The T1D screening kit will initially be available across Europe and in markets that accept CE IVD-marked products. It is not cleared by the US Food and Drug Administration (FDA). The assay relies on capillary dried blood spot (DBS) samples and is processed on Revvity’s GSP instrument — a platform already widely used in newborn screening programmes. The workflow integrates reagents, instrumentation, and dedicated software into a unified system.

The GSP T1D 3plex kit was specifically developed to meet the requirements of emerging national T1D screening programmes. It forms part of Revvity’s broader diabetes portfolio, which spans research, clinical testing, and service models aimed at supporting healthcare systems at multiple levels.

Earlier identification of autoantibody presence is expected to support timely intervention and access to disease-modifying treatment, which remains a core objective for national health programmes tracking paediatric diabetes risk.

Revvity’s Vice-President and Reproductive Health General Manager, Anna Godenhjelm, commented on the significance of the launch: “The launch of the GSP T1D 3plex kit marks an important step forward for Revvity’s expansion into child health screening. This milestone demonstrates how our expertise in newborn screening can be leveraged to advance earlier detection in new areas of healthcare.”

On-Site and Off-Site Testing Services in Partnership with Sanofi

Revvity plans to present details on its T1D screening kit on-site installation and off-site testing services at the European Association for the Study of Diabetes meeting in Milan, Italy. These services have been developed in partnership with Sanofi and represent an extension of the company’s commitment to supporting national diabetes detection initiatives across Europe.

In a related development, Revvity received FDA clearance in May for a total testosterone automated chemiluminescence immunoassay (ChLIA) through its Immunodiagnostic Systems (IDS) subsidiary. This offering enables direct ChLIA measurements of SHBG, total testosterone, and free testosterone, delivering both first-line and second-line diagnostic capabilities for identifying suspected hypogonadism in men.

The autoantibody detection capabilities embedded in the GSP T1D 3plex kit, combined with Revvity’s established footprint in newborn screening, position the company to support the growing demand for early-stage paediatric disease identification across European healthcare markets.

The system is designed to address the challenges patients face between appointments, such as managing treatment side effects, addressing new symptoms, and answering complex questions about their diagnosis. By feeding insights from member responses back into the system, the partnership aims to refine future guidance and improve clinical outcomes over time. According to Amy Flaster, MD, Cigna’s chief medical officer, bringing the power of OpenAI to the group will better connect patients to specialized resources and support, whether they are dealing with the physical impact of treatment or the emotional burden of a diagnosis.

Expansion of Cigna’s Digital Health Infrastructure

The collaboration with OpenAI follows other significant artificial intelligence initiatives already established by The Cigna Group. In July, the company launched Pharmacy Forward, a $100 million AI-driven program under Accredo, designed to expand personalized care management within its health plans. The group has reported that these expanded programs have already contributed to a 42% reduction in avoidable hospital stays among engaged members. By further integrating AI in clinical workflows, Cigna seeks to continue this trend of improving healthcare efficiency through data-driven insights.

This partnership adds Cigna to a growing list of major health insurers collaborating with artificial intelligence firms to modernize their operations. Other recent industry moves include Optum’s partnership with Anthropic in July and Elevance Health’s 2023 agreement with OpenAI to provide enterprise ChatGPT access and training to its workforce. The deal reflects a broader industry shift toward utilizing generative AI to synthesize complex healthcare information and provide more timely, personalized intervention for members managing chronic or complex conditions.

The expansion is particularly critical for the estimated 70% of countries that report weak or inadequate regulatory systems for health products. By broadening the prequalification programme for medical devices, WHO aims to improve equitable access to technologies such as male and female condoms, intrauterine devices (IUDs), and computer-aided detection software for tuberculosis (CAD-TB). As part of this transition, the prequalification of condoms and IUDs has been transferred from the United Nations Population Fund (UNFPA) to WHO. Additionally, male circumcision devices have been integrated into this broader framework to support voluntary medical services that can reduce the risk of HIV acquisition in men by approximately 60%.

Dr. Sylvie Briand, WHO Chief Scientist and Assistant Director-General a.i. for Health Systems, Access and Data, stated: “Everyone, everywhere, should be able to rely on medical devices that are safe, effective and meet high standards of quality. By expanding WHO prequalification, we are strengthening confidence in these essential health products and helping countries expand equitable access to the technologies people need to protect and improve their health.” The inclusion of CAD-TB software also introduces artificial intelligence-driven digital health technology into the programme, facilitating earlier detection and treatment in high-burden countries.

