Federal AI policy prioritizes rolling back safeguards, fast-tracking private-sector-led innovation, and banning diversity initiatives, producing lasting impacts on medical practice, public health governance, and patient equity. Government actions have removed data, cut research funding for marginalized groups, and pressured scientists to suppress politically inconvenient findings, shaping what is measured and published. Those constraints are migrating into AI development, creating incentives for developers to select designs and datasets that avoid political scrutiny. The resulting technical choices—encoded in algorithms, protocols, and deployed broadly—risk cementing contemporary biases into clinical tools, making harms persistent and difficult to reverse. AI already permeates many medical tasks.
The White House's AI Action Plan, released in July, mentions "health care" only three times. But it is one of the most consequential health policies of the second Trump administration. Its sweeping ambitions for AI-rolling back safeguards, fast-tracking " private-sector-led innovation," and banning "ideological dogmas such as DEI" -will have long-term consequences for how medicine is practiced, how public health is governed, and who gets left behind.
Already, the Trump administration has purged data from government websites, slashed funding for research on marginalized communities, and pressured government researchers to restrict or retract work that contradicts political ideology. These actions aren't just symbolic-they shape what gets measured, who gets studied, and which findings get published. Now, those same constraints are moving into the development of AI itself. Under the administration's policies, developers have a clear incentive to make design choices or pick data sets that won't provoke political scrutiny.
These signals are shaping the AI systems that will guide medical decision making for decades to come. The accumulation of technical choices that follows-encoded in algorithms, embedded in protocols, and scaled across millions of patients-will cement the particular biases of this moment in time into medicine's future. And history has shown that once bias is encoded into clinical tools, even obvious harms can take decades to undo-if they're undone at all.
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