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Every day, we hear about new algorithms, groundbreaking analyses, and the potential for AI to revolutionize patient care. Yet, for many physicians and their teams on the front lines, AI can feel like another layer of complexity, another screen to navigate, or another barrier between them and their patients. The reality is, most doctors don’t compete with each other; they fight to survive under the weight of the healthcare system’s administrative demands. Providers face burnout from outdated processes, complex government regulations, and the confusing world of insurance payers. While many AI tools promise relief, too often they create new points of frictioninterfaces to learn, workflows to manage, or alerts to respond toadding rather than removing complexity. This disconnect between AI’s promise and its practical application highlights a fundamental flaw. It’s time for a different approach. For AI to be truly impactful for doctors, it needs to recede into the background. Specifically in a clinical setting, AI should be present but not visible, perceptive but not intrusive, and powerfully helpful without demanding attention or detracting from time with patients. Unfortunately, contrary to their intent, many of today’s AI tools can actually increase workloads rather than decrease them. Engineering AI to Adapt to Providers In healthcare, we’re emerging from an era where technology has played too prominent a role in the patient-provider interaction. Hardware interfaces, supporting digital charting, coding, and billing, consumed valuable energy that should have been dedicated to patient care. While electronic health records (EHRs) initially promised efficiency by moving providers away from paper charts, the demands and growing complexity of documentation and reporting requirements quickly outpaced these EHRs’ capabilities. This led to a new layer of burnout for providers and a less personal experience for patients. More recently, the rapid deployment of AI ambient listening solutions, though well-intentioned and helpful with transcription, can produce unintended consequences. Providers sometimes find themselves correcting the mistakes of poorly trained AIs or spending hours after clinic responding to messages, reviewing alerts or actioning downstream steps needed to complete the patient visit, adding to cognitive overload and burnout. These early one-size-fits-all AI tools often feel like half measures, designed in silos or by technology teams that have failed to truly grasp the holistic challenges doctors face with their time and efficiency. Every minute spent troubleshooting technology, correcting errors, or navigating a clumsy interface is a minute taken away from precious eye contact, active listening, and the invaluable opportunity to offer true empathy and build connection with patients. Of course, new technology always requires users to learn new ways of doing things, but instead of asking physicians to adapt to AI, true adoption depends on designing AI to adapt to them. In the context of the doctor’s office, this means: By Doctors, For Doctors: For AI to truly recede into the background and become a reliable partner, its precision cannot be overstated. This level of contextual understanding of the doctors world is not an inherent feature of generic algorithms; rather, it needs to be painstakingly forged with AI models rigorously trained with extensive input from doctors and vast, de-identified real-world clinical data. Deep Medical Intelligence: Unlike generic AI, systems should be grounded in vast, medical language and structured data that is medically unique to the needs of each doctor and their area of expertise. This allows AI to understand the subtle language of various specialties like dermatology or ophthalmology, delivering insights that match the needs and rhythms of each clinical domain. Seamless Workflow Integration: There should be no new disruptions. AI should enhance existing workflows, reducing clicks and administrative burdens without forcing radical changes. For example, ambient listening technology can capture clinical conversations in real time, seamlessly, safely, and without disrupting the flow of discussion between doctor and patient. Augmenting, Not Replacing: The commitment must be to responsible AI. Tools should offer intelligent suggestions, surface crucial information, and automate repetitive tasks, always ensuring the physician maintains control and clinical judgment. In this model, AI acts not as a replacement, but as a silent partnera trusted copilot, bolstering clinical expertise without overshadowing it. Building Trust: Trust in AI doesn’t come from splashy featuresit’s earned through consistency, safety, and clarity. Systems should surface when they’re helpful, and step back when they’re not. When AI respects clinical boundaries, avoids false alarms, and delivers reliable results, providers learn to trust it as part of the care team, not a replacement for it. Quiet transformation The future of healthcare AI isn’t about shouting from the rooftops about technological prowess. It’s about the quiet, profound transformation that occurs when technology enhances care without announcing itself. It’s about technology acting as a catalyst for deeper human connection, helping doctors to be fully doctors again and patients to feel truly heard and cared for. The goal is clear: build AI that becomes woven into the fabric of the practice, thoughtfully amplifying clinical excellence and fostering unparalleled patient experiences. When we design technology that respects the time, intelligence, and humanity of providers, we allow the patient-provider connection to shine. This is the next era of healthcare, defined not by what AI can do but by how effortlessly it helps providers do what they do best.
