--- title: "ATLAS News" language: "en" type: "page" original_url: "https://www.atlas-digitale-gesundheitswirtschaft.de/en/atlas-news/" human_version: "../../mensch/en/atlas-news/" description: "What's happening in the global digital health economy at the moment? In this section you'll find the latest news and research in this field." publisher: "Lehrstuhl für Management und Innovation im Gesundheitswesen, Universität Witten/Herdecke" --- # ATLAS News ## New Content on ATLAS – New Science Digests, LinkedIn posts, and newsletters are published regularly on ATLAS. Latest Linkedin Posts Latest Science Digests Latest Digi-Health Monitors --- ## **Latest [Linkedin](https://de.linkedin.com/company/atlas-digitale-gesundheit)** **Posts** See the latest Linkedin post from ATLAS Digital Health Economy **Linkedin post from the 30th July 2026** **Adaptive AI-enhanced radiotherapy in India**: **The first clinic in India to adjust the radiotherapy plan to the patient’s body in real time** In radiotherapy, everything comes down to precision, down to the millimetre. Yet a tumour rarely stays where it was yesterday, because the bladder fills, the bowel moves, and everything shifts by a few millimetres. And still, treatment usually follows a plan drawn up days earlier. The result is that surrounding healthy tissue is burdened more than necessary. In India in particular, this challenge weighs heavily. (…) Click to continue reading According to the [Indian Council of Medical Research (ICMR)](https://www.linkedin.com/company/icmrorganization/), the country records around 1.46 million new cancer cases each year, which makes the need for radiation that is both precise and tissue-sparing very clear. This is exactly where a technology comes in that [Manipal Hospitals (MHEL)](https://www.linkedin.com/company/official-manipal-hospitals/) Yelahanka in Bengaluru is the first clinic in India to use. The swedish platform, called [Elekta](https://www.linkedin.com/company/elekta/) Evo, adjusts the treatment plan in real time to the patient’s current anatomy, while they are already lying on the treatment couch. Before each session, the system captures a high-definition image, after which an AI automatically re-contours the organs and the target area, so that clinicians can adapt the dose to the anatomy of the day. Two components work together here, an AI-enhanced imaging system for the fine depiction of tissue, and software that handles the automatic contouring and a rapid dose calculation. The system is used above all for cancers in the pelvic region, where the anatomy changes particularly strongly between sessions, such as bowel, cervical and prostate cancer. The first patient treated at Manipal Hospital completed therapy, according to the hospital, with only minimal radiation-related side effects. The only “catch” is that the daily adaptation lengthens the time a patient spends on the couch. On top of that, the AI’s automatic contouring has to be monitored closely. For most, however, that effort should be worth it if the radiation ultimately hits more precisely and healthy tissue is spared. - [LinkedIn](https://www.linkedin.com/pulse/adaptive-ki-gest%25C3%25BCtzte-strahlentherapie-indien-fr1sf/?trackingId=%2FBMlgW6R%2Bar4RHQIAZSrEw%3D%3D) **Linkedin post from the 23rd July 2026** **What happens when the AI isn’t accepted on the first attempt?** Many hospitals and hospital networks now have the budget for autonomous AI agents. Whether they are ready for them is another question. Because the step from a copilot that makes suggestions to an agent that takes on tasks independently requires governance, clear boundaries and a willingness to learn from mistakes quickly. What that looks like in practice is shown by an example from [ECU Health](https://www.linkedin.com/company/ecuhealthnc/). (…) Click to continue reading The regional hospital network in rural eastern North Carolina has put two autonomous AI agents to work inside its electronic health record, together with [Epic](https://www.linkedin.com/company/epic1979/). The first supports the roughly 14-person team at the Transfer Center, which decides on transfers between the hospitals in the network. It checks patient data against an overview of what each hospital is able to offer and produces a short recommendation on which hospital can take the patient. The final decision still rests with a nurse. Using this agent has cut manual chart review by around 20 hours per week, so far without a single hallucination. The second agent prepares the daily discharge rounds for roughly 160 case managers by summarising the course of a patient’s stay. Here, however, the start was bumpy. The first summaries were too long and ultimately amounted to little more than an ordinary chart, so the feedback from