Optimal Medical Liability for AI

I study medical liability when artificial intelligence acts as a doctor rather than as a passive clinical tool.The central object is the legally usable medical record: the inputs, logs, warnings, prescriptions, follow-up instructions, and outcomes on which courts, contracts, insurers, and regulators can condition responsibility.I show that AI medical liability is an institutional design problem under imperfect legal information.If the record separates AI-controllable error from patient nonadherence and natural disease progression, high-powered AI-fault liability implements the standard accident-law ideal.If the record is coarse, the first best may be infeasible: the same transfer that disciplines the AI also insures the patient's hidden action.With joint causation, the relevant object is a marginal-responsibility score rather than a posterior cause label.I characterize the feasible set of liability incentives generated by the record and show when the optimal rule is no liability, strict liability, negligence, a safe harbor, comparative fault, or a continuous warranty.I then study algorithmic defensive design, through which AI developers can design not only medical recommendations but also the record on which future liability depends.Adoption, learning, enterprise liability, insurance, no-fault compensation, and regulation enter as ways to change the record, the liable entity, or the financing of compensation.The framework yields conditional implications rather than a one-size-fits-all rule.