Long before the current hype, a 2005 AI project used electronic medical records and genomic data to find children with an ultra-rare disease who would have otherwise died before age 10. This highlights AI's long-standing, life-saving impact beyond recent commercial applications.

Related Insights

The speaker regrets not using AI to guide a physical exam of his son. A key diagnostic breakthrough occurred when a doctor found a specific point of pain on his son's abdomen. This suggests a powerful, untapped use case for AI in helping patients or caregivers identify crucial physical symptoms that might otherwise be missed.

The most effective AI strategy focuses on 'micro workflows'—small, discrete tasks like summarizing patient data. By optimizing these countless small steps, AI can make decision-makers 'a hundred-fold more productive,' delivering massive cumulative value without relying on a single, high-risk autonomous solution.

AI serves as a powerful health advocate by holistically analyzing disparate data like blood work and symptoms. It provides insights and urgency that a specialist-driven system can miss, empowering patients in complex, under-researched areas to seek life-saving care.

The speaker faced two simultaneous crises: his son's cancer and a basement flood. AI was able to bridge the knowledge gap between oncology (immunosuppression risks) and mold remediation (air quality management), providing a synthesized, actionable plan that no single human expert could realistically offer.

Early researchers were overwhelmed by the massive, chaotic changes in gene expression in sepsis patients, terming it a "genomic storm." Inflammatics' founders viewed this complexity not as an obstacle but as a rich dataset. By applying advanced computational analysis, they identified specific, interpretable signals for diagnosis and prognosis.

Chronic disease patients face a cascade of interconnected problems: pre-authorizations, pharmacy stockouts, and incomprehensible insurance rules. AI's potential lies in acting as an intelligent agent to navigate this complex, fragmented system on behalf of the patient, reducing waste and improving outcomes.

A Chinese hospital's AI program is achieving early success not just by detecting cancer, but by screening asymptomatic patients' routine CT scans taken for unrelated issues. This unlocks a powerful and safe method for widespread early screening of dangerous cancers like pancreatic, which was previously unfeasible.

While doctors focused on the immediate, successful treatment, the speaker used AI to research and plan for the low-probability but high-impact event of a cancer relapse. This involved proactively identifying advanced diagnostics (ctDNA) and compiling a list of relevant clinical trials to act on immediately if needed.

By continuously feeding lab results and treatment updates into GPT-5 Pro, the speaker created an AI companion to validate the medical team's decisions. This not only caught minor discrepancies but, more importantly, provided immense peace of mind that the care being administered was indeed state-of-the-art.

A major frustration in genetics is finding 'variants of unknown significance' (VUS)—genetic anomalies with no known effect. AI models promise to simulate the impact of these unique variants on cellular function, moving medicine from reactive diagnostics to truly personalized, predictive health.