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AI is impacting all corners of healthcare, not least the clinical workforce
New research from HIMSS Market Insights offers perspectives from clinical leaders worldwide. It finds enthusiasm for AI innovation, ongoing challenges around trusted adoption and cautious optimism on boosting productivity and reducing burnout.
Google Cloud, DeepMind, and the EU: A New Model for AI-Driven Science
Google's latest European research initiative signals a shift toward collaborative AI innovation, where technology developers provide the tools and scientific institutions establish the priorities for discovery.
Innovation That Solves: From Business Needs to AI Solutions
Targeted AI innovation transforms business challenges like hydrocarbon compliance into effective technological solutions, writes Jorge Mandujano.
Mount Sinai and Hasso Plattner Institute Launch International Graduate Student Exchange Program to Advance Digital Health and AI Innovation
The Icahn School of Medicine at Mount Sinai and the Hasso Plattner Institute for Digital Engineering (HPI) are expanding their academic collaboration in digital health through the launch of a new graduate student exchange program offered through Mount Sinai’s Graduate School of Biomedical Sciences.
Health, AI Innovations Prompt Longevity Concerns
TIAA study examines the retirement implications of tech including AI and weight-loss drugs, and the need for lifetime income
Illinois State University partnership advances AI innovation in healthcare
Through a partnership with Healthy Tray, Illinois State University faculty and student researchers are helping transform how hospitals measure and manage patient nutrition.
Jian Li and Akshat Dave Among Faculty Awarded Grants From AI Innovation Institute
Ten interdisciplinary projects have received funds from Stony Brook University’s AI Small Grant Program. Selected from a pool of 63 applications, these research initiatives will advance integrated AI innovation and strategic diffusion of AI technology across Stony Brook’s campus, New York State and the world. Lav Varshney, Della Pietra Infinity Professor and inaugural director of
Oracle Academy champions responsible AI innovation at Young Scientists Kenya 9th National Exhibition
Oracle Academy champions responsible AI innovation at Young Scientists Kenya 9th National Exhibition
Anthropic's Labs team drives AI innovation as IPO nears
Anthropic Labs built some of the company's biggest hits, like Claude Code. Cofounder Ben Mann lays out his team's efforts and changing ambitions.
Microsoft's latest AI rebrand beats its own history of terrible rebrands, renaming Microsoft 365 Roadmap to "AI at Work"
If you use Microsoft 365 and want to know what’s coming next, what would you search for on Google/Bing? Microsoft 365 Roadmap, right? Wrong. Microsoft now wants you to search for “AI at Work Roadmap,” as it “centralizes AI innovations” across products like Word, Excel, PowerPoint, or Outlook. I use Microsoft 365 products all the […]
St. Louis-based geospatial nonprofit launches AI innovation center with support from major tech firms
The local initiative is backed by some of the biggest companies in the rapidly-growing AI industry.
The Science Behind the AI Panic Is Shakier Than You Think
A column by Sascha Lobo Is AI making us dumb? So asked DER SPIEGEL on its cover a while ago. After a few weeks of recovery and a therapeutic intervention with the help of my AI, I feel able to respond. That’s a bit unfair, of course; the article itself is more nuanced than the loud cover, the image of a schoolkid with dull prompt questions, or the subtitle: “AI is conquering the schools, and educators fear a disaster.” The short answer to “Is AI making us dumb?” is: no. The long answer is more interesting, and even carries a preliminary glimmer of the spectacular. Unfortunately, it requires a detour into the sphere of artificial intelligence, into the mogul-field terrain of technology assessment, the attention economy, and the destructive virus of cultural pessimism. Along with narrowing the scope to AI’s effects on education and work rather than on intelligence. Because the honest version of the question would be: Does AI help us learn and work, or not? Generative AI, which set off the still-ongoing hype with ChatGPT, has only existed in relevant distribution since late 2022, early 2023. For a technology-driven transformation of people and society, that is extremely short. For that reason alone, research is still in its infancy. And technology assessment is not a fast discipline anyway. Prominent voices from Elizabeth Eisenstein to RAND to Tom Wheeler say we still cannot conclusively judge the consequences of the printing press. Its invention is a mere 580 years back, and some scholars would rather not circulate assessments that might have to be retracted in two or three hundred years. We’re Going Through Growing Pains A phenomenon that already caused upheaval in social media research makes serious assessment of AI’s consequences even harder: the “moving target” problem of LLM-ology, as the linguist Sean Trott called it in 2025. AI develops so quickly and so unpredictably that researchers often end up studying snapshots of the past. There are concepts, from morphological analysis to anticipatory governance, for keeping research, planning, and regulation halfway meaningful anyway. But even with those, universally valid statements are rare. DER SPIEGEL - The German View is a reader-supported publication. To receive new posts and support our work, consider becoming a free or paid subscriber. Subscribe We should therefore treat findings about the interplay between humans and AI as far more procedural and situational. Unfortunately, this challenge comes at the worst possible time. We are going through