# Quantum Computing and Machine Learning in Medical Decision-Making: A Comprehensive Review
## 3 Applications in Medical Decision-Making
### Large Language Model-Based Chatbots in Healthcare  
The application of large language models (LLMs), such as GPT (Generative Pretrained Transformer) and BERT (Bidirectional Encoder Representations from Transformers), as conversational agents is transforming the landscape of healthcare [66; 67]. LLMs are capable of processing vast amounts of textual data, generating human-like responses, and engaging in meaningful conversations. This has led to their increasing use in various healthcare settings, from patient education and symptom checking to clinical decision support [68]. LLMs like GPT and BERT [69] are deep learning-based models designed to understand and generate human language. GPT, a generative model, can create coherent and contextually appropriate text based on a given input, making it ideal for tasks such as dialogue generation and content creation [70]. BERT, on the other hand, is a transformer model that excels in understanding the context of words within sentences, making it particularly useful for tasks that require comprehension, such as question answering and sentiment analysis. In healthcare, these models serve as conversational agents, capable of interacting with users in natural, human-like conversations. By processing large datasets of the medical literature, clinical guidelines, and patient information, LLMs can generate relevant and accurate responses to healthcare-related queries [71]. These capabilities make LLM-based chatbots powerful tools for enhancing patient engagement, supporting clinical workflows, and improving healthcare accessibility. LLM-based chatbots are increasingly being used in patient education, providing individuals with personalized, accessible information about their conditions, treatments, and medications. These chatbots can answer patients' questions about symptoms, procedures, and preventive care, helping them better understand their health and treatment options [72]. The ability to access healthcare information in real-time enhances patient empowerment, reduces anxiety, and promotes informed decision-making. Another significant application of LLM-based chatbots is in symptom checking. By asking patients a series of questions about their symptoms, these chatbots can assess potential conditions and offer recommendations on next steps, such as scheduling an appointment or seeking emergency care [73]. These chatbots are trained on medical databases and clinical guidelines to provide accurate assessments, acting as a first point of contact for individuals seeking guidance about their health. However, it is crucial to note that these systems are not intended to replace healthcare professionals but rather to supplement them by triaging patients and providing initial advice [74]. In clinical decision support, LLM-based chatbots can assist healthcare providers by offering evidence-based recommendations, assisting in diagnostic processes, and providing treatment suggestions [75]. These chatbots can review patient records, clinical guidelines, and research the literature to offer clinicians up-to-date and relevant information. By integrating with EHRs, chatbots can analyze patient data to suggest potential diagnoses or flag concerns, helping clinicians make more informed decisions quickly and efficiently [76]. One of the key advantages of LLM-based chatbots is their ability to integrate with other ML models for personalized responses and diagnostics. By incorporating patient-specific data, such as medical history, demographics, and previous health conditions, these chatbots can generate responses tailored to the individual. For example, if a patient queries about a treatment option, the chatbot can factor in their specific health conditions, comorbidities, and preferences to offer a personalized recommendation [77]. Furthermore, when integrated with diagnostic algorithms, LLM-based chatbots can help healthcare professionals by providing clinical insights based on the patient's symptoms, lab results, and medical history.