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GLP-1 Weight Loss Drugs Linked to Lower Breast Cancer Risk

The rapid rise of GLP-1 receptor agonists has transformed the treatment of obesity and type 2 diabetes. Now, a large observational study suggests these drugs may also play a role in cancer prevention.

Researchers from the Perelman School of Medicine reported that women taking GLP-1 medications were significantly less likely to develop breast cancer than comparable women who did not receive the drugs. The findings, presented at the 2026 American Society of Clinical Oncology Annual Meeting and simultaneously published in JCO Oncology Practice, add to a growing body of evidence linking GLP-1 therapy to reduced cancer risk.

While the study cannot establish causation, investigators say the results provide a strong rationale for prospective clinical trials evaluating whether GLP-1 drugs could become part of future breast cancer prevention strategies.

Beyond weight loss

GLP-1 receptor agonists were originally developed to improve blood glucose control in patients with type 2 diabetes. More recently, agents such as semaglutide and tirzepatide have become widely used for weight management because of their ability to produce substantial and sustained weight loss.

Interest in the drugs has expanded beyond metabolic disease as researchers have begun exploring their broader biological effects. Obesity is a well-established risk factor for postmenopausal breast cancer, and GLP-1 therapies influence several pathways implicated in cancer development, including chronic inflammation, insulin signaling, and metabolic regulation.

“While our study was observational and does not definitively confirm an association between GLP-1 medications and reduced breast cancer incidence, it does add to the growing body of evidence suggesting that it’s worth investigating these weight-loss drugs as potential cancer prevention tools,” said Elizabeth McDonald, MD, PhD, professor of radiology at Penn Medicine and lead investigator of the study.

Lower breast cancer rates across multiple analyses

The researchers analyzed electronic health records from women who underwent breast imaging at Penn Medicine between January 2022 and June 2025.

The primary analysis included nearly 95,000 women between the ages of 45 and 80 who had a body mass index above 25, placing them in the overweight or obesity categories. Approximately 15,000 women had documented exposure to a GLP-1 medication before breast imaging, while nearly 80,000 had no recorded use of the drugs.

Breast cancer was diagnosed in 1.65% of women who had received a GLP-1 prescription compared with 2.6% of women who had not.

After statistical analysis, GLP-1 use was associated with a 37% reduction in the odds of developing breast cancer.

To address potential confounding factors, investigators also performed a propensity-matched analysis pairing GLP-1 users and non-users based on age, race, ethnicity, body mass index, breast density, and diabetes status.

The association remained significant in the matched cohort, where GLP-1 exposure was linked to approximately 25% lower odds of breast cancer diagnosis.

“These findings support the need for prospective trials investigating incretin medications for breast cancer prevention,” the authors concluded in their ASCO abstract.

Why might GLP-1 drugs affect cancer risk?

The biological explanation remains uncertain, but several plausible mechanisms exist.

Weight loss itself is likely an important contributor. Excess adipose tissue promotes inflammation, alters hormone levels, and creates a metabolic environment associated with increased breast cancer risk, particularly after menopause.

However, researchers suspect the benefits may extend beyond weight reduction alone.

GLP-1 receptor agonists have been shown to reduce systemic inflammation, improve insulin sensitivity, and influence metabolic pathways that may contribute to tumor initiation and growth. Emerging laboratory studies have also suggested potential effects on cellular signaling and epigenetic regulation.

“GLP-1 medications are intriguing from a cancer research perspective because they weren’t designed for cancer therapy, but they do affect many different targets and pathways associated with cancer development, so we’re eager to study them in this context,” McDonald said.

A potential new approach to prevention

The findings arrive at a time when breast cancer prevention options remain limited.

For women at very high genetic risk, preventive mastectomy can dramatically reduce cancer incidence but is an irreversible surgical intervention. Anti-estrogen therapies such as tamoxifen can also reduce risk substantially, yet uptake has historically been low because of concerns about side effects.

In contrast, GLP-1 medications are already being used by millions of patients worldwide and have established safety profiles in obesity and diabetes management.

If future prospective studies confirm a protective effect, these agents could potentially represent a new category of pharmacologic prevention for women at elevated breast cancer risk.

Researchers at Penn are already planning a multisite clinical trial that will evaluate whether GLP-1 therapy can reduce breast cancer incidence among high-risk women, including breast cancer survivors.

Important limitations remain

Despite the encouraging findings, the study has several limitations typical of retrospective analyses.