Current operational bottlenecks in radiation oncology planning frequently involve coordinating complex data streams across disparate hardware and software systems. Clinical workflows often require up to 17 discrete manual stages for importing treatment parameters, and analyses of clinical cases indicate more than 44 variations in treatment planning pathways. These inefficiencies are primarily driven by unstructured information, such as clinical progress notes, pathology summaries, and disconnected imaging files. The collaboration evaluates an AI query system designed to extract and synthesize both structured electronic health record data and unstructured clinical assets, allowing clinicians to retrieve patient histories and anatomical landmarks using natural language queries.

Integration and Clinical Performance

The project evaluates a system architecture capable of parsing DICOM imaging sets, medical texts, and historical dosimetry plans into unified contextual profiles. This functional capability operates alongside the workflow orchestration engine of the iRT platform, which automates cross-department handoffs between medical physicists, dosimetrists, and radiation oncologists. By incorporating these generative AI tools, the research aims to minimize manual data curation and prevent configuration errors during the dose planning phase.

The current research builds upon previous software integration efforts between the two entities. Clinical workflow solutions implemented at Massachusetts General Hospital across more than 11,000 treatment plans successfully reduced the operational interval between patient intake and treatment initiation from 30 days to eight days. The ongoing deployment phase explores the integration of generative retrieval algorithms into broader multimodality oncology workflows, including MR-guided therapy and molecular radiotherapy guidance, to improve operational throughput in high-volume cancer centers.

The year-long programme, built around Proximie’s Intelligence Suite, targets more than 20,000 procedures annually across participating sites. The initiative aims to improve theatre efficiency and unlock additional patient capacity from existing NHS facilities, without the need for new infrastructure or extended staff working hours.

A Technology Partnership Built for Scale

The programme brings together three partners — Proximie, AWS, and Deloitte — each contributing distinct capabilities to the evaluation. Proximie leads the programme as the core technology provider. AWS is providing the cloud and AI infrastructure required to securely process high volumes of surgical data across all eight hospital sites. Deloitte is supporting programme delivery and transformation across participating NHS Trusts and the NHS London region.

Kira Levy, Head of Healthcare UK at AWS, described the initiative as “a powerful example of how cloud and AI can help hospitals to unlock capacity within existing resources, so more patients can be treated sooner.”

Sara Siegel, Global Health and Human Services Sector Leader at Deloitte, highlighted the broader context of operating theatre use: “Operating theatres are the most costly and resource-heavy part of a hospital. Efficient use of these facilities allows NHS Trusts to maximise resources and, importantly, ensure more patients receive timely procedures.”

How the Intelligence Suite Works

Proximie is a UK-headquartered health technology company that transforms operating rooms into connected ecosystems of people, devices, and data. Its cloud-based platform combines surgical telepresence, data capture, AI, and computer vision to support collaboration, training, and workflow optimisation.

The Intelligence Suite captures surgical and operational data in the background of the operating theatre without disrupting clinical teams. Data is de-identified and anonymised at source, and Proximie’s AI models then translate it into real-time insights across the operative pathway. This enables clinical teams to identify bottlenecks, reduce delays between procedures, and make better use of available theatre time.

A notable feature of the AWS infrastructure component is its ability to scale across all eight NHS hospital sites without requiring additional on-site infrastructure at each location, making the programme operationally efficient to deploy at this scale.

The Vision Behind the Evaluation

Dr. Nadine Hachach-Haram, Founder and CEO of Proximie, spoke about the motivation behind the programme following the announcement. Writing on LinkedIn, she stated: “I still operate in the NHS, and nobody in those theatres is short of effort… Sometimes there is simply a better way to do things, and it takes hospitals willing to innovate, work with us and push the NHS forward.”

She further added: “Although we are already seeing material impact from our current global deployments, this evaluation is built to find out how big an impact this makes across the NHS, at scale.”

The NHS Surgical AI pilot reflects a response to ongoing capacity challenges. The Royal College of Surgeons (RCS England) had previously expressed concern that the NHS might miss the Government’s interim target of 65% of patients starting treatment within 18 weeks by March 2026. The target was subsequently met, with 65.3% of patients starting treatment within 18 weeks in March. However, RCS England has continued to warn that sustaining that momentum remains a challenge given existing capacity constraints.

