The Impact of Artificial Intelligence, Machine Learning, and Large Language Models on Your Health and Wellness With Dr. Renee Deehan

In this episode of Longevity by Design, Dr. Gil Blander talks with Dr. Renee Deehan, Senior Vice President of Science and AI at InsideTracker. They explore the world of artificial intelligence and its applications in health and wellness.

Renee explains the differences between AI, machine learning, and large language models (LLMs). She discusses how InsideTracker has developed a specialized LLM called Ask InsideTracker. This tool allows users to interact with a vast knowledge base of health and wellness information.

The conversation delves into the potential future applications of AI in personalized health recommendations. Renee emphasizes the importance of data privacy and security when developing these tools. She also shares her perspective on the broader impact of AI in various industries and its potential to enhance decision-making processes.

Episode highlights:

• Introduction: 00:00:00
• Defining AI and Machine Learning in Simple Terms: 00:04:00
• AI is a branch of computer science: 00:05:00
• The Power and Limitations of Large Language Models: 00:08:00
• Developing Specialized AI Tools for Health and Wellness: 00:25:00
• GPT general foundational model: 00:26:00
• The Future of AI in Personalized Health Recommendations: 00:37:00
• A balance between nutrition, exercise, and recovery: 00:47:00
• The best habit that you can do: 00:48:00

Specialized LLMs Enhance Health Information Accuracy

LLMs tailored to specific domains can significantly improve the quality and reliability of information provided to users. By constraining general AI models with specialized knowledge, such as InsideTracker's corpus of health and wellness blog posts, these tools can deliver more accurate and relevant answers. This approach helps filter out noise and misinformation often found in general internet searches. For health and wellness applications, specialized LLMs can offer personalized insights based on high-quality, expert-reviewed content. This method bridges the gap between vast amounts of available information and individual user needs, potentially revolutionizing how people access and understand health-related information.

AI in Health: Balancing Personalization and Privacy

The integration of AI in health and wellness platforms promises highly personalized recommendations but raises important privacy concerns. As these systems evolve to incorporate individual health data, such as biomarkers and lifestyle information, the potential for tailored advice increases dramatically. However, this advancement requires robust security measures to protect sensitive personal information. The challenge lies in creating AI systems that can access and analyze personal health data while maintaining strict privacy standards. Striking this balance is crucial for the widespread adoption and trust in AI-powered health tools, potentially transforming how individuals manage their health and make lifestyle decisions.

Continuous Refinement Key to AI Tool Effectiveness

The development and improvement of AI tools, particularly in health and wellness, require ongoing refinement based on user interactions and feedback. This iterative process involves analyzing user queries, identifying areas of improvement, and continuously updating the AI model. By studying how users interact with the tool and the types of questions they ask, developers can enhance the AI's ability to provide relevant and accurate information. This approach ensures that AI tools evolve to meet user needs more effectively over time. The continuous refinement process is crucial for maintaining the tool's reliability and relevance, especially in rapidly evolving fields like health and wellness.

For science-backed ways to live a healthier longer life, download InsideTracker's Top 5 biomarkers for longevity eBook at insidetracker.com/podcast