The investigators did not evaluate individual GLP-1 agents, duration of treatment, genetic predisposition, breast cancer subtype, or stage at diagnosis. Residual confounding also remains possible despite matching analyses.

Because the study was observational, it cannot determine whether GLP-1 therapy directly lowers breast cancer risk or whether other factors associated with medication use contributed to the observed differences.

Nevertheless, the consistency of the signal across multiple analyses strengthens the case for prospective investigation.

“Ultimately, we want to find better options to prevent breast cancer,” McDonald said. “It’s been encouraging to see the survival rates for breast cancer improve over recent decades, and we’d love to see the same gains in prevention.”

As GLP-1 therapies continue to reshape obesity treatment, researchers are increasingly asking whether their benefits extend far beyond weight management. This study suggests breast cancer prevention may be one of the most important questions to answer next.

The post GLP-1 Weight Loss Drugs Linked to Lower Breast Cancer Risk appeared first on Inside Precision Medicine.

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Rehumanizing global health care with agentic AI

The global health care sector is under increasing strain. 

Decades of chronic underinvestment and constraints in recruitment have coincided with a surge in demand for services for aging populations. Gaps in provision are already taking a toll, with fragmented access to care and high rates of stress and burnout among staff. And it’s getting worse. The World Health Organization has warned that current shortfalls will increase to 11 million workers by 2030. 

In their urgent hunt for a solution, many health-care providers are now pinning their hopes on agentic AI, with more than two-thirds (68%) having already adopted AI agents into their workforce, according to KPMG. 

The technology is being deployed to automate complex back-office processes, collaborate with medical teams, and even triage patients, all in a bid to reduce the cognitive load on clinicians and improve quality of care for patients as the supply of human health-care workers dwindles.

A different type of digitalization 

Until now, the benefits of digitalization within health care have been limited. 

Many staff have blamed slow or outdated technology for adding to the administrative burden rather than alleviating it. For example, U.S. patient data was migrated to electronic health records (EHRs) in the early 2000s, but this data remains fragmented and reliant on manual inputs. 

New telehealth services and digital care tools, like remote monitors, have had similar shortcomings, says Ashis Barad, MD, chief digital and technology officer at Hospital for Special Surgery (HSS), an academic medical center in New York that focuses on musculoskeletal health. Both technologies have helped improve access to health care by removing geographical barriers, he says, but they’ve failed to replicate the quality of in-person care or win trust from patients. 

Agentic AI is different from these existing technologies, he insists. 

Rather than relying on manual inputs or defaulting to human workers for any case that sits slightly outside a rigid framework, AI agents can handle nuanced, complex scenarios. They can make autonomous decisions, retrieve information from expert clinical sources, and iterate over time, freeing clinicians to focus on higher-level patient care. As Dr. Barad puts it: “Agentic AI takes your workflow and collapses it, augments it, supercharges it, and makes it more performant.” 

At HSS, AI agents have already been deployed in multiple areas. They handle complex backend processes, such as insurance claims that previously took several weeks to complete and involved both HSS staff and a third-party contractor to handle the volume. Now, says Dr. Barad, AI agents complete 1,100 claims per month. They’ve reduced the appeals stage from 45 minutes to five and improved the success rate of those appeals from 65% to 100% in the nine months since implementation. HSS now handles all claims in-house. 

Building on that success, HSS is now deploying AI agents in non-clinical patient-facing settings with an AI scheduling and triage service, as part of a collaboration with enterprise agentic AI developer Ema Unlimited. The service is accessible 24/7 via web, text, or phone. It uses conversational AI to ask patients clarifying questions about their condition and then books appointments with the most appropriate clinician, factoring in location, insurance coverage, and physician availability. “It completes the whole loop,” says Dr. Barad. The AI agent is trained on “all of our context, all of our rules, and all of our knowledge base,” he adds, providing patients with streamlined access to highly specialist knowledge from world-leading surgeons.

Given the high-stakes decisions delegated to AI agents, the triage service has built-in safeguards—sensitive, complex, or uncertain scenarios are escalated to human specialists. Every decision made by the AI agent is auditable and human staff can step in at any point. Patient data is kept secure and the system is trained on all HSS protocols, policies, and care pathways. By keeping humans in the loop, Ema says its technology strikes the balance between efficient automation, patient-first safety, and human-informed decision making. 

As the technology becomes more prolific, it will be incumbent on providers to ensure they have these sorts of guardrails embedded into systems, says Dr. Barad. At HSS all decisions around the technology are filtered through an AI subcommittee that Dr. Barad co-chairs alongside a senior nursing executive. AI agents that may touch on patient care will be scrutinized with far more rigor than, say, backend processes, he explains.