growing pains: AI’s everyday impact has grown very large very fast, and with it the longing for guidance. Annoyingly, the body of solid knowledge has not exploded along with it. Experts cannot be moved to broad consensus on even banal statements about AI. Sascha Lobo, born in 1975, is an author and strategy consultant focusing on the internet and digital technologies. Together with Jule Lobo, he explores the debates of the day in the podcast “Feel the News – Was Deutschland bewegt” (”What Moves Germany”). Nvidia CEO Jensen Huang says software developers are dying out. Bill Gates considers coders one of only three professions that will survive the AI tsunami. Top tech economists find virtually irrefutable arguments both for and against an AI bubble. The same naturally applies to the structurally youth-hostile debate over whether AI is good or bad for young people. The gap between the demand for insight and the supply of solid knowledge creates an orientation vacuum. This vacuum of AI cluelessness is filled, as the attention economy described by Georg Franck in 1998 would have it, with half-knowledge (funnily enough, often AI-generated), marketing, and self-marketing. After all, infinite money is at stake, along with academia’s best current shot at global fame. But this iridescent froth of interpretation operates more by the algorithmic principles of the digital public sphere than by seriously earned insight. That is why dramatized, fear-feeding claims about AI are so promising. It tempts parts of academia into distorted drivel, and parts of the media then sensationalize the published drivel once more. Was the Research Aimed at a Desired Result? How badly this can influence academia and public perception is demonstrated by one of the most-discussed studies on AI’s effects: “Your Brain on ChatGPT.” The MIT study by Nataliya Kosmyna also gets plenty of space in the SPIEGEL cover story. What was measured and interpreted: lower brain activity in people who write with AI assistance, compared to people who only google or write without any tools. As is common with preprints, the study has not yet been peer-reviewed, and it contains substantial weaknesses that experts have sharply criticized. The first round had 54 participants in three groups, the second only 18, who reportedly also came from MIT’s own orbit. There was no preregistration (committing in advance to what you want to find out with which data). In their analysis, Stanković et al. even discovered an opposite effect that the study does not further explain. Some measurement data has not (yet) been published. And the EEG analysis method used is considered too imprecise to support such far-reaching conclusions. The biggest criticism, though, is the suspicion of an activism-driven study. In an interview with Time, lead author Kosmyna said she published the study immediately, rather than after full peer review, because she feared politicians might otherwise decide to put ChatGPT in kindergartens. She considers that absolutely harmful. It is legitimate to hold such a view. It just seems unscientific to approach a study with it, and then to design the setup so that the chance of a fitting desired result remains as high as possible. Ideologies Collide It is not easy to separate genuine AI insights from studies that trivialize and weaken well-founded AI criticism. The same goes for the opposing, AI-enthusiastic narratives, because the pro-AI side works against it with billions in resources. Ideologies collide, fed by egos, PR strategies, moral posturing, and once-in-a-lifetime opportunities. Doomsday marketing makes the search even harder: AI companies can profit from warnings about the dangers of AI, whether through the notoriety of apocalypse narratives or because, in some target groups, danger translates into power. Some seemingly negative findings are therefore quite welcome in the AI industry. If you try to get at the findings presumably less influenced by interest groups, the picture in education seems relatively clear: AI can improve educational outcomes when the right AI is used the right way, namely pedagogically. Simply dumping an AI chatbot into a classroom, by contrast, accomplishes little. Huge surprise. But something else is also obvious: whether they are allowed to or not, kids use AI for school, which is why the controlled integration of AI tools into education is, in the medium term, entirely without alternative. In the world of work, the landscape of findings is more complex and requires more differentiation. A series of serious studies points to an AI effect that suits neither the alarmists, regulation fetishists, and AI opponents, nor the trillion-dollar corporations with their armada of paid scientists, PR professionals, and ecosystems: working with AI helps the inexperienced (at least for now) considerably more than it helps the pros, in certain fields of work, and provided they know how to use it correctly. Missing Domain Knowledge Among the experienced, the professionals, and the high flyers, by contrast, despite frequent time savings, partially negative effects and new problems are emerging. Take the jagged frontier problem: AI solves task A brilliantly and fails completely at task B, even though both look similar even to professionals. Or the verification-cost problem, in which AI’s greater productivity gets eaten up by the enormous effort of checking its output. Or the illusion of competence, because AI makes new domains seem easily penetrable, until you painfully discover that deep domain knowledge is missing. Taken as a whole, the effects of working with AI do lean positive. But the promise that AI works as a turbocharger for everyone and boosts practically everything simply refuses to materialize. The AI magic the world has been feeling since ChatGPT arrived in late 