Lloyd Price
- Mar 24

Personal Health LLM's to emerge in HealthTech 2024?

Exec Summary:

Personal health large language models (LLMs) are an exciting new area in healthcare that uses AI to provide users with personalised guidance and support. Here's a breakdown of how they work and their potential:

What exactly are Personal health LLMs?

LLMs are essentially AI models trained on massive amounts of health data and text. This allows them to understand and respond to your questions and concerns in a way that's relevant to you.

What can Personal health LLMs do?

Health Coaching: Imagine a virtual coach that can analyze your health data (with your permission, of course) and provide personalized recommendations for diet, exercise, and overall well-being.
Health Information: LLMs can translate complex medical jargon into easy-to-understand language and answer your questions about specific conditions or treatments.
Improved Communication: LLMs can help bridge the gap between patients and doctors by facilitating communication and streamlining administrative tasks.

Benefits of Personal health LLMs:

Better Understanding: LLMs can empower patients to take charge of their health by improving their understanding of their conditions and treatment options.
Increased Engagement: A personalized approach can motivate users to stay on track with their health goals.
Clinician Support: LLMs can free up doctors' time by handling routine tasks and providing patients with initial guidance.

Early days of Personal health LLMs:

Early Days: Personal health LLMs are still under development, and their capabilities are constantly evolving.
Not a Replacement: LLMs should not be seen as a replacement for qualified medical professionals.
Data Privacy: Security and privacy of personal health data is paramount.

Overall, personal health LLMs have the potential to revolutionize healthcare by providing accessible, informative, and personalized support to users.

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FitBit and Google Research:

'Fitbit and Google Research are working together to build a Personal Health Large Language Model that can power personalized health and wellness features in the Fitbit mobile app, helping people get even more insights and recommendations from the data from their Fitbit and Pixel devices.

This model is being fine-tuned to deliver personalized coaching capabilities, like actionable messages and guidance, that can be individualized based on personal health and fitness goals. For example, this model may be able to analyze variations in your sleep patterns and sleep quality, and then suggest recommendations on how you might change the intensity of your workout based on those insights.

This model is being built on Gemini models and fine-tuned on a de-identified, diverse set of health signals from high-quality research case studies. The studies are being collected and validated in partnership with accredited coaches and wellness experts, enabling the model to exhibit profound reasoning capabilities on physiological and behavioural data.

For example, we’re testing performance using sleep medicine certification exam-like practice tests, and are already seeing that our model currently performs well. We'll continue to iterate and learn as we build this new Personal Health Large Language Model, and will share more research soon.'

Source: https://blog.google/technology/health/google-generative-ai-healthcare/

InteliHealth's personalised health plan

InteliHealth is a US-based company that uses LLMs to create personalised health plans. Patients can use the InteliHealth website to answer questions about their health and lifestyle. The LLM will then generate a personalised plan that includes recommendations for diet, exercise, and medication.

What InteliHealth offers:

Interactive Platform: You interact with InteliHealth through their website or app.
Health Assessment: You answer a series of questions about your health history, lifestyle habits (diet, exercise, sleep), and any current health concerns.
LLM Analysis: InteliHealth's LLM analyzes your responses, considering vast amounts of healthcare data and research.
Personalized Plan: Based on the analysis, the LLM generates a personalized health plan with recommendations for:
Diet: The plan might suggest specific foods or dietary patterns based on your needs and goals.
Exercise: You might receive recommendations for types and duration of exercise routines.
Medication (with disclaimers): In some cases, the plan might suggest over-the-counter medications but with a strong emphasis on consulting a doctor for confirmation and prescriptions.

Important Considerations:

Early Stage: InteliHealth's LLM technology is still under development, so its recommendations should be viewed as a starting point, not a definitive diagnosis or treatment plan.
Doctor Consultation: InteliHealth emphasizes the importance of consulting a doctor before making any major changes to your diet, exercise routine, or medication use. The LLM cannot replace a qualified medical professional.
Data Privacy: When using InteliHealth, make sure you understand their data privacy practices to ensure your health information is secure.

Overall, InteliHealth's LLM-based personalized health plans offer a convenient and potentially helpful way to take charge of your well-being. However, it's crucial to remember it's a tool to be used in conjunction with consulting a doctor for personalised medical advice.

Future of personal health LLMs

The future of personal health LLMs (large language models) is brimming with exciting possibilities that could transform how we manage our health. Here's a glimpse into what the coming years might hold:

Advanced Capabilities:

Precision Medicine: LLMs could be fine-tuned to analyze an individual's unique genetic makeup, health history, and lifestyle data to provide hyper-personalized recommendations for disease prevention, treatment, and overall well-being.
Mental Health Support: LLMs could offer more sophisticated mental health chatbots trained to recognize and address symptoms of anxiety, depression, and other mental health conditions. They could provide emotional support, guide users to relevant resources, and even flag potential situations requiring professional intervention.
Proactive Health Management: Imagine an LLM constantly monitoring your health data from wearables and other sources, identifying potential health risks early on and suggesting preventive measures.

Integration and Collaboration:

Seamless Healthcare Ecosystem: LLMs could seamlessly connect with electronic health records, wearables, and other healthcare devices, creating a unified platform for managing your health data and receiving personalized insights.
Enhanced Doctor-Patient Communication: LLMs could act as intelligent assistants for doctors, summarizing patient data, translating medical jargon for patients, and facilitating more informed conversations.

Addressing Challenges:

Data Privacy and Security: Robust security measures will be crucial to ensure the privacy of sensitive health data used by LLMs.
Algorithmic Bias: Mitigating bias in the data used to train LLMs is essential to prevent unfair or inaccurate recommendations.
Accessibility and Equity: Efforts must be made to ensure everyone has access to personal health LLMs, regardless of socioeconomic background or technical expertise.

Overall, the future of personal health LLMs is promising. As these AI models become more sophisticated and integrated into healthcare systems, they have the potential to empower individuals, improve preventative care, and revolutionise the way we approach our health.