AI agents prompt systems-level change

For example, Dr. Barad has plans to create a dedicated AI lab at the HSS main campus in New York City—a move that aims to democratize access to the technology across the organization. It will be open to all staff looking to understand or build AI agents, he explains, with informative classes and one-on-one training. “We’re getting agentic AI into everybody’s hands,” he says. This echoes research by Deloitte, which found that leading agentic AI adopters in health care were far more likely to have opted for multiagent solutions, redesigning end-to-end workflows rather than sticking to narrow solutions or individual use cases.

The key, it appears, is to integrate AI agents across the entire enterprise, treating them as a general-purpose technology. As Dr. Barad puts it: “It’s wrong to think of agentic AI in use cases… It’s a general-purpose technology, analogous to electricity.”

In practice, this means health-care providers need to set the right foundation to achieve value with agentic AI. This includes creating a unified data strategy, one that integrates fragmented data sources across an organization to create a single, comprehensive source of truth. In health care, data is often split across multiple departments and providers, each with their own legacy IT system.

In systems that rely on fragmented data sources, metrics often lack standardized definitions too. For example, Dr. Barad says that each hospital he’s worked in has had a slightly different definition for “time to start surgery,” a metric commonly used to gauge operating room efficiency. This level of fragmentation impedes AI agents from retrieving information from different sources or applications and assimilating the tacit knowledge that differentiates them from other technologies.

By creating greater interoperability of data at HSS, patient-facing AI agents can draw from a patient’s clinical care history and existing recommendations from their clinician, combine this information with current symptoms, and decide whether a situation requires escalation before notifying the correct specialist and informing the patient. 

Building better outcomes

For Dr. Barad, the potential for AI agents to overhaul health care and alleviate the current pressures on resources, access, and patient care is huge. 

He envisions a future in which 90% of non-clinical health-care tasks could be administered by AI agents, freeing clinicians up for what he calls white-glove work, meaning the most complex, specialized, and sensitive cases.

Most health-care providers seem equally optimistic. According to research by KPMG, 84% of providers are already comfortable handing decision making about specific processes over to AI agents.

“We’re spending so much time on keyboards and computers right now that we’re actually not doing what we should be doing,” says Dr. Barad. “This is going to rehumanize health care.”

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

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How small businesses can leverage AI

This article is from Making AI Work, MIT Technology Review’s limited-run newsletter examining how to apply LLMs across industries. To receive it in your inbox,sign up here.

From accounting to design to market research and product development, there’s a staggering breadth of skills needed to run a business. A large company can hire experts to handle these tasks, but small businesses don’t always have this luxury.

That’s where AI comes in. Today’s AI models do a decent job at these tasks. The trick for small businesses is to understand where AI is good enough and where it’s not.

One place where a “good enough” AI can already be quite valuable to small business owners is in providing secretarial skills and handling basic administrative matters. Let’s take a look at how one private tutor is using it to improve his recordkeeping and free up his time.

Case study

Sam Finnegan-Dehn works in fundraising for a charity, but he moonlights as a math and philosophy tutor for university students from his home in London. Through this part-time business, he can leverage his degrees in philosophy and share his love of the subject with clients.

But meeting with students is only a fraction of the work it takes to be a good tutor. He also plans lessons and finds fresh reading materials, creates assignments, sends invoices, and keeps up with new research—all on top of his regular job. Given these demands, Finnegan-Dehn doesn’t have as much time as he’d like to grow his tutoring roster.

So he’s turned to AI for some help in managing the day-to-day aspects of his business. He says AI has taken on a secretarial role across all of his digital notebooks, where he jots down reminders about his clients’ progress and new readings to keep himself up-to-date. He describes using AI as kind of like having a second memory that helps him connect ideas he’s written down in various places.

While he has experimented with different tools like Claude and ChatGPT, he’s now landed on Notion AI because it integrates better with his tutoring notes, which live across his notebook tabs in the Notion app. Finnegan-Dehn doesn’t use AI to create teaching materials, but he does let Notion AI record meetings with his clients (after getting their consent), and then uses its automated summaries to refine his teaching strategy. For example, if he notices from the AI’s summary that it seems like a certain technique was not helping a student, he may change how he approaches the subject next time.

