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Related Experiment Video

Updated: May 5, 2026

High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity
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Who's really in the loop? Rethinking oversight in AI-assisted health care.

Rawan Abulibdeh1, Gift Osei Agyemang2, Leo Anthony Celi3

  • 1Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada; Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA.

Lancet (London, England)
|May 3, 2026
PubMed
Summary

Human-in-the-loop oversight in healthcare AI offers false security. Current models amplify inequities and obscure institutional blame, necessitating new accountability frameworks for safer AI deployment.

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Area of Science:

  • Health Informatics
  • Artificial Intelligence Ethics
  • Sociology of Technology

Background:

  • Human-in-the-loop (HITL) oversight is a common safeguard for artificial intelligence (AI) in healthcare.
  • However, its effectiveness in preventing harm is questioned, often serving as symbolic reassurance rather than substantive protection.

Purpose of the Study:

  • To critically evaluate the efficacy of HITL oversight in healthcare AI.
  • To identify systemic failures in current oversight models.
  • To propose alternative accountability frameworks for AI in healthcare.

Main Methods:

  • Analysis drawing upon actor-network theory, feminist epistemology, and Iris Marion Young's social connection model of justice.
  • Examination of how current governance structures individualize responsibility and obscure institutional complicity.
  • Identification of three key reasons for HITL failure: amplification of inequities, elusion of intersectional harms, and clinician constraints.

Main Results:

  • HITL oversight fails to provide substantive protection against AI harms in healthcare.
  • Existing AI systems can amplify structural inequities at scale.
  • Oversight models struggle to detect intersectional harms, and clinicians face constraints limiting meaningful algorithmic review.

Conclusions:

  • Current HITL models individualize responsibility, masking organizational complicity in AI harms.
  • Proposed solutions include co-reasoning frameworks, community-owned governance, and institutional liability structures.
  • These pathways aim to shift accountability from individual clinicians to organizations designing and deploying AI.