AI+ Pharma Practitioner™
- From 200 EUR + VAT
- Self-paced online Start anytime
- Duration 8 hours of content, self-paced
- Language English
- Topic AI Healthcare
Overview
What the course covers
The course, the materials and the exam are in English. The description below is the original AI CERTs® text.
Revolutionize Healthcare Expertise with AI+ Pharma Practitioner™ for Smarter, Data-Driven Decisions
- Beginner-Friendly Pathway: Ideal for learners and professionals entering the world of AI in pharmaceuticals, offering clear fundamentals and easy-to-grasp concepts
- Integrated Learning Experience: Combines core pharma knowledge with intuitive AI tools, real-world case studies, and guided practice to strengthen analytical and operational skills
- Industry-Focused Growth: Equips you with practical projects, scenario-based exercises, and actionable insights to help you apply AI in drug development, research, compliance, and patient-centric solutions
Prerequisites
Requires basic biology knowledge, familiarity with pharmaceutical development and regulatory fundamentals, foundational understanding of AI and machine learning, essential data analytics skills, and strong awareness of ethical considerations in AI-powered healthcare.
Audience
Who is it for
Pharmacy & Life Sciences Students: Learners who want to complement their pharma or biotech background with practical AI skills.
Pharmaceutical & Biotech Professionals: R&D, clinical, or regulatory teams aiming to apply AI in drug discovery, trials, and safety.
Healthcare & Medical Practitioners: Doctors, clinicians, and healthcare managers interested in AI-driven decision support and precision therapeutics.
Data scientists & AI Engineers: Technical professionals looking to specialize in pharma, healthcare analytics, and intelligent drug development pipelines.
Healthtech & Medtech Innovators: Entrepreneurs, product managers, and consultants building AI-powered solutions for pharma, clinical research, and digital health.
In the price
What the price includes
- Self-paced online course with the full learning material
- The official AI CERTs® exam
- Digital badge after passing the exam
Course content
Syllabus
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
Exam
About the exam
- 50 Questions
- 90 min Time limit
- 70% Pass mark
- Online proctored exam
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