Medical consultation using llms involves the use of large language models (LLMs) for providing effective patient care. LLMs can be integrated with existing electronic health record (EHR)systems to enhance patient experience during online consultations. For example,LLMs can be used to generate more personalized responses to patient inquiries by incorporating details from their medical history. They can also provide tailored medical advice by retrieving information from reputable health resources. Several approaches have been proposed to optimize LLMs for medical use. These include refining general language models using datasets of real patient-doctor dialogues, enabling the models to self-retrieve information from medical knowledge bases, and integrating them with computer-aided diagnosis systems. Studies show that optimizing and augmenting LLMs with medical knowledge can significantly enhance their ability to understand patient needs, provide accurate medical advice, and generate high-quality medical reports. While LLMs have shown promising potential in medical consultations, they still face limitations in providing nuanced clinical judgments and recommendations that account for patient circumstances. They may generate responses with variable levels of accuracy that need to be interpreted carefully based on the medical context. LLMs also cannot replace physicians in situations requiring in-person evaluation or intervention. Close monitoring and oversight by medical experts are necessary to ensure the safe and ethical use of LLMs for patient care. Further research on enhancing the medical capabilities of LLMs may involve developing reasoning pathways that can match human clinical decision making, incorporating empathy and emotional intelligence, handling a wider range of medical conditions and specialties, and generating responses in different languages. Using offline and online data from patient interactions can continue to strengthen LLM proficiency for medical consultations. With continuous progress, LLMs can serve as a useful adjunct to physicians in providing more efficient, personalized and round-the-clock patient care.