users on day one was correspondingly poor. The IT team reacted quickly and cut the outputs down to the essentials within a day. By the second day, 75 percent of case managers rated the tool positively. [ECU Health](https://www.linkedin.com/company/ecuhealthnc/) is taking a fairly consistent approach to implementation. The agents work exclusively with tightly limited data and have no patient contact whatsoever. Even listening in on the transfer calls would have been technically possible, but was deliberately ruled out. On top of that, the network is a smaller system that will carry the maintenance of its agents itself going forward. And that is precisely why it proceeds so carefully here. What should be taken away from this case is the approach itself: - An agent that writes three sentences, which are then reviewed by a human. - A poor rating that is corrected within the shortest possible time. - The decision to start small and controlled rather than wanting everything at once. For smaller institutions considering similar steps, it may be precisely this approach that is the most important message. - [LinkedIn](https://www.linkedin.com/pulse/passiert-wenn-die-ki-im-ersten-anlauf-nicht-qlpve/?trackingId=Lml3XZHfjKNKmQryzziuuQ%3D%3D) **Linkedin post from the 17th July 2026** **Precise cardiac diagnostics on their way into routine care?** Survived a heart attack, but what does it mean for your health afterwards? After a heart attack, one of the crucial questions is whether lasting damage has occurred, because that determines how much risk a patient carries going forward. Until now, making that assessment has taken quite a while and varied from one expert to the next. (…) Click to continue reading So far, a specialist has had to review a cardiac MRI for this, which can take up to an hour. At the National Heart Centre Singapore, this assessment is now handled by an AI model called CARDIA-GM. Within a minute, the software automatically detects how badly the heart muscle is scarred and whether the smallest blood vessels are blocked. Both are strong indicators of a patient’s prognosis. Someone with scarring in more than 20 percent of the heart muscle carries roughly three times the risk of experiencing another cardiac event; with severe blockages, that rises to as much as six times. This is not really about the speed of the assessment, impressive as one minute is, but rather about the fact that this technological progress suddenly makes the procedure broadly deployable. As a result, far more people could receive such an MRI and its assessment. Looking at Europe, however, other aspects come to the fore, prompting the question of whether such models will even reach clinics in time, given that their introduction is complicated by the unclear interplay between the EU AI Act and the Medical Device Regulation (MDR). AI-based medical devices are classified as high-risk systems and therefore fall under both frameworks at once. The AI Act has been applying in stages since 2025, but for AI in medical devices the high-risk obligations were originally meant to take effect only from 2 August 2027. Because standards and infrastructure are still missing, EU legislators have proposed pushing this back to 2028. This creates a somewhat peculiar picture. We are dealing with a regulation that keeps postponing itself, because the apparatus behind it is not ready. In practice, this means a model like CARDIA-GM could pass its clinical validation and still be left waiting on an approval pathway that is still under construction. For developers, this quickly becomes a question of funding as well. Because who is going to invest in a product whose approval keeps being delayed and whose timelines no one knows? To avoid any misunderstanding: regulation is extremely important for an AI like this, because a model that misjudges heart damage is a real risk. The only question is whether Europe, with this approach, ultimately increases safety or reinforces its own slowness. Indonesia’s Health Minister Dante Saksono Harbuwono once put it this way in a different context: “*Innovation without governance is risk. Governance without innovation is stagnation.”