2022 has so far barely shown up in most companies’ numbers. The consulting firm McKinsey has coined a term for this, the “gen AI paradox,” and believes that work processes and corporate structures would have to be rebuilt quite fundamentally to realize AI’s benefits at scale. Reuters just reported that only 3.3 percent of Microsoft’s customers have subscribed to the AI package Copilot. That may be down to the product; in other areas we continue to see soaring successes and new AI triumphs. But after the first quarter of 2026, disillusionment is the word for all too many AI projects at traditional companies: because the promised revolution of AI agents has so far failed to arrive or been postponed, and because of the leveling effect that many of the studies cited here suggest. Against Inequality The societal effects, on the other hand, appear rather encouraging. Many previous technologies operated on the Matthew principle, named for the Gospel verse about giving to those who already have: they tended to reward the more competent and more privileged. AI, however, seems more often to have an equalizing effect once a threshold of access and education is crossed. Some anecdotal insights point in this direction, for example, how an unemployed man in Leipzig successfully defended himself in court in 2025 against fraud accusations from Germany’s federal employment agency — without a lawyer, armed only with ChatGPT. The AI made mistakes and cited invented court rulings, but in the end the man walked away unpunished. And a key capability of artificial intelligence is its capacity to learn: set up correctly and fed with data, it can keep getting better. It is a question of time, or more precisely of AI innovations and training data, until even free AI chatbots write better legal briefs than law graduates with perfect exam scores. Whether that will make anyone dumb is something the world’s press will surely inform us of in due time. Subscribe Leave a comment
Recent Coverage Highlights AI Innovation, Rural Health Challenges and Cyclospora Response
A July 21 Detroit News story explored how health systems across the state are using artificial intelligence to improve diagnosis and treatment.
Ohio State researchers to lead national teams exploring science, AI innovation
The Ohio State University continues to build momentum as a national leader in artificial intelligence research and innovation. This week, two teams led by Ohio State researchers joined a $293 million initiative sponsored by the U.S. Department of Energy (DOE) to harness AI for breakthroughs in energy dominance, discovery science and national sec...
Maid Brigade Leadership Summit Highlights AI Innovation, Business Growth and Franchise Excellence
Maid Brigade franchise owners from across North America gathered July 16-18 at The Source Hotel in Denver for the 2026 Maid Brigade Leadership Summit, where they explored innovative business strategies, shared best practices and celebrated franchise excellence.
California doesn’t need to put an ‘Out of Business’ sign on AI innovation
California should be leading the AI revolution, not regulating itself onto the sidelines.
Why healthcare CIOs must fix their foundations before scaling AI
Healthcare leaders are focused on generative AI innovation, but the organizations most likely to succeed may be those investing first in architecture, governance and interoperability.
MICROIP to Debut "AI Vehicle System Business Group" and Edge AI Innovations at COMPUTEX 2026
/PRNewswire/ -- MICROIP (TPEx: 7796), specializing in AI software and ASIC design services, will unveil its "AI Vehicle System Business Group" at COMPUTEX 2026...
Tech-Ignored: How AI Innovation Is Widening The Digital Health Divide
AI in healthcare is booming but the digital health divide is widening. How underserved communities are being tech-ignored.
Entertainment Thought Leader: Keynote Speaker Scott Steinberg
Entertainment thought leader, futurist keynote speaker and consulting expert Scott Steinberg. AI innovation change leadership TV movies music games books apps - 3000 brands served!
AI can open up possibilities for strained rural health care providers in Utah
A St. George conference about how technology can help rural health care providers allowed for connections that will help bring AI innovation to busy offices.
'The Adding Machine' review: Elmer Rice's play gets a sluggish revival
Elmer Rice's "The Adding Machine," an expressionist classic on automation and worker insecurity, speaks directly to an age in which livelihoods are threatened by AI innovation.
Alibaba CEO takes helm of new AI-focused business group
Alibaba Group Chief Executive Eddie Wu will head the newly formed Alibaba Token Hub (ATH) Business Group, the firm said in a statement on Monday. The new group will comprise existing Alibaba units Tongyi Laboratory, MaaS Business Line, Qwen Business Unit, Wukong Business Unit, and AI Innovation Business Unit. Alibaba said its mission would be to "create, deliver, and apply tokens," and it would focus on building AI-native work platforms for enterprises.
Accelerating AI innovation in healthcare: real-world clinical research applications on the Mayo Clinic Platform
npj Health Systems - Accelerating AI innovation in healthcare: real-world clinical research applications on the Mayo Clinic Platform
Report: Apple's AI Strategy Could Finally Pay Off in 2026
Apple's restrained artificial intelligence strategy may pay off in 2026 amid the arrival of a revamped Siri and concerns around the AI market "bubble" bursting, The Information argues. The speculative report notes that Apple has taken a restrained approach with AI innovations compared with peers such as OpenAI, Google, and Meta, which are investing hundreds of billions of dollars in data centers, chips, and large language model training.