AI can be health equity’s best friend—or one of its worst frenemies
AI can hurt or help the cause of advancing equality of resources, services and outcomes in healthcare. If it’s to do more helping than hurting, the technology must permeate primary care—and do so with robust bias mitigation strategies, comprehensive digital literacy programs and, not least, input from underserved communities.
This is the view of researchers in the U.K. who conducted a systematic review of the relevant literature and had their findings published July 18 in the International Journal of Medical Informatics.
Primary care physician Ayokunle Osonuga, MBChB, of the University of East Anglia and colleagues found only 15% of healthcare AI tools around the world include community engagement in development. Further, they uncovered, algorithmic bias leads to 17% lower diagnostic accuracy for minority patients. What’s more, close to 30% of patients living in rural areas lack access to AI-equipped healthcare tools.
Upon analyzing these statistics and others, the team came up with a number of key observations for moving the needle in the right direction. Here are four.
1. The integration of AI technologies into primary care represents both a tremendous opportunity and a significant responsibility for advancing health equity.
“Based on our systematic analysis spanning diverse AI applications, populations and implementation contexts,” Osonuga and co-authors write, “current evidence demonstrates that AI tools—when thoughtfully designed and implemented—can meaningfully address some of the persistent disparities that plague healthcare delivery.” More:
‘From AI-powered risk stratification systems that improve chronic disease management in underserved populations to telemedicine platforms that expand access to care in rural communities, these technologies offer tangible benefits for populations that have historically experienced inferior healthcare.’
2. The potential for AI to exacerbate rather than ameliorate health disparities remains a serious concern that demands sustained attention and proactive mitigation efforts.
“Our analysis reveals significant challenges including algorithmic bias affecting up to 24% of diagnostic decisions for racial minorities, digital divide issues excluding 29% of rural populations from AI-enhanced care, and insufficient community engagement in 85% of AI development processes,” the authors report. More:
‘The challenges of algorithmic bias, digital divide issue, and insufficient community engagement are not merely technical problems to be solved through better algorithms or more sophisticated technology. They represent fundamental questions about power, participation, and justice in healthcare that require thoughtful policy responses and genuine commitment to equity principles.’
3. Successful AI implementation for health equity requires a paradigm shift from technology-first to equity-first development approaches.
“This shift involves meaningful community engagement from the earliest stages of AI development, robust bias detection and mitigation strategies, comprehensive digital literacy and access programs, and policy frameworks that prioritize equity alongside safety and efficacy,” the researchers write.
‘Without these foundational elements, AI technologies risk becoming another source of healthcare inequality rather than a solution to existing disparities.’
4. The path forward demands collaboration across multiple sectors and stakeholders.
These include technology developers, healthcare providers, community organizations, policymakers and the communities most affected by health disparities, Osonuga and colleagues state. More:
‘This collaboration must be genuine and sustained, moving beyond consultation to true partnership in shaping the future of AI-enhanced healthcare delivery.’
The paper is posted in full for free.
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- University at Buffalo: Advances in National Science Foundation engines grant program with proposal to boost healthcare via AI
- University of Nevada: How AI can enhance early detection of emerging viruses
- Technical University of Dresden: Autonomous AI agents in healthcare
- University of Utah: Is there a place for AI in placing medical needles?
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Dave P. has worked in journalism, marketing and public relations for more than 30 years, frequently concentrating on hospitals, healthcare technology and Catholic communications. He has also specialized in fundraising communications, ghostwriting for CEOs of local, national and global charities, nonprofits and foundations.