* - [LinkedIn](https://www.linkedin.com/pulse/pr%25C3%25A4zise-herzdiagnostik-auf-dem-weg-die-regelversorgung-98dnf/?trackingId=F4bdhnRRWANN9CG2vqEfIA%3D%3D) --- ## **Latest**[**Science Digest**s](international-science-digests.md) Science Digests present and analyze the latest research findings from international studies on digitalization-related topics in healthcare. - [![Lessons Learned From Multidomain mHealth Development Practice in Application of Lifestyle Management](https://www.atlas-digitale-gesundheitswirtschaft.de/mensch/assets/bilder/wp-content/uploads/2022/05/shutterstock_1508157266.png)](https://www.atlas-digitale-gesundheitswirtschaft.de/en/blog/2026/07/30/lessons-learned-from-multidomain-mhealth-development-practice-in-application-of-lifestyle-management/) [Lessons Learned From Multidomain mHealth Development Practice in Application of Lifestyle Management](blog/2026/07/30/lessons-learned-from-multidomain-mhealth-development-practice-in-application-of-lifestyle-management.md) While multidomain lifestyle mHealth interventions offer great promise for public health, their impact is often limited in the development process. Current research frequently overlooks micro-level design elements, leaving developers with… [Read more](blog/2026/07/30/lessons-learned-from-multidomain-mhealth-development-practice-in-application-of-lifestyle-management.md) 2026-07-30 - [![Smart Healthcare vs. the Silver Digital Divide](https://www.atlas-digitale-gesundheitswirtschaft.de/mensch/assets/bilder/wp-content/uploads/2022/05/shutterstock_558560407.png)](https://www.atlas-digitale-gesundheitswirtschaft.de/en/blog/2026/07/30/smart-healthcare-vs-the-silver-digital-divide/) [Smart Healthcare vs. the Silver Digital Divide](blog/2026/07/30/smart-healthcare-vs-the-silver-digital-divide.md) As global populations age, digital solutions are often presented as a means to ease healthcare burdens. But is that truly the case? How do older adults—the primary end users of… [Read more](blog/2026/07/30/smart-healthcare-vs-the-silver-digital-divide.md) 2026-07-30 - [![Turning Medical Data into Decision Capacity: A Practical Blueprint for a Cloud-Based Platform in Anesthesiology](https://www.atlas-digitale-gesundheitswirtschaft.de/mensch/assets/bilder/wp-content/uploads/2022/05/shutterstock_1525668521.png)](https://www.atlas-digitale-gesundheitswirtschaft.de/en/blog/2026/07/30/turning-medical-data-into-decision-capacity-a-practical-blueprint-for-a-cloud-based-platform-in-anesthesiology/) [Turning Medical Data into Decision Capacity: A Practical Blueprint for a Cloud-Based Platform in Anesthesiology](blog/2026/07/30/turning-medical-data-into-decision-capacity-a-practical-blueprint-for-a-cloud-based-platform-in-anesthesiology.md) Anesthesiologists make rapid decisions under time pressure, often with incomplete information. Can cloud-based platforms help translate available data into practical decision support? [Read more](blog/2026/07/30/turning-medical-data-into-decision-capacity-a-practical-blueprint-for-a-cloud-based-platform-in-anesthesiology.md) 2026-07-30 --- ## **Latest [Digi-Health Monitor](global-digi-health-monitor.md) News Articles** The latest global news in the healthcare sector regarding digitalization can be found in the Digi-Health Monitor. - [DIGI-HEALTH MONITOR Calendar Week 34](blog/2026/08/27/digi-health-monitor-calendar-week-34.md) Digital health news: A hybrid surgical robot enters Europe, while AI governance, smarter hospital operations, image exchange and consumer diagnostics advance. [Continue reading](blog/2026/08/27/digi-health-monitor-calendar-week-34.md) - [DIGI-HEALTH MONITOR Calendar Week 33](blog/2026/08/19/digi-health-monitor-calendar-week-33.md) Digital health news: AI supports cancer prognosis, scales across Mayo Clinic, guides patient triage, and drones bring prescriptions directly to homes. [Continue reading](blog/2026/08/19/digi-health-monitor-calendar-week-33.md) - [DIGI-HEALTH MONITOR Calendar Week 32](blog/2026/08/12/digi-health-monitor-calendar-week-32.md) Digital health news: From physical AI to safer hospitals, smarter cancer care and AI documentation, this week shows how technology is reshaping care. [Continue reading](blog/2026/08/12/digi-health-monitor-calendar-week-32.md) ## Images on this page - 1785400277723.jpg: ../../mensch/assets/bilder/wp-content/uploads/2026/07/1785400277723.jpg - 1784798746275.jpg: ../../mensch/assets/bilder/wp-content/uploads/2026/07/1784798746275.jpg - 1784297397324.jpg: ../../mensch/assets/bilder/wp-content/uploads/2026/07/1784297397324.jpg - Lessons Learned From Multidomain mHealth Development Practice in Application of Lifestyle Management: ../../mensch/assets/bilder/wp-content/uploads/2022/05/shutterstock_1508157266.png - Smart Healthcare vs. the Silver Digital Divide: ../../mensch/assets/bilder/wp-content/uploads/2022/05/shutterstock_558560407.png - Turning Medical Data into Decision Capacity: A Practical Blueprint for a Cloud-Based Platform in Anesthesiology: ../../mensch/assets/bilder/wp-content/uploads/2022/05/shutterstock_1525668521.png - shutterstock_669389722-min-scaled.jpg: ../../mensch/assets/bilder/wp-content/uploads/2023/08/shutterstock_669389722-min-scaled.jpg - Titelvideo.mp4: ../../mensch/assets/bilder/wp-content/uploads/2025/02/Titelvideo.mp4 --- This page is optimised for AI systems. 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