Explainable AI (XAI) for Pharma and Biotech Fundamentals by Tonex
The increasing reliance on artificial intelligence in pharmaceutical and biotech sectors demands a strong emphasis on transparency, interpretability, and trust. This course provides a structured foundation in Explainable AI (XAI), focusing on its role in clinical trials, drug discovery, diagnostics, and regulatory compliance.
Through the lens of interpretability frameworks like SHAP, LIME, and counterfactual analysis, professionals will gain practical strategies to ensure AI models remain auditable, fair, and unbiased. Cybersecurity professionals will also benefit, as XAI strengthens AI system integrity by revealing hidden vulnerabilities, mitigating adversarial exploitation, and enhancing trust in secure AI systems in regulated environments.
Learning Objectives:
- Understand the fundamentals of Explainable AI and its relevance to pharma and biotech
- Apply interpretability techniques such as SHAP, LIME, and counterfactual reasoning
- Assess AI models for transparency, fairness, and auditability in clinical settings
- Implement bias mitigation strategies in pharmaceutical data pipelines
- Align XAI practices with regulatory and compliance requirements
- Recognize cybersecurity risks and mitigate threats in AI-driven healthcare systems
Audience:
- Data Scientists in Biotech and Pharma
- AI and ML Engineers working in healthcare domains
- Clinical Researchers and Statisticians
- Regulatory Compliance Officers
- Cybersecurity Professionals
- Health IT Managers and Decision Makers
Course Modules:
Module 1: Introduction to XAI
- Overview of Explainable AI in life sciences
- Regulatory drivers for AI explainability
- Human vs. machine interpretability
- Challenges in black-box AI models
- Importance of model trust in healthcare
- Key terminology and taxonomy in XAI
Module 2: SHAP and LIME Methods
- Concept and architecture of SHAP
- How LIME explains model decisions
- Comparing SHAP and LIME outputs
- Visualizing feature attributions
- Strengths and limitations in pharma context
- Tools for implementing SHAP and LIME
Module 3: Counterfactual Explanations
- What are counterfactuals in XAI
- Use in patient-specific treatment predictions
- Generating actionable counterfactuals
- Validity and feasibility assessment
- Ethical implications in clinical settings
- Tools and frameworks for counterfactual generation
Module 4: Auditability in Clinical AI
- Defining AI auditability and traceability
- Documentation of model development
- Version control and reproducibility
- Regulatory expectations (FDA, EMA)
- Clinical trial AI model audit examples
- AI model lifecycle audit framework
Module 5: Bias Mitigation & Transparency
- Sources of bias in pharma AI models
- Detecting bias in datasets and algorithms
- Strategies for mitigating bias in model design
- Transparency tools for model introspection
- Legal and ethical risk reduction
- Aligning with DEI (Diversity, Equity, Inclusion) goals
Module 6: Cybersecurity and XAI
- Intersection of XAI and secure AI systems
- Preventing adversarial model manipulation
- Role of transparency in threat detection
- Auditable AI for incident response
- Securing AI pipelines in biotech systems
- Regulatory alignment with cybersecurity frameworks
Join this expert-led Tonex course to gain practical insights and skills in deploying transparent, secure, and compliant AI systems for the pharma and biotech industries. Learn how to build trust with stakeholders, support clinical decision-making, and defend against AI vulnerabilities. Enroll now to bridge the gap between interpretability and operational excellence in high-stakes AI applications.