Computer scientist and AI researcher building Health.AI to validate medical truth through artificial intelligence. Combining 40+ years of technical innovation with deep research in gerontology and synthetic biology to solve healthcare's most fundamental problem: access to reliable, evidence-based medical knowledge.
Explore Health.AI Connect with MichaelMichael Greenhalgh is a computer scientist and AI researcher working at the intersection of artificial intelligence, longevity science, and evidence-based medicine. With Stanford training in Natural Language Processing and AI (2016-2017) and foundational work in computer science and linguistics from Sonoma State University, he brings a rigorous, first-principles approach to solving healthcare's most challenging problems.
As co-founder of Health.AI, Michael is building systems that validate medical information through multi-agent AI analysis, cutting through the noise of conflicting health advice to surface evidence-based truth. His vision: create transparent, open-access tools that function as a "ground truth engine" for healthcare—helping both patients and professionals navigate the reality that nearly 50% of current medical protocols are invalid, incomplete, or influenced by hidden conflicts of interest.
Michael's technical career spans four decades of building innovative companies—from co-founding IMS in 1984 (creating a programming language in assembler for distributed RDBMS systems), to designing metropolitan-area wireless internet infrastructure with Metro.Net and SAIC, to managing global network implementations for Wells Fargo's 5,000 branches. Each venture solved complex technical challenges through elegant system design.
Over the past decade, Michael shifted his focus to the hardest problem he'd encountered: healthcare information accuracy. As Clinical Director of Pacific Hyperbarics and co-owner of neurorehabilitation clinics across California, he witnessed firsthand how fragmented medical knowledge and physician overload impact patient outcomes. This clinical experience became a testbed for developing intelligent systems that could synthesize medical literature, detect logical inconsistencies, and identify non-obvious conflicts of interest.
His current research explores the convergence of AI, gerontology, and synthetic biology from a data science perspective. Michael believes recent advances in AI now make it possible to approach "true optimum health" by creating systems that can process global medical knowledge sources, break through AI guardrails imposed by economic influences, and validate diagnoses and treatments through rigorous multi-step truth algorithms.
Education: Stanford University (NLP & AI, 2016-2017) • Sonoma State University (Computer Science, Computer Linguistics, Philosophy, 1974-1981) • Thesis: ArpaNET as collective intelligence evolution
Health.AI addresses a fundamental problem: nearly 50% of medical diagnoses and treatments are invalid, undetected, or unused. Michael is building a suite of AI-powered tools that validate medical information through rigorous multi-agent analysis, creating what he calls a "7-step truth algorithm" to assess logic, identify conflicts of interest, analyze biological mechanisms, and synthesize agnostic global knowledge sources.
The platform combines Natural Language Processing, multi-agent AI systems, and deep medical literature analysis to break through AI guardrails imposed by economic influences at every level of healthcare—from pharmaceutical research to clinical protocol development. Rather than replacing physicians, Health.AI augments human intelligence with transparent, evidence-based validation.
Michael's approach is methodical: build practical tools that solve real problems while advancing the broader mission of healthcare truth validation. The result is a growing ecosystem of applications—from personal health assistants and supplement comparators to clinical workflow automation and medical content verification systems—all designed to make reliable health knowledge universally accessible.
Current Focus Areas: AI-driven diagnostic validation • Medical information truth-checking • Longevity science applications • Clinical workflow optimization • Patient-centered health intelligence
A comprehensive ecosystem of AI-powered tools addressing healthcare's most critical challenges
Personalized health intelligence platform with document scanning, multilingual support, and wearable integration. Provides evidence-based medical advice with full references and early warning predictions.
Multi-agent AI system that analyzes health videos and content for accuracy, providing truth confidence ratings. Combats medical misinformation at scale.
Science-backed supplement analysis engine that compares products across retailers, validates manufacturer claims, identifies interactions, and cuts through marketing noise with transparent data.
