Conference Presentation, Keynote
When Biology Moves to Engineering
- AI is expected to diagnose conditions like cancer with 90% or greater accuracy from blood samples and predict atrial fibrillation from wearable cardiogram data with 97% accuracy.
- Future healthcare is projected to shift toward an "augmented intelligence" model where doctors and computers collaborate rather than computers replacing medical professionals.
- Biology and healthcare are anticipated to transition from being limited by human understanding to being managed through engineering and AI tools.
- Bioengineers utilizing Cello software are expected to predict biochemical circuit accuracy with 90% precision, with manual construction of three circuits yielding 999 out of 1,000 functional units.
- Engineering protein-producing cells to be just 10% more efficient could generate approximately $200 million in annual savings for a company like Genentech.
- Applying circuit design to cell therapies aims to evolve the field from primitive hand-engineering to a sophisticated roadmap comparable to the microprocessor evolution from the 4004 to the Pentium or Z10.
- Digital therapeutics are expected to undergo weekly iterative improvements via A-B testing, contrasting with traditional drugs that require new clinical trials for each iteration.
- Omada's behavioral therapy is predicted to demonstrate efficacy exceeding metformin for type 2 diabetes, with therapeutic efficacy increasing continuously over time due to data network effects.
- Patient Ping is expected to reduce healthcare waste by using transparent messaging to steer patients toward cost-effective treatments like physical therapy instead of emergency rooms.
- Data generated by Patient Ping is anticipated to facilitate the development of new applications and features built upon the patient location and treatment knowledge network.
- Slowing the aging process by 50% is expected to delay the onset of Alzheimer's disease by 40 years, while a 100% slowdown could delay it by 80 years.
- BioAge plans to employ machine learning to analyze young blood samples to identify healing properties for new diagnostics and therapeutics, avoiding the risks associated with distributing young human blood to older humans at scale.
- Many areas currently defined by scientific risk are expected to become solvable engineering problems through the application of machine learning and engineering mechanisms.
- Engineers are expected to finally address the accumulated "technical debt" of biology using these new engineering approaches.