According to the two organisations, the healthcare AI model will integrate Mayo Clinic’s anonymised health data, medical knowledge base and patient care experience with Microsoft’s technology infrastructure. Once operational at scale, the model is expected to analyse diverse types of clinical information to support tasks such as earlier diagnosis and more personalised treatment planning.

The model is currently being deployed within Mayo Clinic’s own clinical environment, where it will be tested and refined through real-world application. The organisations have not disclosed how widely the model is being used at this stage, which specific clinical areas are involved, or when it might be made available to other healthcare providers beyond Mayo Clinic.

Ownership of the model will rest with Mayo Clinic, while Microsoft intends to make it accessible to developers and healthcare companies through Azure Foundry APIs software tools that allow external parties to connect the model directly into their own applications and services.

Gianrico Farrugia, M.D., president and CEO of Mayo Clinic, commented on the development: “We have long believed AI can help transform healthcare. Seven years ago, we launched Mayo Clinic Platform to move healthcare from a pipeline to a platform model through a safe, trusted, patient-centric de-identified data foundation designed to accelerate innovation, breakthroughs, and cures. Now, by combining our clinical expertise and data foundation with Microsoft’s engineering and AI capabilities, we are once again building something new in healthcare and bringing more of Mayo Clinic to more patients.”

Mustafa Suleyman, CEO of Microsoft AI, also spoke on the collaboration: “This is the best collaboration imaginable to help us accelerate towards that future. Mayo has unparalleled clinical expertise, de-identified clinical health data and longitudinal medical insights, and we’re thrilled to partner with their world-class physicians to build a state-of-the-art foundation model for healthcare.”

Healthcare has emerged as one of the most active frontiers for advanced artificial intelligence development, though it also presents considerable challenges. Medical AI systems must be capable of handling complex clinical data, accommodating individual patient health histories, and satisfying the most rigorous standards for safety, privacy and validation.

Under the European Union’s AI Act, AI-based software developed for medical purposes is classified as high-risk. This classification requires that such systems meet a defined set of safeguards, including risk-mitigation mechanisms, high-quality training datasets, clear communication to users, and ongoing human oversight, as outlined by the European Commission.

The growing use of AI in healthcare is reflected in broader public behaviour as well. A 2025 survey of 2,000 patients in the United Kingdom, conducted by healthcare startup Semble, found that one in four representing 24% of respondents were turning to AI tools and social media platforms such as ChatGPT and Instagram for health-related guidance. Separately, in Denmark, visits to the public health information website Patienthåndbogen dropped by 31% between January and November 2025 following the launch of Google’s AI Overview, according to the country’s national news agency Ritzau.

Healthcare AI model development is gaining momentum because the technology holds the potential to process large volumes of medical information rapidly, assist clinicians in diagnostics and complex clinical decisions, and significantly reduce the administrative burden on healthcare staff. Microsoft has described “frontier medical intelligence” as being close to realisation, positioning this partnership as a step in that direction.

Nevertheless, the application of artificial intelligence in medicine continues to raise legitimate concerns around accuracy, potential bias, data privacy and accountability. These concerns are particularly prominent given the sensitive nature of health data and the direct impact that clinical decisions can have on patient outcomes. The two organisations have not provided a detailed timeline for when the healthcare AI model will be made available to healthcare providers outside of Mayo Clinic’s own clinical setting.

Among the newly cleared capabilities is Auto Measure Abdomen, an AI-enabled feature developed to automate routine measurement tasks during examinations. Philips said the technology can help reduce variability in measurements while saving time for clinicians. The company also expanded access to Koios AI decision-support tools, which were previously available off-cart. The functionality is now integrated directly into EPIQ Elite and Affiniti systems, allowing clinicians to classify breast lesions and thyroid nodules within the ultrasound workflow. According to Philips, integration with Koios Bi-RADS enables interpretation and malignancy risk assessment in less than two seconds.

The Elevate Plus package also introduces imaging upgrades aimed at improving visualization of anatomy and blood flow. Philips said XRes Pro+ provides cleaner tissue detail and sharper anatomical boundaries, helping create more consistent images across different body types. In addition, Super Res MVI Pro enhances visualization of microvascular flow, giving clinicians greater clarity when assessing blood circulation and vascular structures. These enhancements are designed to improve image quality while supporting more efficient diagnostic workflows.

