Toward human–AI symbiotic medicine: a symbiotic clinical compact for the era of medical agents

Medical artificial intelligence (AI) is evolving from algorithms that perform isolated predictive tasks into medical agents capable of continuously perceiving clinical context, decomposing goals, invoking tools, coordinating communication, and tracking outcomes. This transition shifts the central clinical question from whether AI can replace physicians to how patients, physicians, agents, and healthcare institutions can jointly form judgments, authorise actions, and assume responsibility for their consequences. Existing evidence indicates that large language models (LLMs) can perform strongly in medical question answering, differential diagnosis, and management reasoning. Yet “a model outperforms a human” does not mean that “a human–AI team outperforms a human alone”: randomised trials and imaging studies have exposed systemic risks arising from interaction design, automation bias, error amplification, diffusion of responsibility, and erosion of professional competence. We propose the Symbiotic Clinical Compact (SCC), comprising four interrelated commitments—shared intelligence, bounded action, patient co-governance, and traceable accountability—to define the rights, authority, and responsibilities of patients, physicians, agents, and healthcare systems. We further introduce the FADA clinical loop—Frame, Analyse, Deliberate, and Authorise—together with a four-stage translational pathway extending from human review to human–AI symbiotic medicine. Human–AI symbiotic gain (HASG) and symbiosis-related harm (SRH) are proposed as dual-anchor evaluation endpoints. The SCC does not grant agents unrestricted authority to practise medicine. Instead, it embeds task-level autonomy within revocable authorisation, risk stratification, continuous monitoring, and institutional accountability. Future research should move beyond static model validation towards real-world evaluation of integrated human–AI systems, and beyond average accuracy towards patient outcomes, subgroup fairness, relational quality, professional competence, and lifecycle safety. The goal of human–AI symbiotic medicine is to enable agents to manage informational and procedural complexity, physicians to focus on judgment, responsibility, and compassionate care, patients to become genuine partners in medical decision making, and healthcare systems to evolve from episodic service providers into continuously learning, traceable health partners.

Key Words: medical agents; large language models; human–AI collaboration; shared decision making; patient autonomy; clinical governance; traceable accountability; artificial intelligence ethics