Programme Scope at a Glance

The scale of the evaluation underlines the ambition of the partnership. The programme spans 104 operating theatres, catheterisation laboratories, and endoscopy suites across 8 NHS hospital sites, runs for a duration of 12 months, targets over 20,000 procedures per year, and is led by three technology and transformation partners — Proximie, AWS, and Deloitte.

The NHS Surgical AI evaluation represents one approach being explored to make better use of existing theatre capacity, using AI and operational data to identify efficiencies across the surgical pathway. Over the next 12 months, participating sites will use insights from the Intelligence Suite to reduce procedural delays and optimise theatre scheduling, with the goal of enabling more patients to be treated without placing additional demands on clinical staff.

The partnership between Proximie, AWS, and Deloitte illustrates how cloud-based platforms and AI-driven analytics are being applied to address one of healthcare’s most persistent operational challenges — doing more with what already exists.

Known as the Manchester Sandbox, the programme provides a structured environment where innovators, healthcare professionals, and regulators can assess new technologies in real-world clinical conditions. The initiative was officially launched at MFT’s Oxford Road Campus in Manchester, bringing together representatives from the regulatory, NHS, industry, and research sectors.

The programme is expected to initially focus on AI-enabled medical devices, with the broader aim of generating robust evidence on their safety, effectiveness, and suitability for deployment within NHS care pathways. The Manchester Sandbox also intends to explore regulatory approaches that can keep pace with increasingly sophisticated medical technologies as they continue to evolve.

Faster Access to Innovation for Patients

Health Innovation Minister James Frith commented at the launch, stating: “Manchester is leading the way in getting the best new technologies into the hands of local patients faster, from AI models that can identify people at risk sooner to innovative treatments that could transform NHS care.”

The MHRA noted that the programme will enable emerging technologies to be assessed at an earlier stage, while also helping developers understand the evidence requirements needed to support their adoption within healthcare systems. For NHS healthcare technology stakeholders, including NHS organisations themselves, the sandbox is designed to offer greater insight into how new solutions perform in live operational environments and how they could realistically be integrated into existing services.

At the launch event, several technologies were highlighted as examples of the innovation the programme aims to support. These included wearable devices and AI-powered Ambient Voice Technology — the latter of which is already being used in active patient care settings.

The MHRA and MFT confirmed that learnings gathered through the Manchester Sandbox will be shared more broadly to support the safe and informed adoption of innovative technologies across the wider NHS. A roundtable is also planned, bringing together regulators, NHS organisations, innovators, researchers, and patient and public representatives to identify further opportunities for the programme.

The MHRA is expected to invite medical device developers to submit expressions of interest for the next phase of the sandbox. The partnership forms part of wider efforts to accelerate healthcare innovation while maintaining appropriate regulatory oversight, reinforcing the commitment to responsible development of NHS healthcare technology across the United Kingdom.

The National Commission into the Regulation of AI in Healthcare, established by the Medicines and Healthcare products Regulatory Agency (MHRA) in September 2025, arrived at its recommendations following an extensive evidence-gathering process involving more than 12,000 stakeholders — including clinicians, healthcare leaders, industry representatives, and technology developers.

Broad Public Support, With Clear Conditions

The commission’s findings revealed broad public support for AI in healthcare, though that support comes with clear conditions. Stakeholders consistently called for strong safety standards, meaningful human oversight, and greater transparency about when and how AI is being used in patient care. These three pillars form the backbone of the commission’s regulatory thinking.

Professor Alastair Denniston, NHS consultant and Chair of the National Commission into the Regulation of AI in Healthcare, stated: “We innovate because we believe tomorrow’s healthcare can be better than today’s. But any new innovation — including AI — needs to earn its place and earn our trust. These technologies need to show that they are safe, effective and bring benefits to patients, staff and the wider NHS, without leaving people behind.”

He added: “Over the past year we have heard from more than 12,000 people, including patients, carers, clinicians and the technologists building AI tools. One message came through clearly: people are open to AI improving their care, but only if it is safe, overseen by humans, and if they know when it is being used.”

Staged Authorisations: The ‘L-Plates’ Approach

On the matter of faster access to safe and effective AI, the commission recommends that the MHRA introduce staged authorisations for new AI models — a process it likens to ‘L-plates’ for learner drivers. Under this approach, new AI models would be deployed under close supervision with tight guardrails, allowing them to demonstrate real-world safety and performance before being granted fuller authorisation.