Commenting on the clearance, Jie Xue, chief business leader, Precision Diagnosis, Philips, said: “Elevate Plus underscores our commitment to advancing AI-powered ultrasound to help clinicians deliver more consistent, efficient, and confident ultrasound care to patients. By combining intelligent imaging with AI-powered workflow automation across our ultrasound platform, Elevate Plus is designed to reduce variability, streamline routine exams, and support faster, more informed clinical decisions as care teams face growing demand.”

Gretchen Sammy, ultrasound manager at Boston Medical Center, stated: “Elevate Plus is a game-changer for our ultrasound workflow. Automating key measurement tasks allows our sonographers to reduce scanning time by up to 30% without sacrificing clinical precision. During our evaluation, measurements were consistently placed exactly where we would expect them saving time while maintaining the accuracy we rely on.”

Maria Cristina Chammas, director of ultrasound at Hospital das Clínicas, School of Medicine, University of São Paulo, added that AI Ultrasound automation could help shorten scanning times, improve workflow efficiency and reduce repetitive manual tasks that contribute to sonographer fatigue while supporting more consistent clinical results.

The two companies have merged Cercare Medical’s automated perfusion software with multiphase 3D acquisition protocols from Siemens Healthineers. On a technical level, this setup uses the vendor-neutral Cercare Medical Neurosuite to read perfusion maps and metabolic metrics directly inside the angiography suite. By connecting this software to the Syngo DynaCT Multiphase system, operators can generate up to ten cone-beam computed tomography phases for immediate review.

With these tools in place, medical staff can complete detailed stroke care imaging without having to move the patient to a different wing of the hospital.

Keeping a patient in the angiography room removes the typical requirement to transport them for standard magnetic resonance or computed tomography scans during the most critical acute window.

• Cutting down on patient transfers directly reduces procedural delays.
  Faster treatment decisions save millions of neurons that are otherwise lost each minute.
• During a thrombectomy, having instant data about cerebral blood flow and tissue viability gives operators the exact metrics they need to modify their approach on the spot.

Quick access to stroke care imaging makes it much easier to evaluate brain tissue health. The combined technology shows exactly how well different regions of the brain are receiving oxygen and essential nutrients. Because of this, clinical teams can spot microvascular issues, no-reflow occurrences, and distal occlusions right alongside the main thrombectomy procedure.

Clinical researchers who helped establish the scientific groundwork for this solution point out that having direct imaging in the intervention space ensures treatment can carry on seamlessly, even when conventional imaging machines are occupied or unavailable. This immediate line of sight ultimately helps predict clinical outcomes and assess any residual complications as they happen.

With qualitative perfusion technology now holding necessary regulatory clearances, both companies plan to scale their specialized offering globally, aiming to standardize how hospitals handle time-sensitive neurological emergencies.

The radiology network acquisition encompasses I-MED’s minority equity interest in Harrison.ai, a technology firm developing artificial intelligence applications for radiology, including computed tomography (CT) chest and brain scans. I-MED delivers comprehensive diagnostic imaging services across regional and metropolitan regions. As an established teleradiology provider, I-MED offers remote medical image interpretation across Australia, the US, and New Zealand. This service aids patient diagnostics in areas experiencing radiologist shortages. The core modalities include:

• Magnetic resonance imaging (MRI) and computed tomography (CT)
• Positron emission tomography (PET) and nuclear medicine
• Ultrasound and X-ray diagnostics

Jardine Matheson plans to finance this healthcare investment utilizing a combination of debt and cash reserves on its balance sheet. The transaction supports the organization’s long-term strategy for APAC healthcare investment in prominent regional enterprises. Jardine Matheson CEO Lincoln Pan stated that the company is eager to collaborate with Dr. Shrey Viranna, Clare Battellino, and the I-MED clinical team. Pan highlighted the management team’s ability to drive earnings growth and maintain innovation, specifically referencing the AI integration that supports their market position and high clinical standards. He added that the goal of this long-term investment is to develop larger, high-quality assets across their portfolio.

