Addressing the Unique Requirements of MLOps for Healthcare & Life Sciences

  1. Regulatory compliance: Safety and privacy regulation means data science teams can’t introduce just any tool for analyzing confidential patient data. MLOps tools need to meet all requirements (like HIPAA), no matter the data environment.
  2. Explainability and experimentation: There are some areas where black-box ML approaches can work, but for the most part researchers and regulatory approval require establishing causality through continuous and concurrent experimentation. Data scientists and researchers need to quickly identify when and why their back-tested models aren’t matching in the field conditions (for example, chest x-rays of sickly patients lying down threw off early diagnostic models for COVID).
  3. Efficiently analyzing massive and unstructured data sets: As an example, data about a single human genome sequence would take up 200 gigabytes. Additionally, much of HLS data is unstructured, like clinical notes, digital pathology slides, or X-ray images. Clinical data abstraction can benefit from complex natural language processing (NLP) models deployed easily on large and complex clinical data pipelines or using computer vision (CV) for AI-assisted imaging data classification and segmentation. But these are computationally intense models that can be expensive to run in production
  • Clinical data abstraction
  • Diagnostics for patient reports
  • MRI scan segmentation with AI
  • Clinical trial matching
  • Biomarker discovery
  • Automated data cleaning
  • Anomaly detection
  • Device failure prediction

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