A new bet on early heart failure detection and why women’s health is at the center.
Updated
January 8, 2026 6:28 PM
A doctor holding an artificial heart model. PHOTO: ADOBE STOCK
Heart disease does not always announce itself clearly, especially in women. Many of the symptoms are ordinary, including fatigue, shortness of breath and swelling. These signs are frequently dismissed or explained away. As a result, many women are diagnosed late, when treatment options are narrower and outcomes are worse. That diagnostic gap is the context behind a recent investment involving Ultromics and the American Heart Association’s Go Red for Women Venture Fund.
Ultromics is a health technology company that uses artificial intelligence to help doctors spot early signs of heart failure from routine heart scans. It has received a strategic investment from the American Heart Association’s Go Red for Women Venture Fund.
The focus of the investment is a long-standing blind spot in cardiac care. Heart failure with preserved ejection fraction, or HFpEF, affects millions of people worldwide, with women disproportionately impacted. It is one of the most common forms of heart failure, yet also one of the hardest to diagnose. Studies even show women are twice as likely as men to develop the condition and around 64% of cases go undiagnosed in routine clinical practice.
Ultromics works with a tool most patients already experience during heart care: the echocardiogram. There is no new scan and no added burden for patients. Its software analyzes standard heart ultrasound images and looks for subtle patterns that point to early heart failure. The goal is clarity. Give clinicians better signals earlier, before the disease advances.
“Heart failure with preserved ejection fraction is one of the most complex and overlooked diseases in cardiology. For too long, clinicians have been expected to diagnose it using tools that weren't built to detect it and as a result, many patients are identified too late,” said Ross Upton, PhD, CEO and Founder of Ultromics. “By augmenting physicians' decision making with EchoGo, we can help them recognize disease at an earlier stage and treat it more effectively.”
The stakes are high. Research suggests women are twice as likely as men to develop the condition and that a majority of cases are missed in routine clinical practice. That delay matters. New therapies can reduce hospitalizations and improve survival, but only if patients are diagnosed in time.
This is why early detection has become a priority for mission-driven investors. “Closing the diagnostic gap by recognizing disease before irreversible damage occurs is critical to improving health for women—and everyone,” said Tracy Warren, Senior Managing Director, Go Red for Women Venture Fund. “We are gratified to see technologies, such as this one, that are accepted by leading institutions as advances in the field of cardiovascular diagnostics. That's the kind of progress our fund was created to accelerate.”
Ultromics’ platform is already cleared by regulators for clinical use and is being deployed in hospitals across the US and UK. The company says its technology has analyzed hundreds of thousands of heart scans, helping clinicians reach clearer conclusions when traditional methods fall short.
Taken together, the investment reflects a broader shift in healthcare. Attention is shifting earlier—toward detection instead of reaction. Toward tools that fit into existing care rather than complicate it. In this case, the funding is not about introducing something new into the system. It is about seeing what has long been missed—and doing so in time.
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AI growth is increasingly becoming a manufacturing, packaging and deployment challenge — not just a computing one.
Updated
May 26, 2026 5:28 PM
Taipei 101 and Taipei Nan Shan Plaza, viewed from Elephant Mountain. PHOTO: UNSPLASH
As AI companies continue scaling larger models and data centers, the pressure is no longer falling only on chip design. Manufacturing capacity, advanced packaging and infrastructure deployment are becoming equally important parts of the AI race. AMD’s latest investment announcement reflects how quickly that shift is accelerating.
The US chipmaker announced plans to invest more than US$10 billion across Taiwan’s semiconductor and manufacturing ecosystem to support next-generation AI infrastructure. The investment focuses on expanding partnerships and increasing advanced packaging capacity needed for future AI systems.
The announcement highlights a growing reality across the AI industry. Building powerful AI chips is no longer enough on its own. Companies now also need the manufacturing networks, packaging technologies and supply chain coordination required to deploy AI infrastructure at global scale.
AMD’s investments center heavily around advanced chip packaging, an area becoming increasingly critical as AI systems demand higher performance and greater power efficiency. Traditional chip architectures are struggling to keep pace with the size and complexity of modern AI workloads. Advanced packaging helps connect processors, memory and computing systems more efficiently while managing power and cooling limitations inside large-scale AI environments.
The company said it is working with Taiwan-based partners including ASE, SPIL and PTI to develop next-generation packaging technologies for its upcoming 6th Gen AMD EPYC processors, codenamed “Venice.” AMD also said it had qualified what it described as the industry’s first 2.5D panel-based EFB interconnect technology alongside PTI.
At the center of the broader strategy is AMD Helios, the company’s rack-scale AI infrastructure platform scheduled for deployment beginning in the second half of 2026. The platform combines AMD Instinct MI450X GPUs, 6th Gen EPYC CPUs, networking systems and AMD’s ROCm software stack into integrated AI infrastructure systems designed for hyperscale deployment.
Rather than selling individual processors alone, companies are increasingly building complete AI infrastructure platforms that combine hardware, software, cooling systems and power management into unified deployments. That transition is reshaping how AI infrastructure is designed, manufactured and delivered.
Taiwan is also becoming more deeply embedded in that process. AMD’s investment spans not only semiconductor packaging companies but also manufacturing and system integration partners including Sanmina, Wiwynn, Wistron and Inventec. The partnerships reflect Taiwan’s growing role as one of the operational centers of the global AI infrastructure economy.
Dr. Lisa Su, Chair and CEO of AMD, said: “As AI adoption accelerates, our global customers are rapidly scaling AI infrastructure to meet growing compute demand. By combining AMD leadership in high-performance computing with the Taiwan ecosystem and our strategic global partners, we are enabling integrated, rack-scale AI infrastructure that helps customers accelerate deployment of next-generation AI systems”.
Power efficiency is becoming another major challenge shaping AI infrastructure decisions. As AI workloads consume more electricity and generate more heat, infrastructure providers are increasingly being forced to rethink cooling systems, interconnect technologies and deployment economics.
AMD’s announcement signals how the AI competition is evolving beyond model development and raw computing power. The next stage may depend just as heavily on who can manufacture, package and deploy AI infrastructure fast enough to support global demand.