The in-class schedule is currently unavailable. Check our online instructor-led schedule or leave your contact details to send you schedule updates.
AI Pharma
This comprehensive certification program equips pharmaceutical professionals, healthcare researchers, and healthcare managers with the expertise to harness artificial intelligence for drug discovery, clinical trials optimization, and precision medicine. The curriculum covers fundamental AI principles, applications of AI technologies, practical case studies, and hands-on projects using accessible no-code tools. Participants will gain practical insights into leveraging AI to enhance efficiency, accuracy, and innovation in pharmaceutical practices.
hours
6 hours of instructor led training or unlimited one year access for E-Learning
language
English
Summary
This comprehensive certification program equips pharmaceutical professionals, healthcare researchers, and healthcare managers with the expertise to harness artificial intelligence for drug discovery, clinical trials optimization, and precision medicine. The curriculum covers fundamental AI principles, applications of AI technologies, practical case studies, and hands-on projects using accessible no-code tools. Participants will gain practical insights into leveraging AI to enhance efficiency, accuracy, and innovation in pharmaceutical practices.
prerequisites
- Basic understanding of pharmaceutical sciences or healthcare management.
- Familiarity with foundational concepts in biology, chemistry, and data analysis.
- Interest in applying AI tools practically, without requiring programming skills.
Topics Covered
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- 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)
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- 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
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- 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)
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- 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
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- 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
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- 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
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- 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
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- 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
minimize course outline
The in-class schedule is currently unavailable. Check our online instructor-led schedule or leave your contact details to send you schedule updates.