{"course":{"productid":36658,"modality":1,"active":true,"language":"de","title":"AI+ Pharma\u2122","productcode":"APHARMA","vendorcode":"AH","vendorname":"AI Certs","fullproductcode":"AH-APHARMA","courseware":{"has_ekit":false,"has_printkit":true,"language":""},"url":"https:\/\/portal.flane.ch\/course\/aicerts-apharma","objective":"<ul>\n<li>Bridges AI and Life Sciences: Connects core AI skills with pharmaceutical R&amp;D, clinical workflows, and regulatory realities to make you truly industry-ready.<\/li><li>Speeds Drug Discovery &amp; Development: Equips you to apply AI for target identification, molecule screening, and trial optimization, shortening development cycles.<\/li><li>Enhances Decision-Making in Healthcare: Enables data-driven decisions using AI models for risk assessment, patient stratification, and treatment optimization.<\/li><li>Increases Career Opportunities in Pharma &amp; Healthtech: Positions you for emerging roles at pharmaceutical companies, biotech startups, CROs, and AI-driven health platforms.<\/li><li>Prepares You for the Future of Precision Medicine: Builds the skills to contribute to personalized therapies, adaptive clinical pathways, and AI-augmented healthcare ecosystems.<\/li><\/ul>","essentials":"<p>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.<\/p>","audience":"<ul>\n<li>Pharmacy &amp; Life Sciences Students: Learners who want to complement their pharma or biotech background with practical AI skills.<\/li><li>Pharmaceutical &amp; Biotech Professionals: R&amp;D, clinical, or regulatory teams aiming to apply AI in drug discovery, trials, and safety.<\/li><li>Healthcare &amp; Medical Practitioners: Doctors, clinicians, and healthcare managers interested in AI-driven decision support and precision therapeutics.<\/li><li>Data scientists &amp; AI Engineers: Technical professionals looking to specialize in pharma, healthcare analytics, and intelligent drug development pipelines.<\/li><li>Healthtech &amp; Medtech Innovators: Entrepreneurs, product managers, and consultants building AI-powered solutions for pharma, clinical research, and digital health.<\/li><\/ul>","contents":"<h5>Module 1: AI Foundations for Pharma<\/h5><ul>\n<li>1.1 AI and Machine Learning Basics<\/li><li>1.2 AI Algorithms and Models<\/li><li>1.3 Use Case: Predictive Modeling for Adverse Drug Reactions and Drug-Drug Interactions Using Historical Patient Datasets<\/li><li>1.4 Hands-on: Build Predictive Models Using No-Code Tool (Teachable Machine)<\/li><\/ul><h5>Module 2: AI in Drug Discovery and Development<\/h5><ul>\n<li>2.1 AI in Molecular Drug Design<\/li><li>2.2 AI in Drug Repurposing<\/li><li>2.3 Use Case: AI-Driven Drug Repurposing Successes (COVID-19 Therapeutics)<\/li><li>2.4 Hands-On: Practical AI-Driven Molecular Design and Drug Repurposing Using Orange Data Mining Tool<\/li><li>2.5 Hands-On 2: Exploring Disease-Drug Associations with EpiGraphDB<\/li><\/ul><h5>Module 3: Clinical Trials Optimization with AI<\/h5><ul>\n<li>3.1 AI-Enhanced Patient Recruitment<\/li><li>3.2 Clinical Data Management and Monitoring<\/li><li>3.3 Use Case: Pfizer&rsquo;s AI-Driven Analytics for Optimizing Clinical Trials<\/li><li>3.4 Hands-on: Implementing Clinical Data Analytics Using No-Code Platforms (KNIME)<\/li><\/ul><h5>Module 4: Precision Medicine and Genomics<\/h5><ul>\n<li>4.1 Personalized Treatment Strategies<\/li><li>4.2 Biomarker Discovery<\/li><li>4.3 Case Study: AI-Assisted Biomarker Discovery and Validation in Cancer Treatments<\/li><li>4.4 Hands-on: Hands-On Genomic Analysis &ndash; Exploring AI-Driven Genomic Interpretation Using CBioPortal<\/li><\/ul><h5>Module 5: Regulatory and Ethical AI in Pharma<\/h5><ul>\n<li>5.1 Ethical Considerations and AI Governance<\/li><li>5.2 AI Compliance and Regulatory Frameworks<\/li><li>5.3 Case Study: Analyzing Ethical and Regulatory Challenges Encountered in Major AI-Driven Pharma Initiatives<\/li><li>5.4 Hands-on: Developing AI Governance Strategies Based on Ethical Frameworks<\/li><li>5.5 Hands-on: Literature Mining with LitVar 2.0<\/li><\/ul><h5>Module 6: Implementing AI in Pharma Projects<\/h5><ul>\n<li>6.1 AI Project Management<\/li><li>6.2 Evaluating AI Tools and ROI<\/li><li>6.3 Hands-On: Practical AI Project Management Using Airtable