Tech
AI is transforming medicine: Here’s how we make sure it works for everyone
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What if your doctor could instantly test dozens of different treatments to discover the perfect one for your body, your health and your values? In my lab at Stanford University School of Medicine, we are working on artificial intelligence (AI) technology to create a “digital twin”: a virtual representation of you based on your medical history, genetic profile, age, ethnicity, and a host of other factors like whether you smoke and how much you exercise.
If you’re sick, the AI can test out treatment options on this computerized twin, running through countless different scenarios to predict which interventions will be most effective. Instead of choosing a treatment regimen based on what works for the average person, your doctor can develop a plan based on what works for you. And the digital twin continuously learns from your experiences, always incorporating the most up-to-date information on your health.
AI is personalizing medicine, but for which people?
While this futuristic idea may sound impossible, artificial intelligence could make personalized medicine a reality sooner than we think. The potential impact on our health is enormous, but so far, the results have been more promising for some patients than others. Because AI is built by humans using data generated by humans, it is prone to reproducing the same biases and inequalities that already exist in our healthcare system.
In 2019, researchers analyzed an algorithm used by hospitals to determine which patients should be referred to special care programs for people with complex medical needs. In theory, this is exactly the type of AI that can help patients get more targeted care. However, the researchers discovered that as the model was being used, it was significantly less likely to assign Black patients to these programs than their white counterparts with similar health profiles. This biased algorithm not only affected the healthcare received by millions of Americans, but also their trust in the system.
Getting data, the building block of AI, right
Such a scenario is all too common for underrepresented minorities. The issue isn’t the technology itself. The problem starts much earlier, with the questions we ask and the data we use to train the AI. If we want AI to improve healthcare for everyone, we need to get those things right before we ever start building our models.
First up is the data, which are often skewed toward patients who use the healthcare system the most: white, educated, wealthy, cisgender U.S. citizens. These groups have better access to medical care, so they are overrepresented in health datasets and clinical research trials.
To see the impact this skewed data has, look at skin cancer. AI-driven apps could save lives by analyzing pictures of people’s moles and alerting them to anything they should have checked out by a dermatologist. But these apps are trained on existing catalogs of skin cancer lesions dominated by images from fair-skinned patients, so they don’t work as well for patients with darker skin. The predominance of fair-skinned patients in dermatology has simply been transferred over to the digital realm.
My colleagues and I ran into a similar problem when developing an AI model to predict whether cancer patients undergoing chemotherapy will end up visiting the emergency room. Doctors could use this tool to identify at-risk patients and give them targeted treatment and resources to prevent hospitalization, thereby improving health outcomes and reducing costs. While our AI’s predictions were promisingly accurate, the results were not as reliable for Black patients. Because the patients represented in the data we fed into our model did not include enough Black people, the model could not accurately learn the patterns that matter for this population.
Adding diversity to training models and data teams
It’s clear that we need to train AI systems with more robust data that represent a wider range of patients. We also need to ask the right questions of the data and think carefully about how we frame the problems we are trying to solve. At a panel I moderated at the Women in Data Science (WiDS) annual conference in March, Dr. Jinoos Yazdany of Zuckerberg San Francisco General Hospital gave an example of why framing matters: Without proper context, an AI could come to illogical conclusions like inferring that a visit from the hospital chaplain contributed to a patient’s death (when really, it was the other way around — the chaplain came because the patient was dying).
To understand complex healthcare problems and make sure we are asking the right questions, we need interdisciplinary teams that combine data scientists with medical experts, as well as ethicists and social scientists. During the WiDS panel, my Stanford colleague, Dr. Sylvia Plevritis, explained why her lab is half cancer researchers and half data scientists. “At the end of the day,” she said, “you want to answer a biomedical question or you want to solve a biomedical problem.” We need multiple forms of expertise working together to build powerful tools that can identify skin cancer or predict whether a patient will end up in the hospital.
We also need diversity on research teams and in healthcare leadership to see problems from different angles and bring innovative solutions to the table. Say we are building an AI model to predict which patients are most likely to skip appointments. The working mothers on the team might flip the question on its head and instead ask what factors are most likely to prevent people from making their appointment, like scheduling a session in the middle of after-school pickup time.