Excluding the Harrison.ai stake, Jardine Matheson forecasts that I-MED’s core operations will achieve an adjusted EBITDA multiple of approximately 11.5x the purchase price for the 12-month period ending June 2026. Furthermore, over the five years leading up to June 2025, the teleradiology provider reported a compound revenue increase of 11% and an adjusted EBITDA growth of 12%. The continuous demand for remote medical image interpretation contributes to supporting these consistent financial metrics.

The closing of the transaction remains subject to customary regulatory approvals and is scheduled to be finalized later in 2026. For this radiology network acquisition, Jardine Matheson retained Rothschild and Co for financial counsel. EY-Parthenon supplied tax and financial guidance, while Chapman Tripp, Linklaters, and Allens acted as legal advisors for the healthcare investment.

The Technological Core of Laboratory Automation

At the heart of this revolution is a suite of technologies designed to handle the most repetitive and delicate tasks with a level of consistency that human operators cannot match. Laboratory automation advancing diagnostic workflows involves the deployment of modular systems that can manage everything from initial sample sorting to final result archiving. These systems utilize advanced robotics in labs to perform precise liquid handling, plate movements, and reagent dispensing. By automating these foundational steps, laboratories can operate around the clock, significantly reducing the “turnaround time” for critical tests. This is particularly vital in acute care settings where a delay of even a few hours in receiving a diagnostic result can have a significant impact on a patient’s treatment plan and eventual outcome.

Robotics in Labs: Enhancing Precision and Safety

The introduction of robotics in labs has addressed two of the most significant challenges in the clinical environment: accuracy and safety. Manual pipetting and sample handling are not only tedious but are also the primary sources of pre-analytical errors. A single misplaced sample or a slight variation in reagent volume can lead to skewed results, necessitating costly and time-consuming retests. Automated robotic arms, guided by sophisticated optical sensors and AI-driven software, eliminate these variables by performing every action with mathematical exactness. Furthermore, robotics play a crucial role in biosafety. By minimizing the direct contact between laboratory personnel and potentially infectious biological samples, automation creates a safer working environment. This was never more evident than during recent global health crises, where automated systems allowed for the safe processing of thousands of highly contagious samples daily without putting staff at undue risk.

Streamlining Diagnostic Workflows through Intelligent Integration

Efficiency in a laboratory is not just about how fast a single machine can run it is about how smoothly samples move through the entire facility. Laboratory automation advancing diagnostic workflows focuses on the “total laboratory automation” concept, where disparate instruments are linked together by conveyor tracks and managed by a centralized Laboratory Information System. This integration allows for a continuous flow of samples, from the moment they are logged in at reception to the final verification of results. Intelligent software can prioritize urgent samples, automatically rerouting them to the front of the testing queue without human intervention. This level of orchestration ensures that the laboratory’s total capacity is utilized effectively, preventing the bottlenecks that often occur in manual or semi-automated environments.

The Impact of High Throughput Testing on Public Health

One of the most transformative aspects of this technological shift is the advent of high throughput testing. This capability allows laboratories to process thousands of samples in a single shift, a requirement that has become standard in modern epidemiology and large-scale screening programs. For instance, in the realm of genetic sequencing and molecular diagnostics, high-throughput systems can analyze vast amounts of data in a fraction of the time it took just a decade ago. This is essential for the burgeoning field of precision medicine, where diagnostic workflows must identify specific genetic markers to tailor treatments to individual patients. By making these complex tests more accessible and affordable, laboratory automation is playing a direct role in the democratization of advanced medical care, ensuring that cutting-edge diagnostics are no longer restricted to elite research institutions.

Data Management and the Reduction of Cognitive Load

A significant but often overlooked benefit of laboratory automation advancing diagnostic workflows is the management of the massive data streams generated by modern testing. As sample volumes increase, the cognitive load on laboratory professionals the pathologists and technicians who must interpret results grows exponentially. Automated systems assist in this by performing initial data validation and flagging any results that fall outside of pre-defined normal ranges. This allows specialists to focus their expertise on the most complex and ambiguous cases rather than spending time on routine, normal results. Furthermore, the integration of automation with digital archives ensures that historical patient data is instantly accessible, allowing for the longitudinal tracking of health trends which is vital for managing chronic conditions and identifying emerging health threats.

