comEmail:sales@transparencymarketresearch.com

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Global Market for Artificial Intelligence Chipset Anticipated to Surpass US$ 700 billion, Rising at a CAGR of 31.8% from 2022 to 2031: TMR study -...

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The post John Lydon on Artificial Intelligence: It Will Ultimately Make Decisions for You, and Thats Very Dangerous appeared first on Consequence.

John Lydon, aka Johnny Rotten, once famously sang of No Feelings, but when it comes to artificial intelligence, the punk icon has very strong feelings. Mainly, he doesnt like the direction in which its heading.

The Sex Pistols/Public Image Ltd. frontman discussed his views on the use of AI in a new interview with The Guardian, and he had words of warning regarding its suddenly rampant use in everyday life.

Whos in charge and whos feeding the information and giving the guidelines to these artifices? Lydon questioned. What or where is the moral code? It has infiltrated young peoples minds now to the point of total domination. What will this create?

He then offered up his solution a gradual cleansing of AI from daily life.

My advice is make small steps against this and get that fucking Siri or whatever out of your house. It will ultimately make decisions for you, and thats very dangerous.

Lydon said that another symptom of AI would be the misrepresentation and the rewriting of history done so casually.

Ive got to deal with real human beings doing that, let alone artificial intelligence taking over, he said, harkening back to the Sex Pistols public fallout in 1978 as an example. Thats the other side of that coin.

Elsewhere in the interview, Lydon who faced backlash from fans for his surprising pro-Trump stance in 2020 threw in a sarcastic jab at the sitting president.

An artificially intelligent Joe Biden would be quite an issue, Lydon said. I find life quite hilarious.

Lydon is currently prepping the release of PiLs new album End of World, arriving August 11th. Pre-order the LP on vinyl here. The band will support the release by touring the UK and Europe this fall; tickets are available here.

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John Lydon on Artificial Intelligence: It Will Ultimately Make Decisions for You, and Thats Very DangerousJon Hadusek

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