Workflow for Adopting Open-Source AI/ML Tools in Biomedical & Clinical Research

Workflow for Adopting Open-Source AI/ML Tools in Biomedical & Clinical Research (with FISMA Considerations)

Step 1: Understanding Stakeholder Needs & Challenges
  • Conduct initial discussions to identify key biomedical and clinical research questions.
  • Assess stakeholders' familiarity with AI/ML concepts, tools, regulatory environment, data security requirements, and ethical context. Provide relative training on demand.
  • Identify specific data governance, security, and compliance requirements, especially if working with federally funded projects or handling sensitive health data.
  • Determine the FISMA impact level (low, moderate, high) for research projects involving federal data using the FISMA applicability rubric.
Step 2: Mapping Open-Source AI/ML Solutions
  • Provide an overview of relevant open-source AI/ML tools for biomedical research (e.g., TensorFlow, PyTorch, scikit-learn, MONAI, BioBERT) including FISMA compliance if FISMA applies.
  • Compare available tools based on ease of use, scalability, and security features.
  • If applicable, highlight successful FISMA-aligned AI/ML applications in biomedical and clinical research.
Step 3: Data Preparation & Management (with FISMA Compliance)
  • Guide stakeholders in understanding data security requirements, including access controls, encryption, and data storage best practices.
  • Introduce FISMA-compliant open-source tools for data preprocessing, annotation, and quality control (e.g., Pandas, DVC, OpenRefine).
  • Ensure data handling aligns with FISMA security controls (NIST 800-53) and other applicable regulations (e.g., HIPAA, GDPR).
  • Implement risk assessments and security categorizations for datasets.
Step 4: Model Selection & Implementation
  • Assist in selecting AI/ML models for biomedical and clinical data.
  • Guide stakeholders on best practices for model training, validation, and testing.
  • Introduce frameworks for model explainability and interpretability (e.g., SHAP, LIME) to ensure ethical AI implementation.
  • Ensure AI/ML models are deployed in FISMA-compliant environments, such as FedRAMP-certified cloud services (e.g., AWS GovCloud, Microsoft Azure Government, Google Cloud for Government).
Step 5: Workflow Integration & Security Controls
  • Demonstrate how to integrate AI/ML tools into existing biomedical research workflows.
  • Implement security measures such as multi-factor authentication (MFA), role-based access control (RBAC), and secure API gateways.
  • Automate security monitoring using open-source or FedRAMP-authorized cybersecurity tools.
Step 6: Evaluation & Performance Monitoring
  • Establish metrics for evaluating AI/ML model performance (e.g., accuracy, F1-score, sensitivity/specificity).
  • Implement continuous monitoring for FISMA security compliance using automated tools (e.g., AWS Security Hub, SIEM solutions).
  • Provide strategies for bias detection, robustness testing, and security vulnerability assessments.
Step 7: Knowledge Transfer & Capacity Building through associates training materials
  • Develop training materials that include FISMA compliance guidelines for AI/ML adoption in biomedical research.
  • Educate stakeholders on federal cybersecurity standards (NIST 800-53, NIST 800-171).
  • Encourage open science practices while ensuring federal security requirements are met.
  • Discuss the importance of responsible AI, security, and equity in healthcare applications.

Adopting Open-Source AI/ML Tools in Biomedical & Clinical Research: A Guided Approach

The integration of open-source AI/ML tools in biomedical and clinical research presents vast opportunities but also unique challenges—especially concerning data security, regulatory compliance, and workflow integration. This step-by-step guide provides a structured approach to implementing AI/ML solutions while ensuring FISMA (Federal Information Security Modernization Act) compliance where applicable.

From understanding stakeholder needs to selecting the right tools, securing workflows, and ensuring responsible AI implementation, this workflow outlines best practices for researchers and institutions. Click through each step to explore key considerations, security requirements, and implementation strategies to enhance research efficiency while maintaining compliance and ethical standards.