In healthcare technology, AI-powered diagnostics are rapidly advancing, but with these advances comes the need for responsible and forward-looking development. Cardio Explorer by Exploris Health AG embodies this thoughtful approach to AI. Achieving our EQUAL CARE certification for AI Diagnostics, Cardio Explorer is an example of how AI can be integrated into healthcare with a clear focus on accuracy, transparency, and patient-centered design.
What is Cardio Explorer?
Cardio Explorer is a non-invasive, AI-powered tool designed to help diagnose obstructive coronary artery disease (CAD) and myocardial ischemia. Unlike more traditional, often invasive diagnostic methods, Cardio Explorer uses simple clinical and laboratory data to provide analysis without stress tests or imaging, offering a safer, cost-effective option and expanding access to early diagnosis for more patients.
Commitment to Gender Representation
Balanced gender representation is a key focus of our certification, and Cardio Explorer’s development process reflects this principle. Many AI diagnostic tools tend to over-represent certain demographic groups, which can result in uneven performance across populations. Cardio Explorer, however, has carefully addressed this, achieving a high degree of gender balance throughout its studies.
In its initial studies, the data included 66% male and 34% female participants, a ratio that aligns with prevalence rates for coronary artery disease. In subsequent validation studies, the gender representation gap was further reduced, achieving a balance of 58% male to 42% female, closely mirroring the general population. This attention to balance helps Cardio Explorer perform consistently well for both men and women, with an AUC of 0.87 in both inpatient and outpatient settings for the detection of obstructive CAD and an AUC of 0.76 for ischemia detection. An evidence mapping by IQWiG on CT or MRI diagnostics for detection of obstructive CAD shows similar results with AUC values of 0.90 for CT and 0.897 for stress MRI (1.5 Tesla, ≥ 50% stenosis).
Data Quality and Transparency
The data powering Cardio Explorer is sourced from high-quality clinical studies, including the LURIC study, and validated in real-world settings. Exploris Health AG has taken rigorous steps in data preprocessing and bias analysis, ensuring that the tool performs reliably across different risk categories and demographics. For instance, missing data is managed carefully, and sensitivity analyses have been conducted to verify the model’s stability.
Exploris Health AG also provides transparency in its training and validation processes, making technical details accessible for both clinicians and patients. This openness contributes to patient trust and informed decision-making, aligning with a future-proof approach to AI in healthcare.
A Thoughtful Approach to Future-Proof AI
As AI in healthcare continues to grow, Cardio Explorer offers a measured and thoughtful model for responsible AI development. With a commitment to gender representation, data quality, affordability, and transparency, it provides a strong example of how AI can be used in a way that aligns with both patient needs and ethical standards. By achieving our EQUAL CARE certification, Cardio Explorer has shown that future-proof AI in healthcare is achievable with careful planning and consideration.
As we continue to certify AI diagnostics that prioritize fairness and reliability, Cardio Explorer represents a step toward a more inclusive and effective future in precision medicine.
#CardioExplorer #ExplorisHealth #HealthcareInnovation #CardiovascularHealth #EQUALCAREnow
For more Information
EQUAL CARE certifies medical intervention with a balanced gender representation in data and evidence. Join EQUAL CARE today and lead the health market with our certification. Together, we can set a new standard for healthcare excellence and create a future where everyone receives the care they deserve.
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Sources:
[1]Ischemia prediction by Cardioexplorer (SDS ≥2 (3%myocardium) on PET) subgroup analyses based on data published in the EPMA paper, 2023;14:631-643
https://cdn.prod.website-files.com/62d97b771d212003198ab765/661e8d3dd293053a2a00c2b0_Ischemia_prediction_EPMA_subgroups_4_2024.pdf
[2]Artificial intelligence to improve ischemia prediction in Rubidium Positron Emission Tomography—a validation study https://cdn.prod.website-files.com/62d97b771d212003198ab765/65f06595051243e57b03be50_Frey%2C%202023%2C%20EPMA.pdf
[3]Use of artificial intelligence to assess the risk of coronary artery disease without additional (non-invasive) testing: validation in a low-risk to intermediate-risk outpatient clinic cohort https://cdn.prod.website-files.com/62d97b771d212003198ab765/63481a6db56d591bb19d66cb_Cardio%20Explorer%20Study%20III%20(Maastricht%202022).pdf
[4]A new non-invasive diagnostic tool in coronary artery disease: artificial intelligence as an essential element of predictive, preventive, and personalized medicine https://cdn.prod.website-files.com/62d97b771d212003198ab765/630db5f368a00e241a759150_5eea028018b465e476b0267e_Luric%20Cohort.pdf
[5]A new memetic pattern based algorithm to diagnose/exclude coronary artery disease
https://cdn.prod.website-files.com/62d97b771d212003198ab765/630db5bf04d2d647916e775e_5eea03561d30441509b52d11_Basel%20Cohort.pdf
[6]IQWiG Report – No. 920: CT or MRI Diagnostics for Suspected Chronic Coronary Artery Disease: An Evidence Mapping
https://www.iqwig.de/download/ga20-01_herz-ct-oder-mrt-bei-verdacht-auf-khk_arbeitspapier_v1-0.pdf Hinz, A., Glaesmer, H., Brähler, E., Löffler, M., Engel, C., Enzenbach, C., Hegerl, U., & Sander, C. (2017). Sleep quality in the general population: Psychometric properties of the Pittsburgh Sleep Quality Index, derived from a German community sample of 9284 people. Sleep Medicine, 30, 57–63. https://doi.org/10.1016/j.sleep.2016.03.008
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