# 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
These systems leverage ML models that have been trained on large datasets of medical records and outcomes, allowing them to provide accurate diagnostics and assist clinicians in making data-driven decisions. Figure 5 shows an example of the retrospective assessment of patients enrolled in the University of California, Los Angeles (UCLA) Center for IBD electronic care management platform (UCLA eIBD). The UCLA eIBD platform (ver. 1.4) is a software-as-a-service solution that provides a web-based interface for providers, featuring treatment decision support, business intelligence, messaging functionality, and performance improvement tools. For patients, the platform includes a mobile app offering care management insights, educational modules, surveys, and messaging capabilities [78]. Several examples of LLM-based chatbot applications in healthcare demonstrate their growing utility in clinical settings:  
Buoy Health is an AI-powered chatbot used for symptom checking [79]. It allows users to input their symptoms and receive a preliminary assessment of their condition. The chatbot uses natural language processing to ask follow-up questions and guide users to the  
Figure 5: Overview of the UCLA eIBD platform. AI: artificial intelligence; API: application programming interface. All patients. Reproduced from reference [78] under the Creative Commons Attribution 4.0 International License ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)) (accessed on 24 January 2025)). appropriate next steps, whether it is suggesting self-care, seeking urgent care, or consulting a healthcare professional. Buoy Health has been integrated into various healthcare systems, helping to reduce the burden on healthcare providers by offering patients immediate advice [80]. Babylon Health's chatbot provides virtual consultations by combining AI with human medical professionals [81]. The chatbot helps patients with symptom checking, health monitoring, and medical advice, using ML models to assess patient inputs and determine the likelihood of various conditions. It also integrates with telemedicine services, allowing patients to connect with healthcare providers for further consultation. IBM Watson has developed AI-driven chatbots for healthcare that assist both patients and clinicians [82]. Watson for Health is capable of engaging in conversations, reviewing patient records, and providing evidence-based recommendations to healthcare providers. The system can also assist patients by providing tailored health information, answering medical questions, and supporting chronic disease management [83]. The Mayo Clinic has developed a chatbot that interacts with patients to provide health information, including advice on conditions, symptoms, and treatments [84, 85]. It also helps with appointment scheduling and medication reminders. The chatbot integrates with the Mayo Clinic's database, allowing it to provide accurate and up-to-date information tailored to individual patient needs. Table 1 shows a comprehensive summary of various applications in medical decision-making, highlighting the integration of QC and ML technologies. Each application is described along with its potential impact on healthcare, ranging from drug discovery and radiotherapy to genomic data analysis and personalized medicine.