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Hello once again and thanks for spending time with Fast Companys Plugged In. Fifteen years into the era defined by the iPhone, a question still looms over the consumer tech industry: Whats the next great personal computing device after the smartphone? The brave new ideas keep on coming. A few have succeededbut as phone accessories, not replacements. Others remain works in progress. Some have already failed in spectacular fashion. But one emerging device category has been easy to gloss over: the folding phone. No, it doesnt represent a bold gambit to kill the smartphone. It is a smartphone! However, by squeezing the equivalent of a tablet into pocketable form, it also departs radically from the Hershey Bar-shaped touchscreen gadget that Apple pioneered and the rest of the industry cloned as fast as it could. After six years on the market, foldable smartphones reportedly account for only 1.5% of smartphones sold. At least in part, thats because theyve had a whole flock of albatrosses around their neck. Figuring out how to design ones that are inviting to use when theyre folded upand twice as thick as when unfoldedhas been a challenge. Theyve often been outfitted with less-than-stellar cameras and other sub-flagship components. Hinge robustness and general durability have been open to questions. And all of thats before you get to their price tags, which started at $1,980 with Samsungs original Galaxy Fold and hover in the same imposing vicinity today. Recently, though, foldables seem to be hitting their stride. My colleague Jared Newman was wowed by Samsungs latest model, the Galaxy Z Fold7. Googles Pixel 10 Pro Fold, shipping next month, is the first foldable to achieve IP68 water and dust resistance. Bloombergs Mark Gurman says the first foldable iPhone is a go for 2026evidence that Apple believes the category is real rather than a fad to be ignored. The product of seven generations of evolution, Samsungs Galaxy Z Fold7 has overcome many of the early folding Galaxy phones limitations. [Photo: Courtesy of Samsung] And then theres Honors Magic V5, the newest foldable from one of Chinas major phone makers. Though not for sale in the U.S., it recently debuted in Europe, claiming bragging rights as the worlds thinnest folding phone. Thats only so heady an achievement: It applies to only one color variant (Ivory White), involves beating the Fold7 by just .1 millimeter, and doesnt count the bulbous camera bump, which protrudes from the back of the V5 like a spare tire mounted on a Jeep. Still, after spending a week or so with a review unit provided by Honor, Im impressed. The price remains steep: In Europe, the phone sells for 1,999 euros (about $2,300). But Honor has overcome many design challenges once inherent to the foldable category. It also has a competitive advantage in its use of silicon carbide battery technology, which allows it to pack more battery density into the phones slender frameshort running time being yet another hobgoblin faced by past foldables. In my experience, even using the phone with abandon over the course of a day didnt drain its battery to anywhere near zero. Closed, the Magic V5 is a very pleasant Android phone. It runs Honors MagicOS 9, a reskinned version of Android 15 bearing so close a resemblance to iOS that my fingers forgot they werent maneuvering around an iPhone. I thought the brown vegan leather back on the review unit would come off as tacky, but its aesthetics and comfort quickly won me over. Even sheathed in the bundled casealso in brown vegan leatherthe phone doesnt feel any bulkier in my hand than my own cased iPhone 16 Pro. I was ready to turn my nose up at the Magic V5s brown vegan leather back, but ended up liking it. [Photo: Harry McCracken] I was also prepared for the Magic V5s cameras to be letdowns, like those on some of the foldables Ive tried in the past. Instead, the snapshots I took with its rear-facing camerasultrawide, wide, and 3X telephonewere in the same image-quality ballpark as those from my iPhone. Unfolded, the Magic V5 really does feel more like a tablet than a phone. The 7.9-inch screen is smaller than an iPad Mini, but its a boon to any task that feels cramped on a garden-variety phone, from watching movies to reading books to wrangling spreadsheets. Honors multitasking interface makes it easy to have multiple apps on-screen at once: side by side or with some floating in windows. The phone also includes a profusion of bundled Honor appssuch as a caledar, notetaker, and photo gallerythat smartly fill all that real estate with sidebars and other elements that wouldnt fit on a standard phone screen. Many of the third-party apps I tried did leave me vaguely unsatisfied, though. Only rarely did I detect evidence they were aware they were operating on a