The commission stated that this staged approach is designed to give UK patients world-first access to promising new models, while ensuring risk is carefully controlled at every step of the process.

Continuous Real-World Monitoring Throughout the Device Lifecycle

The commission also recommends that AI-enabled medical devices be subject to continuous, real-world monitoring throughout their lifecycle, rather than evaluation at a single point in time. This recommendation reflects the evolving nature of AI technologies after they are deployed.

According to the commission, this approach would help ensure that as AI systems learn and adapt, patients remain protected and regulators retain the visibility to act quickly if problems emerge. AI-enabled medical devices present a unique regulatory challenge precisely because their behaviour can change post-deployment — making ongoing oversight a critical component of responsible AI deployment.

Public Access to Device Safety Information

Another key element of the commission’s recommendations is a provision for members of the public to easily search for information about the safety of specific AI-enabled medical devices, including any adverse incidents. The intent is to give the public “clear, accessible information” they can trust — making safety data on AI-enabled medical devices more transparent and readily available.

Strengthened Enforcement Powers for the MHRA

The commission also recommends granting the MHRA stronger enforcement powers, enabling the agency to “act decisively” in cases where AI systems “fall short of the standards patients expect.” These enhanced powers are intended to ensure that AI healthcare regulation carries meaningful authority — and that patients are protected when systems underperform or fail to meet required standards.

MHRA Chief Executive Officer Lawrence Tallon responded to the recommendations: “We have heard clearly about the opportunities of AI to speed up and improve care, and to release more of clinicians’ time for the essentially human aspects of their work with patients. We will only realise those opportunities if we have a modern, dynamic regulatory framework that accelerates safe adoption, protects patients and commands public and professional confidence. I am so grateful to the many people who contributed to this expert commission to set out the roadmap ahead.”

With the commission’s work now complete, the UK government and the MHRA will carefully consider its recommendations, with a formal response to follow in due course.

Context: Prior Policy Consultation and Current Regulatory Framework

The commission’s recommendations on safe AI rollout follow an earlier ‘policy-shaping’ consultation on AI healthcare regulation, published in June 2026. In the United Kingdom, AI in healthcare is currently regulated under the nation’s medical device regulation (MDR), with the technology classified under the designations of software-as-a-medical-device (SaMD) or AI-as-a-medical-device (AIaMD).

The consultation revealed that 50% of respondents believed the existing regulatory framework needed substantial revision, while 21% called for a complete overhaul. Key areas of concern included safety, performance standards, data governance, and privacy.

International Regulatory Developments

The MHRA is not the only regulatory body seeking industry input on AI. The United States Food and Drug Administration (FDA) also issued a call for stakeholder feedback on the use of generative AI in medical devices in August. That same month, the FDA and the MHRA announced plans for a more collaborative approach to shaping the future of healthcare regulation, with AI a key focus of that partnership — signalling growing international alignment around responsible AI deployment in clinical settings.

If successful, ARPA-H estimates that ADVOCATE will save lives, reduce preventable hospitalizations, and generate an estimated $28 billion in annual cost savings across the heart failure population.

The ADVOCATE program is organized across three technical areas, each with a distinct mandate.

TA1: Patient-Facing Clinical AI Agent

Teams in this area are tasked with developing a patient-facing clinical AI agentic system capable of autonomously supporting patients with heart failure between healthcare visits, while escalating to the human care team when necessary.

Atman Health will build a clinical AI agentic system on an evidence-based clinical decision engine. The system uses large language models (LLMs) with a voice-first interface, dynamically adjusting the diagnostic questions it poses to each patient based on real-time conversational input.

Tempus AI will develop its clinical AI agentic system by extending its existing Olivia patient health app, incorporating continuous monitoring and deeper clinical analysis triggered by meaningful changes in a patient’s health status.

UpDoc will build a clinical AI agentic system in which conversational intelligence is kept separate from clinical authority through a clinician-built rules system that validates every proposed action against approved protocols before execution. The UpDoc team includes OpenAI, Microsoft, and NVIDIA. UpDoc was selected in part because it already holds FDA clearance for a software medical device that uses a patient-facing LLM and has an established track record in developing highly guardrailed autonomous clinical AI within provider EHR workflows.

Under ADVOCATE’s timeline, TA1 teams must submit a first-of-its-kind FDA-authorization package for this product within 24 months of contract award.

