Referral Required: When AI Blocks Access to Human Doctors

As AI systems evolve from support tools to decision-making elements in clinical workflows, a pivotal shift is occurring in how care is accessed. This paper considers a speculative but increasingly plausible scenario: what if seeing a human physician in the future requires a referral from an AI system? Just as insurers gate access to specialists through primary care referrals, we envision AI care access systems—powered by large language models (LLMs)—serving as arbiters of whether patients may proceed to any human clinician. We examine risks along this path—particularly around opacity, inequity, and denial at scale—and propose a roadmap for building systems that support explainability, appealability, and ethical oversight. We introduce the concept of medicolegal LLMs: agents optimized not only for accuracy, but for accountable refusal. Finally, we outline a system-level blueprint for trustworthy AI care access decision systems, integrating evidence grounding, uncertainty quantification, transparent policies, audit logging, and responsive escalation to ensure that automation supports rather than obstructs access to care.