Simulated conversations between medical experts from different specialties, allowing users to see multiple perspectives on complex health decisions and treatment approaches.
Prescription analysis, medical fax processing, and clinic management tools that reduce administrative burden and improve accuracy in clinical workflows.
Privacy-first health AI that runs locally on user devices, analyzing personal health emails and documents to provide completely personalized, confidential health guidance.
Additional Projects in Development: LifeCode longevity community platform • Agent Zero knowledge validation game • AI debate platform for medical research • Health education certification system • Autonomous content creation tools • Blockchain-based health rewards economy
After selling his first startup and achieving financial independence, Michael spent a decade diving deep into gerontology and synthetic biology from a data science and systems theory perspective. This wasn't casual interest—it was a systematic investigation of aging mechanisms, cellular senescence, epigenetic reprogramming, and the fundamental biological constraints on human lifespan.
His research approach combines computational analysis of longevity literature with practical clinical experience from years operating hyperbaric oxygen therapy clinics. HBOT has demonstrated remarkable results in cellular regeneration, neurological recovery, and wound healing—outcomes that align with emerging longevity science showing the importance of oxygen metabolism, mitochondrial function, and cellular stress responses.
Michael's longevity work operates on multiple levels:
The last two years of AI advancement have been transformative for this work. Michael believes we're approaching a critical inflection point where AI systems can finally process the full complexity of human biology—integrating genomics, proteomics, metabolomics, and clinical outcomes data to identify genuine longevity interventions versus marketing hype.
His vision: combine Health.AI's truth validation capabilities with comprehensive longevity knowledge to create practical, evidence-based protocols for extending healthspan. Not just living longer, but maintaining cognitive function, physical vitality, and quality of life through interventions validated by rigorous analysis of global health data.
Research Areas: Gerontology & cellular senescence • Synthetic biology applications • Hyperbaric oxygen therapy mechanisms • Metabolic optimization • Epigenetic reprogramming • AI-driven protocol validation • Healthspan extension strategies
Michael’s life beyond health tech reflects his love for family, travel, and meaningful connections.
Behind Michael’s success stands Emilia, his unwavering partner in life’s journey.
Michael actively mentors and educates individuals, working to build a stronger, more empowered society.
An avid traveler, Michael explores the world, drawing inspiration for his work.
Michael’s thought leadership in health tech is captured in these technical whitepapers on AI and healthcare.
Michael shares thoughts on the mission for health, technology, and life, including his top book recommendations.
"Michael's approach to medical AI is refreshingly rigorous. Rather than treating AI as a black box, he's built systems that explicitly reason about evidence quality, conflicts of interest, and biological mechanisms. His multi-agent validation framework addresses problems most healthcare AI companies ignore."
"What strikes me about Michael is his systems thinking. He doesn't just build isolated tools—he's architecting an ecosystem where truth validation, clinical workflows, and patient empowerment reinforce each other. That kind of holistic design is rare in health tech."
"Michael understands something crucial: the problem isn't lack of medical data, it's lack of validated synthesis. His work on using AI to identify contradictions and conflicts of interest in medical literature could fundamentally change how we develop clinical guidelines."
"Working with Michael taught me to think about AI safety in healthcare contexts. His insistence on transparency, explainability, and multi-perspective validation isn't just good practice—it's essential for building systems people can actually trust with their health decisions."
We're solving healthcare's hardest problem: validating medical truth at scale. If you're a researcher in AI/ML, medical taxonomy, public health economics, or clinical practice—or an investor who understands the magnitude of rebuilding healthcare information infrastructure—let's connect.
We need expertise in NLP, multi-agent systems, medical ontologies, and longevity research. Join us in building AI systems that can reason about medical evidence.
Physicians, public health experts, and clinical researchers who see the gaps in current medical information systems. Help us validate approaches and ensure clinical relevance.
Strategic partners who understand that fixing healthcare information infrastructure is a generational opportunity with massive impact potential.