Healthcare practitioners are needed in AI development
The last piece of the puzzle is how AI systems are put into practice. Healthcare leaders must be critical consumers of these flashy new technologies and ask how AI will work for all the patients in their care. AI tools need to fit into existing workflows so providers will actually use them (and continue adding data to the models to make them more accurate). Involving healthcare practitioners and patients in the development of AI tools leads to end products that are much more likely to be successfully put to use and have an impact on care and patient outcomes.
Making AI-driven tools work for everyone shouldn’t just be a priority for marginalized groups. Bad data and inaccurate models hurt all of us. During our WiDS panel, Dr. Yazdany discussed an AI program she developed to predict outcomes for patients with rheumatoid arthritis. The model was originally created using data from a more affluent research and teaching hospital. When they added in data from a local hospital that serves a more diverse patient population, it not only improved the AI’s predictions for marginalized patients — it also made the results more accurate for everyone, including patients at the original hospital.
AI will revolutionize medicine by predicting health problems before they happen and identifying the best treatments customized for our individual needs. It’s essential we put the right foundations in place now to make sure AI-driven healthcare works for everyone.
Dr. Tina Hernandez Boussard is an Associate Professor at Stanford University who works in biomedical informatics and the use of AI technology in healthcare. Many of the perspectives in this article came from her panel at this year’s Women in Data Science (WiDS) annual conference.
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Web3 and the transition toward true digital ownership
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How do you think you would answer if I asked you the following question: “What do you own online?”
In real life, you own your home, the car you drive, the watch you wear, and anything else you have purchased. But do you own your email address or your business’s website? How about the pictures that populate your Instagram account? Or the in-game purchases on Fortnite or FIFA video games or whatever else you are playing?
My best guess is, after casting your mind through the things you use the internet for (which for everybody is pretty much everything, social and professional), you would struggle to find a solid answer.
Maybe you would ask me to explain what I mean by “ownership.” But it doesn’t really matter. And while I don’t mean this to be a trick question, it kind of is. Because in the current version of the internet, we don’t have ownership rights online.
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Digital ownership: Participants and products
To understand why we don’t own anything online, we must first understand the evolution of the internet and how it gave rise to the business model that has dominated its current iteration.
In the 1990s — the decade of desktop computers and dial-up connections — the internet was predominantly a content delivery network consisting of simple static websites showcasing information. What we refer to today as Web1 was slow, siloed, and disorganized.
Next came the platforms, such as Facebook (now Meta) and Google, driven by wireless connectivity and the development of handheld devices like laptops, smartphones, and tablets, which gave us free-to-use services that enabled us to edit, interact with and generate content. These platforms centralized the web, putting in place a top-down structure that saw users reliant on their systems and services.
This evolution of the internet took place in the mid-2000s and is the version we know today. We call it Web2. It is a model based on connectivity and user-generated content, made in the image and interests of companies like Facebook, Twitter, Instagram, and YouTube.
In this environment, netizens are both participants and products. We sign up for services in exchange for our data, which is sold to advertisers, and we create content that generates value and fuels engagement for these platforms. We do all this while having no rights to anything online.
Our social media profiles can be taken down and our access to email accounts or messenger apps suspended. We don’t own any of the digital assets we purchase and have no autonomy over our data. Businesses we build online are often reliant on platforms and are therefore vulnerable to algorithms, data breaches and shadow bans.
The deck is stacked against us. Because the option not to be involved, when so much of the commerce and communication in the world takes place online, is not really an option at all. And yet there is nothing that we can point to and call ours. Nothing we have any actual authority over.
And, it is this dynamic that Web3 is determined to change.
Web3 and the “internet of value”
Right now, when most people hear the term “Web3” they probably think “metaverse”. But a better way to think about Web3 is as the evolution of the internet.
Today, the digital experience is very corporate and very centralized. Web3 will offer the dynamic, app-driven user experience of the current mobile web in a decentralized model, shifting the power from big tech back to the users. It will do this by spreading the data outward — putting it back in the hands of netizens who are then free to use, share and monetize it as they see fit — and expanding the scale and scope of interactions between users and the internet.
Underpinning that expansion will be guaranteed access, which means anyone can use any service without permissions and no one can block, restrict or remove any user’s access.
The idea then is that Web3 will not only be more egalitarian but that it will create an “Internet of Value” because the value generated by the web will be shared much more equitably between users, companies, and services, with much better interoperability. Users will have full ownership, authority, and control over both the content they create and their data. But how will this help us transition toward true digital ownership?
NFTs hold the key to digital ownership
The truth is that digital ownership is not too hard a problem to solve. And we already have the solution: NFTs.