Overcoming Implementation Barriers and the Human Factor

Despite the clear advantages, the path to laboratory automation advancing diagnostic workflows is not without its complications. The initial capital investment required for state-of-the-art robotics and software is substantial, which can be a deterrent for smaller clinics or regional laboratories. Additionally, there is the challenge of “workforce transition.” As machines take over routine tasks, the role of the laboratory professional is shifting from manual operator to system manager and data interpreter. This requires a significant investment in retraining and a cultural shift within the organization. However, those who have successfully navigated this transition report higher levels of job satisfaction, as staff are freed from mundane tasks to engage in more intellectually stimulating and clinically significant work.

Future Trends: AI and the Autonomous Laboratory

The next frontier for laboratory automation advancing diagnostic workflows is the full integration of artificial intelligence and machine learning. We are moving toward the concept of the “autonomous laboratory,” where AI systems not only manage the flow of samples but also perform real-time quality control and predictive maintenance on the hardware. An AI-driven system could, for example, notice a subtle drift in a machine’s performance and automatically calibrate it before it affects the accuracy of any results. Moreover, as AI becomes better at pattern recognition, it will assist in complex diagnostic tasks like identifying rare cellular abnormalities in pathology slides or predicting drug resistance in bacterial cultures. This will further enhance the accuracy and clinical utility of the laboratory, making it a proactive partner in patient care rather than a reactive service.

Sustainability and Environmental Considerations in the Lab

Modern laboratory automation also offers a path toward more sustainable operations. Manual processes often involve a significant amount of single-use plastic and reagent waste due to human error or the need for redundant testing. Automated systems are designed for maximum efficiency, using the precise amount of reagent required and optimizing the use of consumables. Furthermore, consolidated automated platforms often have a smaller total footprint and lower energy requirements than a collection of older, standalone instruments. As healthcare systems globally look to reduce their carbon footprint, the efficiency gains provided by advanced clinical lab technology will be an important factor in achieving environmental goals without compromising on the quality of patient care.

Enhancing Reliability in Global Diagnostic Networks

Finally, laboratory automation advancing diagnostic workflows is a key driver in standardizing the quality of care on a global scale. In a manual system, the quality of a test result can vary significantly based on the skill level of the individual technician. In an automated system, the process is standardized, meaning that a test performed in a metropolitan hospital in Europe should yield the same reliable results as one performed in a newly automated lab in an emerging economy. This consistency is vital for international clinical trials and for global efforts to track and manage infectious diseases. By providing a reliable, standardized foundation for diagnostics, automation is helping to build a more resilient and interconnected global health infrastructure.

Conclusion: The Backbone of Modern Medicine

In conclusion, the movement toward laboratory automation advancing diagnostic workflows is one of the most significant developments in the history of clinical medicine. By embracing robotics, high-throughput testing, and intelligent data integration, we are creating a diagnostic infrastructure that is not only faster and more efficient but also inherently more reliable and safe. While the transition requires careful planning and significant investment, the long-term benefits for patient care and public health are undeniable. The laboratory is no longer a hidden room where manual tasks are performed in isolation it has become a high-tech engine of innovation that drives clinical decision-making and empowers doctors to save lives with greater confidence. As we look to the future, the continued evolution of these systems will remain the backbone of a smarter, more responsive, and more equitable healthcare system for all.

AI Integration in Radiology Workflows

At the heart of this transformation is the integration of artificial intelligence into the radiological workflow. For many years, the primary challenge in imaging was not just capturing the data, but interpreting it. A single CT or MRI scan can produce thousands of individual images, creating a massive cognitive load for radiologists. Today, AI algorithms are acting as a second pair of eyes, flagging subtle abnormalities that might be missed by the human eye, such as early-stage lung nodules or minor intracranial hemorrhages. This synergy between human expertise and algorithmic speed is a prime example of medical imaging innovations enhancing diagnostic precision, ensuring that a diagnosis is not just fast, but incredibly accurate.

