Agentic Ai In Healthcare: A Review Of Goal-Oriented Healthcare Assistants With Symptom Guidance, Medication Adherence And Safe Escalation

Healthcare systems increasingly use artificial intelligence to improve patient support, reduce manual effort and assist decision-making. Agentic AI differs from systems that only answer queries: an agent pursues goals, plans multi-step actions, uses tools such as databases and reminders and coordinates several functions to help a user manage health over time. This paper reviews agentic AI for consumer healthcare assistance. It examines the components of large-language-model agents, namely planning, tool use, memory and reflection, and the use of retrieval-augmented generation to ground answers in trusted medical sources. It reviews evidence on the clinical knowledge of large language models, their limitations including hallucination, studies of the accuracy and safety of symptom checkers, conversational diagnostic systems, and interventions that improve medication adherence. A reference architecture for a healthcare assistant is proposed with five layers: user and authentication, agent core with planning and memory, tool layer providing symptom guidance, medicine reminders, report storage and emergency information, a safety and escalation layer enforcing scope limits and red-flag detection, and an interface layer. Research gaps, objectives, a comparison of assistant capabilities and challenges such as accuracy, liability, privacy and digital literacy are discussed. The review emphasizes that such assistants must provide preliminary information only and direct users to qualified professionals.