ARPA-H’s AI Clinical Trials: Systemic Acceleration of Profit-Driven Health Tech Under Regulatory Cover

Indigenous epistemologies view health as inseparable from land, kinship, and spiritual balance, yet ARPA-H’s AI models operationalize health as a data extractive process. Traditional healers’ knowledge of chronic disease prevention is excluded from training datasets, reinforcing colonial health paradigms. The absence of Indigenous data governance frameworks in ARPA-H’s trials reflects a broader erasure of non-Western healing systems. Without Indigenous oversight, AI tools risk perpetuating the same extractive logics that have historically harmed marginalized communities.

The history of medical experimentation—from Tuskegee to Guatemala—shows how 'innovative' health tech is often weaponized against marginalized groups under the guise of progress. ARPA-H’s AI trials echo 20th-century corporate medicine’s pattern of prioritizing shareholder returns over patient safety. The FDA’s fast-tracking of AI tools mirrors the 1990s push for managed care, where cost-cutting algorithms deepened health inequities. Regulatory capture by tech and pharmaceutical lobbies has deep roots in U.S. health policy, normalizing unchecked innovation.

In Kerala, India, community health workers use low-tech AI to predict outbreaks without corporate intermediaries, contrasting with ARPA-H’s Silicon Valley model. Indigenous Māori health frameworks in Aotearoa (New Zealand) integrate AI for suicide prevention while centering cultural safety, a dimension absent in U.S. trials. The Global South’s emphasis on primary care and prevention highlights the limitations of ARPA-H’s hospital-centric, high-cost approach. Cross-cultural comparisons reveal that AI’s most equitable applications emerge from community-led, not venture-backed, initiatives.

ARPA-H’s AI trials lack transparency in dataset composition, risking algorithmic bias that disproportionately affects Black, Indigenous, and low-income patients. Peer-reviewed studies show FDA-authorized AI tools often fail to generalize across diverse populations, yet the agency continues to prioritize speed over validation. The scientific community has raised alarms about the reproducibility crisis in AI health research, yet regulatory agencies remain complicit in greenlighting unproven tools. Without independent audits, these trials risk becoming case studies in how not to deploy AI in medicine.

Artists like Wangechi Mutu and Kara Walker critique the dehumanization inherent in tech-driven health solutions, framing AI as a tool of aesthetic and ethical violence. Spiritual traditions warn against reducing life to algorithmic prediction, yet ARPA-H’s model embodies this reductionism. The absence of creative or spiritual frameworks in AI health trials reflects a broader cultural dissociation from holistic healing. Indigenous and Afro-diasporic art often visualizes health as a communal, not individual, endeavor—directly opposing ARPA-H’s extractive logic.

If unchecked, ARPA-H’s model could lead to a two-tier health system where AI tools are accessible only to elite hospitals, deepening global inequities. Scenario modeling suggests that without democratic control, AI health systems may prioritize shareholder value over patient outcomes, accelerating privatization. The long-term risks include the erosion of physician autonomy, as algorithms dictate care pathways in real time. Conversely, a community-led AI model could democratize diagnostics, but current trajectories favor corporate consolidation.

Black and Indigenous patients are underrepresented in ARPA-H’s clinical trials, yet their data is harvested to train AI tools that may harm them. Disabled communities, who stand to benefit most from AI-assisted care, are excluded from design processes, reinforcing ableist assumptions. Low-income patients, who lack access to hospitals equipped for AI deployment, are rendered invisible in the narrative. The framing centers venture capitalists and regulators while erasing the voices of those most affected by these tools.