AI in Medicine: How Corporate-Led Algorithms Reinforce Health Inequities While Bypassing Ethical Safeguards
Original framing: “Using AI to offer personalized health advice” — Nature
Marginalized communities, such as disabled activists in the *Chronically Capable* movement or *undocumented immigrant health advocates*, have long argued that AI health tools exclude them by design—whether through language barriers, lack of representation in training data, or reliance on biometric tracking that violates privacy. For example, *Latina women* in the U.S. report that AI fertility trackers misdiagnose conditions due to racial bias in algorithms, a systemic issue ignored in mainstream discussions. These voices demand *data justice* frameworks, where communities control how their health information is used—not corporations or governments.
The mainstream framing of AI in healthcare as a neutral, innovative force obscures its role as a *corporate-controlled extension of neoliberal health austerity*, where 'personalization' masks systemic exclusion.
Indigenous and marginalized critiques—such as those from the *First Nations Health Authority* or the *Algorithmic Justice League*—reveal how these tools replicate historical harms, from *Tuskegee* to *Havasupai*, while bypassing ethical safeguards. Cross-cultural models, like *Cuba’s* community-based healthcare or *Rwanda’s* clinician-led telemedicine, demonstrate that AI is not an inevitability but a *choice*—one that prioritizes profit over equity. The trickster lens exposes the absurdity of a system where Silicon Valley CEOs profit from diagnosing poverty-related diseases while their own health is managed by human experts, highlighting how solemnity conceals exploitation. Solutions must center *public ownership*, *decolonial data governance*, and *investment in human-centered alternatives*—shifting from algorithmic extraction to collective care.