AI & Health Equity: Podcast on Underserved Communities
- Physician executive Sreeram Mullankandy discusses his article, "Bridging the digital divide: Addressing health inequities through home-based AI solutions." He highlights the risk of vulnerable populations being left behind...
- He argues that artificial intelligence, when deployed thoughtfully, can be a powerful equity enabler.
- mullankandy notes that only 20% of health outcomes are directly related to clinical care.
Artificial intelligence is poised to revolutionize home healthcare,and this must be done in an equitable manner. Discover how AI is being used to identify and address social determinants of health (SDOH), which influence 80% of health outcomes, as discussed in our latest podcast. sreeram Mullankandy explains how AI can bridge language and literacy gaps, identify hidden patterns, and become a force multiplier for community health workers. This episode also tackles the need for fairness audits to prevent AI biases and the immense economic benefits of a more just healthcare system, which could save trillions in costs. Learn how innovative solutions are being developed to improve outcomes. News Directory 3 brings you this crucial conversation to understand the pivotal role of AI in reducing health inequities. Discover what’s next in the evolving landscape of AI-driven healthcare.
AI-Powered Home Care: Reducing Health Inequities
Updated June 21, 2025
Physician executive Sreeram Mullankandy discusses his article, “Bridging the digital divide: Addressing health inequities through home-based AI solutions.” He highlights the risk of vulnerable populations being left behind as healthcare shifts into patients’ homes.Mullankandy explains that non-medical factors, or Social Determinants of Health (SDOH), can influence up to 80 percent of health outcomes but are often missed by customary systems.
He argues that artificial intelligence, when deployed thoughtfully, can be a powerful equity enabler. The discussion covers how AI can identify hidden SDOH patterns with high accuracy,bridge language and literacy barriers for the nearly 36 million U.S. adults who need it, and serve as a force multiplier for community health workers. Mullankandy also addresses the critical need for fairness audits to prevent AI from perpetuating bias and the massive economic incentive for building a more just system, which could save $1.7 trillion in health care costs.
mullankandy notes that only 20% of health outcomes are directly related to clinical care. The other 80% are related to SDOH, primarily categorized into food security, home security, and transportation. He emphasizes that AI makes it easier for health tech professionals to intervene in these areas.
What’s next
Mullankandy’s work focuses on building an EHR with AI capabilities for home-based healthcare, aiming to improve clinical outcomes by addressing social determinants of health.