Advancements in MRI Technology

Magnetic Resonance Imaging (MRI) has seen some of the most impressive hardware upgrades in recent years. The move toward higher field strengths, such as 7-Tesla magnets, has unlocked levels of anatomical detail previously thought impossible. These high-field systems allow researchers and clinicians to see the microscopic structures of the brain, aiding in the early diagnosis of neurological disorders like Alzheimer’s and multiple sclerosis. Furthermore, the development of silent MRI and faster scanning protocols has improved the patient experience, making it easier for children or claustrophobic patients to undergo necessary diagnostics. These improvements in hardware are essential components of medical imaging innovations enhancing diagnostic precision, as they provide the raw data quality necessary for complex clinical analysis.

Next-Generation CT Imaging with Photon Counting

Computed Tomography (CT) technology has also made significant strides, particularly with the advent of photon-counting detectors. Traditional CT scanners convert X-rays into light before turning them into electrical signals, a process that can lose detail and increase noise. Photon-counting CT, however, measures each individual X-ray photon, providing much higher spatial resolution and the ability to differentiate between different types of tissues and materials with greater clarity. This advancement is particularly beneficial in cardiovascular imaging, where it allows for better visualization of coronary arteries and the detection of plaque that might otherwise be obscured. By improving the fundamental way X-rays are detected, medical imaging innovations enhancing diagnostic precision are providing a clearer map of the patient’s internal anatomy.

Molecular Imaging and Nuclear Medicine

Another critical area of development is molecular imaging and Nuclear Medicine. The combination of Positron Emission Tomography (PET) and CT or MRI (PET/CT and PET/MRI) has allowed doctors to see both the structure and the function of organs simultaneously. Using specialized radiotracers, clinicians can observe the metabolic activity of tumors, which often changes long before structural changes are visible on a standard scan. This functional insight is crucial in oncology, as it allows for the precise staging of cancer and the monitoring of a patient’s response to therapy. The ability to visualize disease at a cellular level is perhaps the ultimate expression of medical imaging innovations enhancing diagnostic precision, moving us closer to the goal of truly personalized medicine.

3D and 4D Visualization in Surgical Planning

The rise of 3D and 4D visualization techniques has also changed the surgical landscape. Surgeons can now use patient-specific 3D models, generated from high-resolution scans, to plan complex procedures before entering the operating room. In some cases, augmented reality (AR) is being used to overlay these imaging data directly onto the patient during surgery, providing a GPS for the surgeon’s instruments. This real-time guidance reduces the risk of complications and ensures that interventions are as targeted as possible. This integration of pre-operative data into intra-operative reality is a direct result of medical imaging innovations enhancing diagnostic precision, showing that the value of an image extends far beyond the diagnostic phase.

Data management and interoperability are also playing a vital role in the effectiveness of these innovations. In a modern hospital, imaging data must be accessible to specialists across different departments and even different locations. Cloud-based Picture Archiving and Communication Systems (PACS) have made it possible for a specialist in one city to review a scan taken in another in real-time. This connectivity ensures that the expertise of a sub-specialist radiologist is available to any patient, regardless of where they are located. When the best minds are combined with the best technology, the result is a significant boost in medical imaging innovations enhancing diagnostic precision.

Patient safety is another pillar of the innovation process. Historically, the radiation dose associated with CT scans was a concern for many. Modern reconstruction algorithms, however, can now produce high-quality images from much lower doses of radiation. These low-dose protocols are particularly important for patients who require frequent monitoring, such as those with chronic lung conditions or pediatric patients. By balancing the need for clarity with the need for safety, medical imaging innovations enhancing diagnostic precision are ensuring that the benefits of imaging always outweigh the risks.

The field of ultrasound is also experiencing a renaissance, driven by portability and miniaturization. Hand-held ultrasound devices that connect to a smartphone or tablet have become a reality, allowing for point of care imaging in environments ranging from sports medicine clinics to emergency helicopters. While these devices may not yet match the resolution of high-end console systems, their ability to provide immediate diagnostic information is invaluable. This democratization of imaging technology is a key trend, reflecting how medical imaging innovations enhancing diagnostic precision are becoming more accessible to a wider range of clinicians.