Beyond this, Notion AI also helps him with goal-setting, drafting lesson notes, invoicing, and generating and syncing social media posts. For goal-setting, for example, Finnegan-Dehn says he understands his long-term goals for his business but not always the concrete steps to build to them. He uses AI to help fill in these gaps. He starts by writing down a “North Star” goal—say, to have a certain number of clients by the end of the year. Next, he asks his AI to generate the steps that he needs to take to get there, given the profile he has built up in the app. Then, he can reflect on the results and choose which tasks to tackle first.

The tool

Notion has been a big player in note-taking software for many years. Its AI add-on, released in late 2023, now has tools that enable it to interact with many other online productivity platforms. There’s an email client, calendar integrations, and a newly released agent. And while this level of access has raised privacy concerns, it can also make for a pretty powerful virtual assistant.

Many of the tasks targeted by Notion AI are less creative and more rote: syncing information across documents or searching through old scribbles, for example. This makes the tool especially appealing to small business owners, who have limited bandwidth, particularly for menial work.

Other companies are developing tools targeted at specific industries. For example, Grandma’s Quilt Shop in Yuma, Arizona, uses Rain, which has a software suite tailored to craft companies, to generate inventory descriptions and pricing for its stock of fabric designs. The owners claim this AI tool cuts the time it takes to list items by 60 to 80%.

There are drawbacks, though, as Finnegan-Dehn described some of Notion AI’s idiosyncrasies as “clunky” at times. And the AI add-on for Notion costs $20 per month. As with all new tools, small business owners should carefully assess how the potential gains and headaches measure up against the cost of just doing the job themselves.

User tips

Consider these points when thinking about whether AI might be able to help you run a business, or make any part of your work life just a little bit easier. 

  • Look before you leap. Since LLMs feed on the data you input to answer your queries or complete tasks, you want to give them information in a way that’s convenient for you and for the model. For many of these notebook AI services, this means, for example, using their platform for notetaking so you don’t have to input or upload notes later. Because of this, it’s a good idea to weigh your options carefully before committing to an AI-powered ecosystem.
  • Work to your strengths. Think about what skills you lack in-house, and see if AI can either help with training or take these tasks on for you. Just be aware: AI hallucinates and makes mistakes, so think about where accuracy is needed and keep humans in charge there.
  • AI isn’t always the best tool. It’s okay to use something off the shelf when that’s the better choice. It’s going to be safer, for example, to use existing payment processing platforms like Shopify or Square than to vibe-code one using AI.
  • Consider using local models for any sensitive information. Our reporting has covered the risks that online AI models have in leaking sensitive data, and there have been many reports about how AI companies collect your data when you ask their chatbots questions. Even if your business doesn’t handle personal information, there can still be some things you’d prefer not to share publicly. In these cases, using an open-source model that makes inferences on your prompts locally can be a great option, instead of ChatGPT or Claude or other proprietary models. Thankfully, some LLMs can now be run off of laptops and small desktops. Here’s how to set one up and start using it.

Sign up for Making AI Work, MIT Technology Review’s limited-run newsletter examining how to apply LLMs across healthcare, climate tech, education, and more.

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Opinion: The virtual end of the doctor’s office waiting room

The word patient comes from the Latin patiens — an adjective meaning enduring, suffering. In medicine, that endurance has long meant waiting: waiting to be seen, to be diagnosed, to be treated. Over the past two decades of practicing emergency medicine, my shifts have begun the same way — walking past a room full of people waiting for care.

That room is not called a lobby or a reception area. It is called a waiting room, because the expectation of waiting is built into the architecture and culture of medicine. Triage — the systematic process of prioritizing patients by the severity of their condition — determines the length of the delay. The sickest are seen first; everyone else bears both their illness and the constraints of the system they have turned to for help.

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STAT+: Online care is caught in the crossfire as states crack down on corporate medicine

In less than a decade, telehealth has expanded from a sideshow of health care to an industry worth tens of billions of dollars. Companies like Hims & Hers and Teladoc have become household names, their ads interrupting streaming TV and flooding social media feeds with the promise of quick, convenient care.

Despite their popularity, few patients understand who’s actually taking care of them when they click through a telehealth site. 

National telehealth brands present a unified front to patients across the country. But in more than 30 states, it’s illegal for corporations to practice medicine. So behind the scenes, telehealth companies work with distinct, independent medical groups. Owned by physicians — who often hold 50 state licenses at once — those practices are meant to act as a firewall, making sure that clinical decisions are driven by patients’ needs, not the profits of the corporations they deal with. 

Continue to STAT+ to read the full story…