larger, folding screen. Way back when Android tablets were first a thing, I groused that they ran smartphone apps that had been merely scaled up rather than reimagined. Today, folding phones suffer from a similar failure by developers to seize the opportunity before themmaybe because their user base remains dinky. Which brings us to the folding iPhone that will theoretically arrive next year. According to the rumor mill, that phone may reflect Apples efforts to address lingering folding-phone downsides, such as the obviousness of the crease down the middle of the unfolded screen. (It was readily apparent on the Magic V5, though I didnt find it particularly aggravating even if I forced myself to obsess over it.) Apple could also bring some fresh thinking to the software side: Oddly enough, Split Screen and SlideOverthe iPad multitasking features abandoned in iPadOS 26would serve as a solid foundation for a folding iPhone interface. As for third-party software, anyone whos already building both iPhone and iPad versions of an app has a head start on tailoring experiences to a variety of screen sizes. That might give a folding iPhone an edge on Android foldables, particularly if developers sense that the device is a hit and its users represent a growing percentage of their customers, even if it starts small. Whether Ill end up owning a folding phone myself, Im still not sure. But seeing phone makers get serious about them has put me in piqued-interest mode. Thats progress in itself. Its fun to wonder where foldables might be by the time Im ready to talk myself into splurging on one. Youve been reading Plugged In, Fast Companys weekly tech newsletter from me, global technology editor Harry McCracken. If a friend or colleague forwarded this edition to youor if you’re reading it on FastCompany.comyou can check out previous issues and sign up to get it yourself every Friday morning. I love hearing from you: Ping me at hmccracken@fastcompany.com with your feedback and ideas for future newsletters. I’m also on Bluesky, Mastodon, and Threads, and you can follow Plugged In on Flipboard. More top tech stories from Fast Company Your phones Share button doesn’t get enough loveBeyond just sharing links and photos with other people, it serves as a hub for all kinds of helpful shortcuts. Read More Perplexity’s new AI bet: Monetize the bots, pay the publishersPerplexity’s new revenue model aims to pay publishers not just for page viewsbut for what its AI bots do with their content. Read More Kids aren’t reading for pleasureand more than tech is to blameDoomscrolling. Poor literacy instruction. Overscheduling. These are some of the reasons cited for a generational decline in students reading for fun. Read More The cortisol cocktail is blowing up on TikTok. Does it really work?TikTok users swear the nonalcoholic drink can lower stress hormones. Experts say the science isn’t nearly as clear. Read More Crypto.com bets big on sports prediction marketsIn partnership with fantasy-sports app Underdog, the company is bringing prediction markets to 16 states, bypassing legal-betting roadblocks in places like California and Texas. Read More Starbucks’s new AI could save its baristas 16,500 hours a weekA new inventory system will save Starbucks a ridiculous amount of time counting pumpkin spice at scale. Read More
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TikTok gave us slang like rizz, while X popularized ratio and doomscroll. But according to new research from Florida State University, the newest force shaping language isnt a person or platform: Its artificial intelligence. In a peer-reviewed study published in the Cornell University archive arXiv, FSU researchers found that AI is influencing not just how we write, but how we speak. After analyzing more than 22 million words from unscripted podcasts, the team observed a surge in terms favored by large language models (LLMs) like OpenAI’s ChatGPT (delve, boast, meticulous, and garner to name a few), while use of their synonyms remained relatively flat. The researchers call this the seep-in effect or lexical seepage. Unlike slang spread by subcultures or mass media, this shift originates with an algorithm. In cognitive psychology, this is known as implicit learning, where recurring phrasing and word choices are unconsciously stored in memory. Likewise, language research also highlights a phenomenon known as priming, where exposure to specific words or syntax leads to an increased likelihood of using them later. In just a few years, the chatbots preferred vocabulary has moved off-screen and into daily conversation. AI may literally be putting words into our mouths, as repeated exposure leads people to internalize and reuse buzzwords they might not have chosen naturally, says Tom Juzek, a computational linguistics professor at FSU and