Looking ahead, the next frontier in imaging lies in radiomics the extraction of large amounts of quantitative data from medical images that are not visible to the naked eye. By analyzing the texture, shape, and intensity patterns within an image, researchers are finding signatures that can predict how a tumor might behave or whether a patient is likely to respond to a specific drug. This move toward quantitative imaging transforms the radiologist’s report from a descriptive narrative into a data-driven prediction tool. It represents the pinnacle of how medical imaging innovations enhancing diagnostic precision can contribute to the broader ecosystem of precision health.

Advancing Diagnostic Precision Through Innovation

In conclusion, the evolution of medical imaging is a testament to the power of human ingenuity and its application to the most complex machine of all the human body. Through the integration of AI, the refinement of hardware, and the emergence of molecular techniques, we are entering a new age of diagnostic clarity. These medical imaging innovations enhancing diagnostic precision are not just making images clearer; they are making the path to recovery more certain. As we continue to push the boundaries of what is visible, we provide clinicians with the tools they need to diagnose earlier, treat more effectively, and ultimately improve the quality of life for patients around the world.

Study Design and Key Findings

The research, conducted by teams from Harvard and Stanford, evaluated OpenAI’s “o1 preview” reasoning model across multiple clinical scenarios. The AI system was tested on 76 real emergency room cases in a Boston hospital, with performance assessed at three critical stages: initial triage, first physician interaction, and patient admission.

Key outcomes include:

• The AI achieved diagnostic accuracy of 67% in early-stage triage, compared to 50–55% for human physicians
• Accuracy increased to 82% with additional patient information, versus 70–79% for doctors
• In management reasoning tasks, including treatment planning, the AI scored 89%, significantly outperforming physicians at 34%
• The system demonstrated strong capability in identifying rare and complex diseases using established clinical case benchmarks

These findings highlight the growing capability of AI emergency triage systems to process structured medical data and deliver high-quality clinical reasoning under time-sensitive conditions.

Clinical and Operational Implications

The study underscores the potential of AI to augment healthcare workflows, particularly in emergency departments where rapid decision-making is critical. Researchers noted that AI systems could serve as a second-opinion tool, helping physicians identify diagnostic errors or overlooked conditions by analyzing electronic health records in real time.

According to the findings, AI models excel in environments where data is primarily text-based, enabling them to synthesize patient histories, vital signs, and clinical notes efficiently. This capability positions AI as a valuable support mechanism in triage processes, where incomplete or noisy data often complicates decision-making.

Regulatory and Safety Considerations

Despite the promising results, researchers emphasized that the study does not support replacing physicians with AI. The evaluation was limited to text-based inputs, excluding critical clinical elements such as imaging, physical examinations, and patient interaction cues.

Concerns around accountability, liability, and patient safety remain unresolved. There is currently no standardized regulatory framework governing AI-driven clinical decisions, raising questions about responsibility in cases of diagnostic error. Additionally, experts highlighted the risk of over-reliance on AI outputs, which could influence independent clinical judgment.

The study calls for controlled, prospective clinical trials to assess the safe deployment of AI systems in real-world healthcare environments.

Strategic Outlook for Healthcare Systems

The findings suggest that AI will play an increasingly collaborative role in clinical practice rather than acting as a replacement for human expertise. Researchers anticipate a hybrid care model where physicians, patients, and AI systems operate in tandem to improve outcomes.

The technology’s strength in complex case analysis and rare disease identification further reinforces its potential as a decision-support tool in specialized care pathways. However, its limitations in interpreting non-textual data and contextual patient factors highlight the continued importance of human oversight.

From an industry perspective, HHM Global notes that this development reflects a broader shift toward AI-enabled healthcare delivery, where digital tools are positioned to enhance and not replace clinical expertise.

The newly cleared Rembra platform is designed to help healthcare providers manage rising demand for imaging services while improving operational efficiency and access to high-quality diagnostics. According to Philips, the platform extends the application of computed tomography across radiology and radiation therapy, enabling more integrated workflows and coordinated patient management. The systems aim to address clinical complexity by supporting faster imaging processes and more streamlined decision-making pathways.

