Enabling Physicians to Build Custom Conversational AI Agents for Cohesive Understanding of Medical Knowledge to Support Data-Driven Healthcare Decision-Making
Healthcare knowledge graphs are sophisticated structures that integrate various medical concepts and data sources through Natural Language Processing (NLP) techniques. These graphs enable a cohesive understanding of complex healthcare data by connecting disparate elements such as symptoms, diseases, treatments, and patient information. They are instrumental in supporting data-driven decision-making by identifying relationships and patterns within the data. This presentation will demonstrate how physicians can utilize large language models (LLM) and healthcare knowledge graphs to create conversational AI agents.
These agents will have a comprehensive understanding of specific medical domains, enhancing data-driven decision-making in areas like symptom analysis, disease diagnosis, treatment recommendation, and patient data review. The speaker will guide the audience through an end-to-end example of developing a custom virtual assistant for a medical specialty using open-source tools. This assistant will be capable of comprehending medical language, gathering patient details through dialogue, and providing insights to physicians by reasoning over the encoded knowledge graph.
The presentation will cover the next steps in deploying and optimizing such assistants. The aim is to equip physicians with the practical skills needed to build AI assistants that supplement their expertise in data-driven medicine. The construction of healthcare knowledge graphs involves the collection and processing of diverse healthcare data using NLP techniques. This is followed by the creation of the graph, which connects entities based on their relationships as identified through NLP analysis. These graphs are then applied in various domains, including predictive analytics and decision support systems. Conference participants will gain practical insights into leveraging AI and knowledge graphs for a comprehensive understanding of healthcare information, fostering more informed decision-making.
The presentation will also address the challenges in constructing these graphs, such as ensuring data accuracy and maintaining the privacy and security of patient data, thereby providing valuable insights and guidance for professionals in the field.
Ali Lazim
CEO at Medixbot
Ali Lazim is a visionary technologist and an accomplished interdisciplinary engineer, recognized for his expertise in artificial intelligence and serial entrepreneurship. With a Master’s degree in AI and over 25 years of experience in pioneering new technologies across biotech, healthcare devices, AI/ML and cybersecurity, Ali has made significant contributions to the tech industry. As a transformational tech leader and a serial entrepreneur, he has founded four R&D startups, with two achieving successful exits. His impressive career also includes holding two patents and leading technology development for industry leaders. Ali’s current endeavors include driving healthcare and technological advancements and cultivating entrepreneurship, with a focus on fostering global collaboration. He is at the forefront of his healthcare startup, Medixbot, striving for unicorn status within 5 years by pioneering FDA-cleared innovations to transform medicine. Ali’s commitment to innovation extends to his roles as an academic advisor and mentor, where he supports entrepreneurs, advises leadership programs, and pushes impactful technologies. His work aims to substantially benefit the USA and create strong innovation ties between the USA, Central Asia, and the MENA region.