In the public consciousness, NFTs are known for the projects that have garnered the most media attention, such as CryptoPunks and Bored Ape Yacht Club. While projects such as these have catapulted the term into the zeitgeist, the usefulness of the underlying technology has been much less discussed.
Simply put, NFTs act as proof of ownership. The details of the NFT’s holder are recorded on the blockchain, all transactions and transfers are tracked and transparent and available to the public, and everything is managed by the token’s unique ID and metadata.
So, how does this work in practice? Let’s say I create an NFT. As soon as I upload it, a “smart contract” is created that tracks its creation, the current owner, and the royalties I will receive. If someone decides to purchase it, they own that NFT and any additional perks that come with ownership. Their details are registered on the blockchain and nobody can edit or remove them.
Now, let’s say that the market for my NFTs starts to heat up, demand grows and the value of my collection begins to rise. If the owner decides to sell, they make a profit and I earn a small royalty from the resale. The change in ownership is tracked on-chain in real-time and the smart contract ensures my royalty fee is deposited directly in my wallet. This is the key value proposition of NFTs: Verifiable ownership and the option to liquidate digital assets.
What’s next for Web3?
This is what ownership looks like in Web3. It is the promise that netizens will be able to own their digital assets in the same way that they own their home, car and watch. NFTs will usher in a more equitable digital economy and will play a central role in the future of digital commerce.
The fact is that as of right now, we are still writing the Web3 rulebook. This is still a very new, very young space. And while few things are certain, what we can say for sure is that the internet is only moving in one direction: ownership.
The guiding principle in Web3 is to accelerate the transition towards a more equitable digital environment. It is very much opt-in, an internet built by the people for the people. It is one in which ownership is the foundation upon which new products, networks, and experiences are being built. And it is fundamental to establishing the internet of value.
Over the next few years, as Web3 develops it will operate alongside Web2. The infrastructure supporting Web2 is very strong and I don’t see us completely shifting away from that any time soon. However, in the medium-to long-term, Web3 will completely reshape our relationship with the internet.
Filip Martinsson is cofounder and chief operating officer of Moralis.
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Tech
Apple blocked the latest Telegram update over a new animated emoji set
Ever since Apple launched the App Store, developers big and small have gotten caught up in the company’s approval process and had their apps delayed or removed altogether. The popular messaging app Telegram is just the latest, according to the company’s CEO Pavel Durov. On August 10th, Durov posted a message to his Telegram channel saying the app’s latest update had been stuck in Apple’s review process for two weeks without any real word from the company about why it was held up.
As noted by The Verge, the update was finally released yesterday, and Durov again took to Telegram to discuss what happened. The CEO says that Apple told Telegram that it would have to remove a new feature called Telemoji, which Durov described as “higher quality vector-animated versions of the standard emoji.” He included a preview of what they would look like in his post — they’re similar to the basic emoji set Apple uses, but with some pretty delightful animations that certainly could help make messaging a little more expressive.
“This is a puzzling move on Apple’s behalf, because Telemoji would have brought an entire new dimension to its static low-resolution emoji and would have significantly enriched their ecosystem,” Durov wrote in his post. It’s not entirely clear how this feature would enrich Apple’s overall ecosystem, but it still seems like quite the puzzling thing for Apple to get caught up over, especially since Telegram already has a host of emoji and sticker options that go far beyond the default set found in iOS. Indeed, Durov noted that there are more than 10 new emoji packs in the latest Telegram update, and said the company will take the time to make Telemoji “even more unique and recognizable.”
There are still a lot of emoji-related improvements in the latest Telegram update, though. The company says it is launching an “open emoji platform” where anyone can upload their own set of emoji that people who pay for Telegram’s premium service can use. If you’re not a premium user, you’ll still be able to see the customized emoji and test using them in “saved messages” like reminders and notes in the app. The custom emoji can be interactive as well — if you tap on them, you’ll get a full-screen animated reaction.
To make it easier to access all this, the sticker, GIF and emoji panel has been redesigned, with tabs for each of those reaction categories. This makes the iOS keyboard match up with the Android app as well as the web version of Telegram. There are also new privacy settings that let you control who can send you video and voice messages: everyone, contacts or no one. Telegram notes that, like its other privacy settings, you can set “exceptions” so that specific groups or people can “always” or “never” send you voice or video messages. The new update — sans Telemoji — is available now.
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