Within the platform, Rembra CT features an 85-cm bore and is engineered for high-throughput environments, allowing up to 270 exams per day across emergency, critical care and interventional settings. Meanwhile, Rembra RT and Areta RT bring similar imaging capabilities into radiation therapy, supporting treatment planning with high-fidelity imaging, an extended field of view and next-generation 4DCT functionality. Collectively, the Rembra platform establishes a unified CT ecosystem designed to operate across the full care continuum.

Dan Xu, business leader of CT at Philips, said:
“As healthcare systems manage increasing demand and complexity, imaging plays a critical role in enabling timely and informed clinical decisions. With the Rembra platform, we are redefining what clinicians can expect from CT, combining speed, scalability, and precision to expand access to high-quality imaging while supporting confident diagnosis and highly accurate treatment planning.”

The new module is engineered to integrate with Johnson & Johnson’s SoundStar Crystal (2D ICE) and NuVision Nav (4D ICE) ultrasound catheters, extending its applicability across a range of clinical scenarios. It is intended to support both the planning phase and real-time execution of procedures targeting various heart rhythm conditions, including AFib, ventricular tachycardia and complex concomitant interventions. By embedding AI into the workflow, the company aims to streamline imaging processes and improve procedural efficiency, further strengthening the role of cardiac mapping as a central tool in electrophysiology practice.

Johnson & Johnson continues to align its mapping innovations with its broader therapy platforms. The Carto system remains a foundational component of the company’s Varipulse pulsed field ablation (PFA) platform, which is used in the treatment of AFib. At HRS 2026, the company is also set to present new real-world data related to Varipulse, following the recent introduction of its next-generation platform in Europe. These developments reflect a coordinated strategy to integrate imaging, mapping and therapeutic capabilities within a single connected ecosystem.

“In my experience, CartoSound Sonata elevates imaging capabilities by streamlining the process of building detailed maps of the heart across multiple chambers using both 2D and 4D ICE technologies, even during the most complex concomitant procedures. This new module continues to demonstrate the power and versatility of the CARTO System and is a clear example of its continued evolution as a key platform in electrophysiology,” said Luigi Di Biase, system director, Electrophysiology, Montefiore Health System.

“For 30 years, the Carto system has led progress in electrophysiology, serving as the foundation of a connected platform that brings together imaging, mapping, and therapy. Looking into the future, we are decisively moving forward with continuous advancements toward new frontiers in cardiac mapping. As we introduce CartoSound Sonata and continue to progress our PFA technologies, we remain committed to innovation that improves how arrhythmias are understood and treated, while evolving and pushing what’s possible in AFib care,” said Michael Bodner, company group chair, Electrophysiology & Neurovascular, MedTech, Johnson & Johnson.

The system has been engineered to optimize the entire imaging workflow, from acquisition to reconstruction. By leveraging AI, it reduces system noise, improves image clarity, and accelerates clinical processes. Designed for seamless integration, the platform delivers PACS-native outputs and embeds directly into existing workflows. Spectral imaging enables the analysis of how tissues absorb varying X-ray energy levels, allowing detector-based systems to generate multiple spectral outputs from a single scan without compromising scan time or performance.

The technology is intended for broad clinical application, including radiology, interventional radiology, and cardiology. It also supports oncology workflows, particularly in treatment preparation and radiation therapy planning. The system combines high-definition conventional imaging with advanced spectral capabilities, while AI-driven reconstruction delivers enhanced image quality with reduced noise. Additionally, the platform is designed to lower radiation dose without affecting diagnostic output and can cut energy consumption by up to 45%.

Powered by third-generation Nano-panel Precise dual-layer detector technology, the system significantly improves processing efficiency. It can reconstruct up to 145 images per second and complete full exams in under 30 seconds, enabling throughput of up to 270 exams daily. This represents a twofold speed increase compared to earlier models. The platform builds on proprietary Spectral Precise Imaging technology, incorporating deep learning to refine imaging outcomes.

Dan Xu, Business Leader of CT at Philips, said:
“With FDA clearance for Verida, we are bringing the next evolution of spectral CT to more markets. By combining always-on spectral imaging with AI-powered reconstruction, Verida enables clinicians to see more, first time right, supporting faster, more informed decisions and expanding the role of CT across clinical pathways.”