AI+ Pharma - eLearning (exam included)
275,00 EUR
- 16 hours
Unlock the power of artificial intelligence to revolutionize the pharmaceutical and healthcare landscape with the AI in Pharma & Healthcare certification. This program empowers professionals to harness AI for smarter drug discovery, optimized clinical trials, personalized patient care, and efficient operational workflows. You’ll explore cutting‑edge applications of machine learning, natural language processing, and predictive analytics tailored specifically for life sciences and clinical environments.
Key Features
Language
Course and material in English
Level
Beginner-Intermediate level
Access
1 year access to the platform 24/7
8 hours of video lessons & multimedia
16 hours of study time recommendation
eBooks, Audiobooks, Podcasts
Quizzes, Assessments, and Course Resources
Exam
Online Proctored Exam with One Free Retake
Certificate
Certification of completion included
Transform Care with Intelligent Innovation
Combines essential AI expertise with pharmaceutical research, clinical processes, and regulatory knowledge, preparing you for real-world industry challenges
Learning Outcomes
At the end of this course, you will be able to:
AI Throughout the Pharma Lifecycle
Explore how AI and machine learning are applied from drug discovery to clinical trials and post-market monitoring
Data-Driven Drug Development
Use AI to analyze clinical, genomic, and real-world data to guide evidence-based decisions.
Predictive Modeling & Patient Segmentation
Develop models for treatment outcomes, risk assessment, and optimized trial design and recruitment
NLP for Pharma and Healthcare
Leverage natural language processing to extract insights from scientific papers, clinical notes, and regulatory documents
Ethics, Regulation & Compliance
Understand ethical, regulatory, and compliance frameworks to ensure responsible AI deployment in pharma.
Tools explored
- Python
- TensorFlow
- PyTorch
- Scikit-learn
- Pandas
- NumPy
- SQL
- Jupyter Notebooks
- MLflow
- DataBricks
- RDKit
- DeepChem
- Biopython
- Hugging Face Transformers for Biomedical NLP
- spaCy / Clinical NLP Toolkits
- Apache Spark for Healthcare Data
- Power BI / Tableau for Clinical Dashboards
Course timeline
Foundations of AI in Pharma
Lesson 1
- AI & Machine Learning Basics – Introduction to core AI concepts and models.
- Use Case: Predictive modeling for adverse drug reactions and drug-drug interactions using historical patient data.
- Hands-On: Build predictive models with a no-code tool (Teachable Machine).
AI in Drug Discovery & Development
Lesson 2
- Explore AI applications in molecular drug design and drug repurposing.
- Use Case: AI-driven repurposing successes, e.g., COVID-19 therapeutics.
- Hands-On: Molecular design and drug repurposing using Orange Data Mining; explore disease-drug links with EpiGraphDB.
AI for Clinical Trial Optimization
Lesson 3
- Enhance patient recruitment, clinical data management, and monitoring.
- Use Case: Pfizer’s AI analytics for optimizing trials.
- Hands-On: Implement clinical data analytics with no-code platforms like KNIME.
Precision Medicine & Genomics
Lesson 4
- Learn personalized treatment strategies and biomarker discovery.
- Case Study: AI-assisted biomarker discovery and validation in cancer.
- Hands-On: Genomic analysis using AI-driven interpretation tools like CBioPortal.
Ethical & Regulatory AI in Pharma
Lesson 5
- Examine ethical considerations, governance, compliance, and regulatory frameworks.
- Case Study: Ethical and regulatory challenges in major AI pharma projects.
- Hands-On: Develop AI governance strategies and perform literature mining with LitVar 2.0.
Implementing AI in Pharma Projects
Lesson 6
- Focus on AI project management, tool evaluation, and ROI assessment.
- Hands-On: Manage AI projects using Airtable for tracking, collaboration, and oversight.
Future Trends & Sustainable AI in Pharma
Lesson 7
- Explore emerging AI technologies and sustainable healthcare applications.
- Case Study: Sustainability initiatives led by AI in pharma.
- Hands-On: Scenario planning and predictive analytics via dashboards for future-focused decisions.
Capstone Project
Lesson 8
- Predictive modeling for adverse drug reactions in polypharmacy.
- AI-enhanced clinical trial recruitment and retention.
- AI-powered drug design for rare diseases.
- Evaluation: Structured capstone project assessment scheme.
Who Should Enroll in this Program?
Students in Pharmacy & Life Sciences: Those seeking to enhance their pharma or biotech knowledge with hands-on AI expertise.
Pharmaceutical & Biotech Professionals: R&D, clinical, and regulatory staff wanting to apply AI in drug discovery, trials, and safety management.
Healthcare Practitioners: Doctors, clinicians, and healthcare leaders aiming to leverage AI for decision support and precision medicine.
Data Scientists & AI Engineers: Technical experts looking to focus on healthcare analytics, intelligent drug development, and pharma applications.
Healthtech & Medtech Innovators: Entrepreneurs creating AI-driven solutions for pharma
More Details
Prerequisites
- Foundational Biology: Basic understanding of human biology concepts.
- Pharmaceutical Knowledge: Awareness of drug development and regulatory processes.
- AI & Machine Learning Basics: Familiarity with core AI and ML principles.
- Data Analysis Skills: Ability to work with and interpret datasets.
- Ethical Insight: Understanding of ethical considerations in AI-powered healthcare.
Exam Details
- Duration: 90 minutes
- Passing :70% (35/50)
- Format: 50 multiple-choice/multiple-response questions
- Delivery Method: Online via proctored exam platform (flexible scheduling)
- Language: English
Licensing and accreditation
This course is offered by AVC according to Partner Program Agreement and complies with the License Agreement requirements.
Equity Policy
AVC does not provide accommodations due to a disability or medical condition of any students. Candidates are encouraged to reach out to AVC for guidance and support throughout the accommodation process.
Frequently Asked Questions
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