Cardiologists know the integration problem intimately.
Bunmi is 74, referred for severe tricuspid regurgitation and progressive dyspnea. Her echocardiogram is open on one screen, months of outpatient notes on another. She mentions that her grandchild told her to start tracking her heart rate on the watch she got as a Christmas gift, so she has six months of data on her phone, if that helps. It does help. Except now there are three screens, a wearable dataset, the TRILUMINATE trial results somewhere in memory, a question about her right ventricular function, and a clinic running forty minutes behind.
Nobody taught Bunmi’s cardiologist how to integrate all of this. No training programme prepared them for a consultation where the patient arrives with more longitudinal physiological data than any generation of patients in history and expects it to mean something.
This is not a technology problem. It is a knowledge problem. And it is happening in consulting rooms everywhere, right now, with real patients and real consequences.
This is why Cardiac Cipher exists.
Cardiac Cipher is an independent research platform at the intersection of clinical cardiology, artificial intelligence and technology.
The name is deliberate. A cipher is a code waiting to be decoded, patterns hidden within data, waiting for the right methods and the right questions to make them legible. That is what is happening in cardiovascular medicine at the moment.
Cardiac data is accumulating faster than medicine knows what to do with it. Imaging archives, electronic health records, genomics, continuous biosignals, and wearable monitoring streams. The patterns are there. Traditional analysis cannot see them, and clinicians were never trained to look for them this way.
The work of this moment is decoding these data. Rigorously. Honestly. With patients at the centre. That is what this platform is for.
Before anything else, I want to be precise about who I am and what I can credibly offer.
I am a physician-researcher, MD with an MSc in Digital Health, and currently a PhD candidate researching multimodal machine learning for predicting mortality and heart failure hospitalisation in patients with tricuspid regurgitation following transcatheter tricuspid valve intervention. That research question sits squarely at the intersection of everything this publication will cover.
I am not a practising cardiologist. I do not see tricuspid regurgitation patients in a clinic. My expertise lies in data, methods, and evidence, grounded in a working understanding of clinical medicine but not in the daily realities of point-of-care practice.
What I do have is daily proximity to leading cardiologists through my research collaborations, deep familiarity with the clinical trials and device literature in this space, and an academic obligation to be precise about what the evidence actually shows.
Three forces are converging in cardiovascular medicine at the same time, and their intersection is what makes this moment pivotal.
The data exists. The modern cardiac patient generates more longitudinal data than any generation in history. A patient with a smartwatch, an implantable loop recorder, a blood pressure cuff, and recent cardiac imaging is producing a continuous multimodal stream of information that would have been unimaginable a few years ago. The challenge is no longer collecting the data. It is making sense of it.
The methods have matured. Machine learning, particularly deep learning, foundation models, and multimodal architectures, has demonstrated genuine capabilities in cardiovascular medicine. AI systems now match or surpass cardiologists’ performance in detecting arrhythmias on ECG, estimating ejection fraction on echocardiography, and identifying high-risk patients from electronic health records. As some of them are already FDA-approved, clinicians deserve independent, honest guidance on what the evidence shows.
The clinical urgency is real. Cardiovascular disease remains the leading cause of death globally. An ageing population is driving a growing incidence of cardiovascular cases, and the clinical workforce was not trained for a data environment that did not exist when most of them qualified.
These three forces together explain why the digital cardiology conversation is not a trend. It is an inevitability. Digital approaches offer a path that traditional methods have not provided. But only if the clinicians meant to use them are equipped to evaluate, question, and apply them. That is the gap this publication exists to close.
Cardiac Cipher is written for five audiences, and I want to name them directly.
Cardiologists and cardiology trainees who want to understand AI and digital tools critically, not as passive consumers, but as clinicians who can evaluate evidence, ask the right questions, and advocate for their patients as these technologies enter practice.
Researchers and data scientists in healthcare who need clinical grounding. Those who want to understand cardiovascular disease, the real-world constraints of clinical data, and why building models that survive contact with the healthcare system is harder than it looks on paper.
Early-career researchers and medical students considering a path at the intersection of medicine and technology. Those who want to understand what this career actually entails, what it requires, and whether the investment is worth making.
Patients with cardiovascular disease, particularly those living with structural heart conditions and valve disease, who deserve access to clearly explained, evidence-based information about what is changing and what it means for them.
Digital health professionals and health-tech founders building in this space who need honest, independent perspective on what the clinical evidence shows and where the genuine unmet needs lie.
Cardiac Cipher is not a clinical advice platform. Nothing published here should be interpreted as guidance for individual patient decisions. If you are a patient, please speak with your cardiologist.
It is also not a vendor platform. No medical device company, pharmaceutical company, or health-tech vendor pays for editorial content here. Every opinion is independent. If that ever changes, readers will be the first to know.
It is not hype. The landscape of AI in medicine is filled with studies that impress in the lab and collapse in the clinic. Distinguishing genuine capability from well-marketed optimism is one of the core purposes of this platform.
So, welcome to Cardiac Cipher. This is the beginning of something I have been thinking about for a long time, and I am glad you are here for it.
The goal is not to be first. It is to be accurate. Not to be impressive, but to be useful. To contribute something real to a conversation that matters for patients and clinicians alike.
The field is moving fast. The evidence is uneven. And the people who need to make sense of all of it deserve a platform that takes that responsibility seriously.
That is what Cardiac Cipher is here to be.
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Welcome.
Dr Valentine, MD, MSc. [ORCID] | [LinkedIn]