Using ECG data and clinician interpretations enables accurate patient phenotyping, avoiding healthier-biased cohorts and improving trial design, event rates, and outcome detection.
Fragmented hospital data across siloed systems requires complex linking, cleaning, and de-identification to create usable, high-fidelity patient datasets for analysis.
Multimodal platform and AHA partnership enable secure, independent validation of clinical AI, assessing performance, bias, and real-world impact before healthcare deployment.
AI biomarkers from ECGs can predict cardiovascular events and simulate trial outcomes before a study begins. By modeling endpoints, eligibility criteria, and populations, researchers can optimize trial design, enrich event rates, and potentially reach conclusions faster.
AI can detect subtle disease patterns in imaging and ECGs—but real-world performance and clinical impact matter most. Through its partnership with the American Heart Association, Dandelion helps evaluate algorithm accuracy, workflow impact, and patient benefit across health systems.
Oncology achieved precision through genomics. Cardiology can be followed by unlocking signals hidden in ECGs, echocardiograms, and CT imaging, revealing distinct patient phenotypes and enabling more precise treatment and trial design.
Precision medicine transformed oncology through genomics. Now deep clinical data can bring that same precision to cardiovascular care—identifying patient phenotypes and revealing which therapies work best for specific subgroups, reducing trial-and-error treatment.
Oncology unlocked precision through genomics. Cardio-metabolic disease is next. By extracting AI-driven signatures from ECGs, echos, and CT imaging, we can define granular cardiovascular phenotypes, enrich trials, and move beyond “one-size-fits-all” heart failure toward truly precise treatment.
By recreating a major GLP-1 cardiovascular outcomes trial, we showed how AI can cut timelines nearly in half. Instead of waiting 3.3 years to measure event reduction, an AI-derived ECG biomarker detected risk change in 1.7 years.
AI in healthcare has reached a turning point: more data, better tools, and clinicians ready for change. But not all algorithms are equal.
HFpEF affects ~1% of the population and is often diagnosed too late. By combining deep multimodal clinical data with AI across ECGs, EMRs, and imaging, Dandelion can identify patients earlier, enable more precise trial enrollment, and accelerate treatment development.
