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Clinical Trial Industry Moon shots AI could solve

7ff130a0c1a4966472a4bdfaaf05e791?s=47 Chandi
June 09, 2021

Clinical Trial Industry Moon shots AI could solve

Dear Start-ups! So you have cutting edge AI platform and talent who can solve for complex problems? Here's your invitation – the presentation will walk through the moon shots, mountain shots and flare shots of our industry (Pharma/Biotech) – from writing a Study Protocol document to designing/developing studies, collecting & cleaning data, standardizing said data to submitting objective evidence to regulatory bodies.



June 09, 2021


  1. Dear Startups Chandi Kodthiwada

  2. Opportunity for AI Impact on Clinical Trials • Moon shots:

    Long-shot goals, may take years, a lot of collaboration and cutting-edge innovation • Flare Shots: Short-focused efforts can solve crisply defined problem sets • Mountain Shots: Large efforts required to work with Regulatory, Pharma and everyone involved to arrive at a pragmatic solution – short, validating steps are paramount
  3. Moon shots Leap in value Transformations

  4. Overarching AI engine to run Clinical Trials • Help Design

    Protocols – Reduce Protocol Amendments & Increase Patient Recruitment Rates • Machine learning driven Study development & Data Standardization • AI companion for Study Conduct: Data Management, Patient Safety, Medical Monitoring etc. • NLP/U generated Patient Narratives, CSR & other Study TLFs
  5. Flare shots Help at the moment of need

  6. Help at the precise moment of need (aka increase the

    speed of knowledge acquisition ) • Mine, Extract, Classify/Categorize, Learn from various unstructured content – Public & Private content • Connect Researchers to Research • Connect Patients with Drugs • Inference Engines: Recommended response for Regulatory responses, Best Next Step for Protocols (What-If Scenarios) • Event driven notifications with just-in-time predictions with explainable AI • Application of NLP/Voice assistants across the spectrum of Clinical Trial Functional teams (Ops, data management, monitoring, safety etc.) • AI/NLP generated PSUR/ASRs
  7. Mountain Shots Herculean efforts with Just enough Just in-time execution

  8. Just Enough Just In-Time • ML-driven data harmonization of all

    historical trial-data at Pharma • ML-generated continuous insights for enabling AI-assisted human study conduct –proactive fraud detection, data cleansing, patient cohort analysis etc. • Synthetic Cohorts – Reduce the burden and cost of running a clinical trial by enabling a competitor drug or Placebo synthetic Cohorts • S/AE (Adverse Event) prediction for critical-care patients
  9. LetÊs build! Chandi Kodthiwada