TA2: Supervisory AI Agent

Stanford University has been selected to design and build the disease-agnostic supervisory AI system that will continuously monitor the TA1 clinical agents following successful deployment. This includes real-time detection of unsafe recommendations and out-of-distribution behavior, providing an ongoing assurance layer for the FDA, health systems, patients, and clinicians.

Stanford’s approach uses a three-stage, compute-escalating pipeline — outlier filtering, rule-based screening, and a deep-research auditing agent — that produces inspectable, per-claim rationales.

Performers across TA1 and TA2 will work closely together throughout the program to develop technical interfaces, safety benchmarks, and evaluation protocols.

TA3: Scalable Implementation in Health Systems

Teams in this area, anchored in prominent health systems, are responsible for designing and executing the real-world implementation strategy to bring ADVOCATE technology to patients and clinicians across diverse care settings, with a particular focus on rural and underserved communities where cardiology access is most constrained.

Duke University will provide a multi-site validation platform testing ADVOCATE agents across five health systems and rural sites, operating on both Epic and Cerner/Oracle electronic health records (EHRs), amplified by the national reach of the American Heart Association.

Kaiser Permanente will lead an enterprise-scale deployment across 21 medical centers and more than 260 clinics, embedding ADVOCATE agents into Epic EHR workflows for heart failure patients and generating reusable deployment blueprints through shadow-mode deployments and pragmatic randomized clinical trials.

High-Risk Clinical Functions at the Core of ADVOCATE

Speaking at a recent meeting of the NIH Collaboratory Grand Rounds, ADVOCATE Program Manager Haider Warraich, M.D., a practicing cardiologist, outlined the program’s ambitions. Beyond lower-risk functions such as care navigation, appointment scheduling, and cost estimation, the program is deliberately focused on higher-risk clinical capabilities.

“These include making treatment recommendations based on the patient’s prompts, but also the patient’s data from the electronic health record, being able to provide a diagnosis and perform triage. Triage is going to be one of the really important functions of these technologies, and you can’t really do triage unless you can make a diagnosis,” said Dr. Warraich.

He further elaborated on the program’s scope: “We really want to specifically focus on things that are unambiguously high-risk. We’re really focused on being able to change or renew or refill existing prescriptions, or even write new prescriptions. And the prescriptions that you would focus on for the purposes of this program are going to be what we describe as in scope to the primary indication, which in this case is patients with heart failure. In-scope medications would be either guideline-directed medical therapy for heart failure or diuretic management.”

Broader Implications for Clinical Care

ARPA-H has noted that ADVOCATE could establish a pathway for cardiovascular agentic AI to emerge as a new autonomous member of the clinical team, extending its potential beyond heart failure to patients across a wide range of conditions. The program represents a coordinated effort that brings together academic medical centers, major technology companies, and prominent health systems to advance the development of FDA-authorized agentic AI in clinical settings.

Alongside the Epic integration, OpenAI also released its Healthcare Public Data plugin, a dedicated tool that directs ChatGPT to draw answers from nine official healthcare datasets, including PubMed, DailyMed, and the CMS Coverage Database.

Connecting Clinical Data Across Systems

In its official announcement, OpenAI outlined the core challenge the partnership seeks to address. “Healthcare organizations need AI that works across the systems and information central to care and operations,” OpenAI stated. “Patient context, medical evidence, public healthcare data, and organizational knowledge often live in different places. Connecting these sources in a governed workspace helps teams find the right information, understand it in context, and put it to work across the business.”

Through the ChatGPT EHR integration, clinicians can now query patient-specific information directly within the AI environment. Use cases include identifying which screenings a patient is due for or determining which lab results a clinician should review ahead of an appointment. In select deployments, ChatGPT will be embedded directly into the EHR workflow, allowing providers to access AI capabilities without leaving the patient chart.

UCSF Health Among First Pilot Partners

UCSF Health served as a pilot partner on the project, giving the integration a real-world clinical testing ground. Suresh Gunasekaran, president and CEO of UCSF Health, commented on the technology’s potential in the press release: “By bringing relevant information together more quickly and comprehensively, the technology has the potential to reduce time spent synthesizing data and give clinicians more time with patients. We’re also engaging frontline teams to validate these capabilities in practice and help shape where they can add the most value.”

