According to a company press release, RadNet, Inc. has acquired Gleamer SAS, a leading radiology AI company based in Paris, France. The acquisition will be integrated into DeepHealth, RadNet’s wholly owned subsidiary and further expands DeepHealth’s AI-powered health informatics solutions and services. With more than 130 professionals, Gleamer is a fast growing, cloud-first, radiology AI…
Cloud-based SaMD (Software as a Medical Device) designed to assist healthcare providers in predicting delivery date using ultrasound imaging
Abbott’s CardioMEMS™ HERO is a compact remote reader that is 60% lighter compared to its previous generation device for heart failure patients with Abbott’s CardioMEMS pulmonary artery pressure sensor.
While some wearables may be tailor made for trial use, they face challenges translating to real-world patient applications outside of a stringent trial setting, and vice versa. This creates a unique challenge for med-devs looking to bring their devices out of the trial setting and into patient care.
The next phase of digital mental health adoption depends less on innovation and more on how the industry uses real-world evidence, builds reimbursement frameworks, and applies regulatory precedent to accelerate scale.
Artificial intelligence is moving quickly into mainstream medical devices, and the industry has become fluent in a familiar set of concerns: bias, transparency, and cybersecurity. These topics matter, but they don’t capture the risks most likely to shape patient safety in the coming decade. The deeper challenges lie in the interactions between algorithms, clinical workflows, data pipelines, and human decision making. Those interactions are where safety is won or lost, and they remain the least examined part of AI adoption.
A recent survey of 2,000 UK patients found one in four patients (24%) already use AI for health guidance, and nearly one in three (30%) would be willing to consult AI or social media rather than wait to see a clinician. Despite this growing reliance on digital tools, the same survey revealed a disconnect in patient confidence, with almost 80% reporting that they do not feel fully in control of their healthcare. How might ChatGPT Health accelerate this shift, as well as what needs to happen to ensure AI adoption genuinely empowers patients rather than adding further complexity?
The initial fear that the artificial intelligence and machine learning evolution will replace humans is shifting. A new narrative recognizes the potential for an AI-enabled workforce — one where the technology is a jobs creator, enabling us all to be more productive rather than making millions of people redundant or obsolete — actually giving rise to the multi-disciplinary, power employee.
The FDA announced the TEMPO pilot, an approach to encourage the use of digital technologies that meet people where they are. The pilot supports innovative tools and a health care delivery model that could improve care for millions of Americans managing chronic disease.
AI and large language models (LLMs) are revolutionizing critical care by integrating scattered data from various sources to deliver real-time insights. This allows for prompt escalation or de-escalation of treatment and enhances patient outcomes. These technologies also help reduce staffing issues and prevent clinician burnout by continuously monitoring patient risk and supporting decision-making in busy ICU settings.
