AI+ Pharma (AP 1405 Self-Paced Training)
Ziele der Schulung
The AI+ Pharma™ certification is designed to equip learners with the skills to apply artificial intelligence across the pharmaceutical value chain — from drug discovery and clinical trial optimization to precision medicine, regulatory frameworks, and project implementation — enabling smarter, data-driven decision-making and innovation in pharmaceutical research and healthcare contexts. Participants will learn how AI and machine learning techniques enhance drug development, improve patient stratification, and support ethical, compliant AI systems in pharma.
Zielgruppe Seminar
- Pharmacy & Life Sciences Students
- Pharmaceutical & Biotech Professionals
- Healthcare & Medical Practitioners
- Data Scientists & AI Engineers
- Healthtech & Medtech Innovators
Voraussetzungen
- Basic Biology Knowledge
- Pharmaceutical Fundamentals (drug development & approval)
- AI & Machine Learning Basics
- Data Analytics Skills
- Ethical Awareness in AI & Healthcare Applications
Seminarinhalt
Module 1: AI Foundations for Pharma
- 1.1 AI and Machine Learning Basics
- 1.2 AI Algorithms and Models
- 1.3 Use Case: Predictive Modeling for Adverse Drug Reactions and Drug-Drug Interactions Using Historical Patient Datasets
- 1.4 Hands-on: Build Predictive Models Using No-Code Tool (Teachable Machine)
Module 2: AI in Drug Discovery and Development
- 2.1 AI in Molecular Drug Design
- 2.2 AI in Drug Repurposing
- 2.3 Use Case: AI-Driven Drug Repurposing Successes (COVID-19 Therapeutics)
- 2.4 Hands-On: Practical AI-Driven Molecular Design and Drug Repurposing Using Orange Data Mining Tool
- 2.5 Hands-On 2: Exploring Disease-Drug Associations with EpiGraphDB
Module 3: Clinical Trials Optimization with AI
- 3.1 AI-Enhanced Patient Recruitment
- 3.2 Clinical Data Management and Monitoring
- 3.3 Use Case: Pfizer’s AI-Driven Analytics for Optimizing Clinical Trials
- 3.4 Hands-on: Implementing Clinical Data Analytics Using No-Code Platforms (KNIME)
Module 4: Precision Medicine and Genomics
- 4.1 Personalized Treatment Strategies
- 4.2 Biomarker Discovery
- 4.3 Case Study: AI-Assisted Biomarker Discovery and Validation in Cancer Treatments
- 4.4 Hands-on: Hands-On Genomic Analysis – Exploring AI-Driven Genomic Interpretation Using CBioPortal
Module 5: Regulatory and Ethical AI in Pharma
- 5.1 Ethical Considerations and AI Governance
- 5.2 AI Compliance and Regulatory Frameworks
- 5.3 Case Study: Analyzing Ethical and Regulatory Challenges Encountered in Major AI-Driven Pharma Initiatives
- 5.4 Hands-on: Developing AI Governance Strategies Based on Ethical Frameworks
- 5.5 Hands-on: Literature Mining with LitVar 2.0
Module 6: Implementing AI in Pharma Projects
- 6.1 AI Project Management
- 6.2 Evaluating AI Tools and ROI
- 6.3 Hands-On: Practical AI Project Management Using Airtable for Tracking, Collaboration, and Management
Module 7: Future Trends and Sustainability in Pharma AI
- 7.1 Emerging AI Technologies in Pharma
- 7.2 AI for Sustainable Healthcare
- 7.3 Case Study: Analysis of Sustainability Initiatives Driven by AI in Pharmaceutical Industry Leaders
- 7.4 Hands-on: Scenario Planning and Predictive Analytics Using Dashboards for Future-Focused Decision Making
Module 8: Capstone Project
- 8.1 Capstone Project 1: Predictive Modeling for Adverse Drug Reactions in Polypharmacy
- 8.2 Capstone Project 2: AI-Enhanced Clinical Trial Recruitment and Retention
- 8.3 Capstone Project 3: AI-Powered Drug Design for Rare Diseases
- 8.4 Capstone Project Evaluation Scheme
Hinweise
Prüfung und Zertifizierung
- Format: 50 Multiple-Choice Questions (MCQs)
- Duration: 90 minutes
- Passing Score: 70% (35/50)
- Delivery Method: Online Proctored Exam (with one free retake)
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