Healthcare Public Data Plugin: Nine Trusted Sources

The Healthcare Public Data plugin represents a focused expansion of ChatGPT’s capabilities within clinical and research environments. Rather than relying solely on general training data, the plugin directs the AI to pull responses from nine verified public health resources. These sources are ClinicalTrials.gov, the CMS Coverage Database, CMS Open Data, DailyMed, openFDA, Medicare Care Compare, the National Plan and Provider Enumeration System, PubMed, and RxNorm.

OpenAI described the plugin as building on ChatGPT’s existing ability to help teams answer clinical questions and synthesize medical research. The OpenAI healthcare offering now goes further by anchoring responses in official, publicly available data. A research team, for instance, could use ClinicalTrials.gov to identify trials in active recruitment, while a pharmacist could consult DailyMed for the most recent medication warnings. Teams performing multidisciplinary work, such as population health teams, can query all nine sources simultaneously.

Safety and Accuracy Evaluated Across Clinical Use Cases

OpenAI emphasized the safety and accuracy standards behind the ChatGPT EHR integration. The company noted that it worked alongside physician partners to evaluate the connected EHR context across 27 clinical use cases. Across a total of 4,363 ratings, physicians rated 99.1% of ChatGPT responses as safe across all use cases. Additionally, 93% of tested responses were rated as having “good” or better accuracy.

The dual release of the Epic Systems integration and the Healthcare Public Data plugin positions ChatGPT as a more structured and verifiable resource for clinical teams, supported by real-world pilot data and direct integration into established healthcare workflows.

First European Procedures Successfully Completed

Following CE Mark approval, the first European cases using the Amulet 360 were completed by three leading physicians: Prof. Boris Schmidt at the Cardiovascular Clinic Bethanien in Frankfurt, Germany; Prof. Ingo Eitel at the University Heart Center in Lubeck, Germany; and Prof. Jens Erik Nielsen-Kudsk at Aarhus University Hospital in Aarhus, Denmark. Abbott has indicated it will continue to expand the device’s use across Europe over the coming weeks.

Understanding the Left Atrial Appendage and AFib

The left atrial appendage is a small, structurally complex pouch connected to the heart’s upper left chamber. In patients with AFib — an irregular heartbeat occurring in the heart’s upper chambers — blood can pool within the LAA, significantly increasing the likelihood of clot formation and, subsequently, stroke.

For AFib patients who are unable to tolerate long-term blood-thinning medication, physicians may opt for left atrial appendage closure through a minimally invasive procedure. Devices such as the Amulet 360 are designed to seal off the LAA entirely, offering a targeted pathway for atrial fibrillation stroke prevention.

Device Design and Dual-Seal Technology

The Amulet 360 is composed of two primary components. Its redesigned lobe — the body of the device — is softer and more conformable than Abbott’s previous-generation LAA occluder, enabling physicians to treat patients with a broader range of LAA anatomies. The lobe features smaller anchors than its predecessor to secure placement within the LAA. A second component, the outer disk, serves as an additional sealing layer to help close off the appendage entirely.

Together, the lobe and disk operate as a dual-seal system designed to prevent blood clot formation and reduce stroke risk.

“Amulet 360 builds on the proven legacy of our dual-seal design seen in the first-generation LAA occluder, which helped many people with AFib reduce their risk of stroke without the need for long-term blood thinners,” stated Christopher Piorkowski, M.D., Chief Medical Officer of Abbott’s electrophysiology business. “As the latest addition to Abbott’s portfolio of stroke prevention technologies, Amulet 360 reflects our commitment to advancing treatment options and improving outcomes for patients around the world.”

Abbott’s Expanding Electrophysiology Portfolio

The CE Mark approval of the Amulet 360 Left Atrial Appendage Occluder further broadens Abbott’s suite of technologies intended to help physicians manage cardiac arrhythmias. It follows U.S. and European approvals for the Volt PFA System in 2025 and the European approval of the TactiFlex Duo earlier this year.

Abbott’s broader AFib treatment ecosystem includes the TactiFlex Duo Ablation Catheter, Sensor Enabled, and the Volt PFA System — both of which utilise specialised energy to address the erratic cardiac signals associated with AFib — as well as the EnSite X EP System, which generates detailed three-dimensional maps of the heart to assist physicians in identifying and treating the source of irregular heartbeats. Procedures designed to combine left atrial appendage closure with AFib treatment simultaneously are currently under investigation in Europe.

The Amulet 360, built upon the foundation of Abbott’s Amplatzer Amulet LAA Occluder — in use in Europe since 2013 and in the United States since 2021 — is approved for investigational use only in the United States.