Large Language Models as Physician Recommenders: Limitations, Alternatives, and the Path Toward Hybrid AI Systems

Digital tools are increasingly used by patients to access health information and navigate care, including choosing clinicians, but the evidence supporting "best doctor" recommendations varies widely across available methods. This manuscript compares the emerging use of consumer-facing large language models (LLMs) for this purpose with four alternative physician-selection strategies: patient networks, expert referrals, consumer rating/search platforms, and outcome-informed artificial intelligence / machine learning (AI/ML) recommender systems. Consumer rating platforms mainly reflect patient experiences rather than technical skills and often show weak or inconsistent links with objective performance measures. General-purpose LLMs may produce convincing rationales that highlight visibility and reputation, but they remain limited by hallucinations, prompt sensitivity, and uncertainty about whether their explanations truly reflect the underlying reasoning. Outcome-informed AI/ML recommender systems could, in theory, incorporate risk-adjusted clinical performance, but they face significant challenges, including unreliable physician-level measurement, incomplete data, residual confounding, and limited transparency, which can undermine trust and informed consent. We suggest that a hybrid system should be seen as a guiding concept and a testable approach rather than a final solution. Such a system could integrate proven, auditable performance measures with transparent patient-preference filters and verifiable, patient-facing explanations. We present key design principles, including clinical relevance, explainability, autonomy, equity, and governance, in line with current US Food and Drug Administration (FDA) guidance related to clinical decision support and adaptive AI life cycle management.