lead author of the study. The deeper concern is that the very same mechanism could shape not just vocabulary but also beliefs and values. The study teamJuzek, Bryce Anderson, and Riley Galpinanalyzed 1,326 episodes of tech and science podcasts, split evenly between a pre-ChatGPT period (2019 to 2021) and a post-ChatGPT period (2023 to 2025). They drew on transcripts where possible, or generated them with OpenAIs Whisper model, resulting in a dataset of about 22 million words. They then compared per-million usage rates of AI-associated buzzwords against close synonyms to test whether shifts reflected ordinary drift or a distinct AI-style influence. It was important that this was unscripted language, so we focused on conversational showsLex Fridman, Radiolab, Ologiesto capture something close to spontaneous speech, Juzek says. We explicitly excluded sources such as conference talks or lectures, which are often scripted and may even be AI-assisted. He explains that LLMs dont inherently overuse buzzwords during pretraining on massive datasets. The effect arises later, during human preference learning. From what we know, raters tend to be young, so ideas about what counts as formal writing may vary, says Juzek. AI model fine-tuning involves tricky trade-offs to achieve usefulness, truthfulness/grounding, and getting high-quality preference data is expensive and hard to obtain. Humans often reward style over substance, so models may pick up polished buzzwords in the process. A similar study in Germany found near-identical patterns on YouTube, suggesting the phenomenon extends beyond American podcasts to other languages and contexts. Is AI Standardizing Human Speech? The implications reach beyond word choice. If OpenAI, Anthropic, or Google fine-tune their models differently, populations could adopt subtly distinct speech patterns. Experts warn this could flatten dialects, erase regional slang, and dampen creativity. While AI does reflect patterns already present, by amplifying and projecting the highest-value version of those patterns learned from millions of interactions, it dramatically shifts the balance of which language forms dominate, says Moti Moravia, cofounder and CTO of Leo AI. Even though you can set parameters for diversity, the main goal of AI models is to maximize perceived quality. While speech patterns have always evolved, today the shift is happening with unprecedented speed. AI Models like ChatGPT, Bard, and Claude are trained on billions of words through web scraping and are used by millions of people almost every day. If algorithms quietly prune our synonym choices, they could also be narrowing how we frame ideas. AI systems tend to magnify dominant language patterns, which speeds up their adoption in broader culture. Without continued human input, they could stagnate, replaying the past instead of adapting to the present. The result might be a creative landscape that feels out of sync with realityunless new frameworks are developed to prioritize originality. This is a terrifying future, but we still have time to change this and build in frameworks so that original human creativity is still rewarded, says Trip Adler, cofounder and CEO of Created by Humans. Likewise, Moravia argues that companies like OpenAI, Anthropic, and Google will continue to chase higher benchmarks by training on the best data available, optimizing for the metrics they know how to measure. The safeguard, he suggests, is to establish a new benchmarkone that explicitly values diversity in language and beyond. Companies should be incentivized to optimize for more varied outputs in the way models speak. This would be a subtle yet powerful way to encourage AI systems to preserve linguistic diversity rather than unintentionally narrow it. Holding on to the Human Tone Juzek cautions that the rise in certain words doesnt prove AI is the sole driver. Many were already trending before 2022, and AI may simply be accelerating an existing shift. It took years before we understood the full mix of benefits and risks that came with social media, and I suspect it will be similar with AI models, he says. Conversations with colleagues tell me that this small tweaks snowball effect may be inherent to gradient descent, the optimization procedure at the core of how the models learn. Understanding that properly will require more foundational research. Looking ahead, he expects language change to accelerate. Some AI-favored words may fade, much like generational slang, but the larger risk is subtle homogenization. Culturally, this matters for trust and creativity, Juzek says. Sooner or later, that same uncertainty will reach spoken interactions, for example, phone calls. Arguably, face-to-face conversations remain safe for the foreseeable future.
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