Dandelion Health, with funding from The SCAN Foundation, has published a new report exploring both the opportunities and risks of clinical AI tools, based on their validation of algorithm performance and bias in historically underserved populations.

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Dandelion Health, a real-world data (RWD) and clinical AI platform powering next-generation precision medicine and personalized care, has released a new report, Equity in Medical AI: Algorithm Validation to Improve Health for Underserved Populations. The report was made possible with funding from The SCAN Foundation.

The report finds that while many AI models perform equitably, significant exceptions highlight how algorithmic bias—especially against older adults and rural populations—can deepen health disparities if left unaddressed. Its insights highlight the critical need for rigorous AI validation to ensure that clinical AI algorithms perform equitably across diverse patient populations, particularly those historically underserved in healthcare.

As AI increasingly has the potential to guide clinical decision-making and transform care, concerns over algorithmic bias remain a barrier to adoption. Dandelion Health has developed a validation framework that evaluates AI models against social determinants of health (SDoH) and demographic measures. The report shares key findings from conducting these validations for electrocardiogram (ECG)-based AI models, and provides actionable insights to ensure AI advances equitable health outcomes rather than reinforcing disparities.

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Among the report’s key findings:

  • AI often underperforms for older adults. Many models struggle to accurately assess patients over 65 due to gaps in training data, leading to misdiagnoses and delayed care.
  • Rural populations face unique AI challenges. Algorithms trained on urban data often fail to generalize to rural healthcare settings, limiting their effectiveness for nearly 20% of Americans.
  • Bias linked to socioeconomic factors is less common but carries high stakes. When errors disproportionately affect lower-income or socially vulnerable patients, they risk deepening existing healthcare inequities.

“Our work demonstrates that AI has the potential to improve health equity—but only if we know how these tools perform for different patient groups,” said Shivaani Prakash, MSc, PhD, Head of Data at Dandelion Health. “Without validation, bias remains invisible. By making these disparities transparent, we can work toward AI systems that benefit everyone, not just those already well-served by the healthcare system.”

“This report underscores the importance of validation to ensure AI effectively supports diverse patient populations—including older adults from historically marginalized communities,” said Vice President of Innovation and Investments Anika Heavener of The SCAN Foundation. “We’re proud to fund this work, which reveals key opportunities in innovation and equips leaders with insights to drive more equitable health outcomes.”

The report is part of Dandelion Health and The SCAN Foundation’s commitment to responsible AI development and health equity, especially with AI used in care decisions for vulnerable populations and older adults. By providing transparent and accessible validation, the organizations aim to support AI developers, healthcare providers, and policymakers in making informed decisions about AI deployment.

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Source – PR Web