What Startup Biotech & Life Sciences Presidents Are Really Thinking

• •deriving meaning and actionability from genomic data
• •finding disease early before symptoms appear
• •improving positive predictive value of blood tests
• •building effective teams with limited resources
• •enabling crops to communicate their needs through genetic modification

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• •lack of clear guidance for doctors on what to do with new blood tests
• •multiple cancer types require multiple solutions, not one-size-fits-all drug discovery
• •eye infections can lead to blindness globally and require hospitalization
• •high false positive rates in existing diagnostic methods like mammography
• •misinterpretation of genetic test results leading to patient harm

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• •80 to 97% chance of survival - early cancer detection compared to 30-40% or <20%
• •accuracy/precision improvements from ml model predictions on re-engineered drugsNew
• •adoption of co-pilot tools across research, communications, and coding functions
• •cost of interpreting each variant ($50 to $100)
• •cost per genome (roughly $1,000, going down to hundreds)

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• •access lens: ensuring solutions are 'accessible to all'
• •agnostic approach across entire genome - using ml to analyze vast datasets without specific hypotheses
• •ai-based diagnostic learning: allowing ai to learn from post-launch test results to improve accuracy
• •build vs. deploy decision - if 70-80%+ of ongoing technological development benefits us anyway, use existing solution; only build if we achieve 10x better
• •clinical informativeness: providing clear guidance for doctor action

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• •adding costs to farmer operations rather than replacing existing expenditures
• •ai output used without human verification in high-truth-accuracy environments
• •assuming dna is static and deterministic
• •clinical trials not large enough to account for edge cases
• •comorbidities like amputations and blindness from diabetes

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