for Tracking, Collaboration, and Management<\/li><\/ul><h5>Module 7: Future Trends and Sustainability in Pharma AI<\/h5><ul>\n<li>7.1 Emerging AI Technologies in Pharma<\/li><li>7.2 AI for Sustainable Healthcare<\/li><li>7.3 Case Study: Analysis of Sustainability Initiatives Driven by AI in Pharmaceutical Industry Leaders<\/li><li>7.4 Hands-on: Scenario Planning and Predictive Analytics Using Dashboards for Future-Focused Decision Making<\/li><\/ul><h5>Module 8: Capstone Project<\/h5><ul>\n<li>8.1 Capstone Project 1: Predictive Modeling for Adverse Drug Reactions in Polypharmacy<\/li><li>8.2 Capstone Project 2: AI-Enhanced Clinical Trial Recruitment and Retention<\/li><li>8.3 Capstone Project 3: AI-Powered Drug Design for Rare Diseases<\/li><li>8.4 Capstone Project Evaluation Scheme<\/li><\/ul>","summary":"<p>Harness AI in Pharma&trade; to speed drug discovery, optimize trials, and enable precision therapies.<\/p>\n<p>Revolutionize Healthcare Expertise with AI+ Pharma&trade; for Smarter, Data-Driven Decisions<\/p>\n<ul>\n<li>Beginner-Friendly Pathway: Ideal for learners and professionals entering the world of AI in pharmaceuticals, offering clear fundamentals and easy-to-grasp concepts<\/li><li>Integrated Learning Experience: Combines core pharma knowledge with intuitive AI tools, real-world case studies, and guided practice to strengthen analytical and operational skills<\/li><li>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<\/li><\/ul>","objective_plain":"- Bridges AI and Life Sciences: Connects core AI skills with pharmaceutical R&D, clinical workflows, and regulatory realities to make you truly industry-ready.\n- Speeds Drug Discovery & Development: Equips you to apply AI for target identification, molecule screening, and trial optimization, shortening development cycles.\n- Enhances Decision-Making in Healthcare: Enables data-driven decisions using AI models for risk assessment, patient stratification, and treatment optimization.\n- Increases Career Opportunities in Pharma & Healthtech: Positions you for emerging roles at pharmaceutical companies, biotech startups, CROs, and AI-driven health platforms.\n- Prepares You for the Future of Precision Medicine: Builds the skills to contribute to personalized therapies, adaptive clinical pathways, and AI-augmented healthcare ecosystems.","essentials_plain":"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_plain":"- Pharmacy & Life Sciences Students: Learners who want to complement their pharma or biotech background with practical AI skills.\n- Pharmaceutical & Biotech Professionals: R&D, clinical, or regulatory teams aiming to apply AI in drug discovery, trials, and safety.\n- Healthcare & Medical Practitioners: Doctors, clinicians, and healthcare managers interested in AI-driven decision support and precision therapeutics.\n- Data scientists & AI Engineers: Technical professionals looking to specialize in pharma, healthcare analytics, and intelligent drug development pipelines.\n- Healthtech & Medtech Innovators: Entrepreneurs, product managers, and consultants building AI-powered solutions for pharma, clinical research, and digital health.","contents_plain":"Module 1: AI Foundations for Pharma\n\n\n- 1.1 AI and Machine Learning Basics\n- 1.2 AI Algorithms and Models\n- 1.3 Use Case: Predictive Modeling for Adverse Drug Reactions and Drug-Drug Interactions Using Historical Patient Datasets\n- 1.4 Hands-on: Build Predictive Models Using No-Code Tool (Teachable Machine)\nModule 2: AI in Drug Discovery and Development\n\n\n- 2.1 AI in Molecular Drug Design\n- 2.2 AI in Drug Repurposing\n- 2.3 Use Case: AI-Driven Drug Repurposing Successes (COVID-19 Therapeutics)\n- 2.4 Hands-On: Practical AI-Driven Molecular Design and Drug Repurposing Using Orange Data Mining Tool\n- 2.5 Hands-On 2: Exploring Disease-Drug Associations with EpiGraphDB\nModule 3: Clinical Trials Optimization with AI\n\n\n- 3.1 AI-Enhanced Patient Recruitment\n- 3.2 Clinical Data Management and Monitoring\n- 3.3 Use Case: Pfizer\u2019s AI-Driven Analytics for Optimizing Clinical Trials\n- 3.4 Hands-on: Implementing Clinical Data Analytics Using No-Code Platforms (KNIME)\nModule 4: Precision Medicine and Genomics\n\n\n- 4.1 Personalized Treatment Strategies\n- 4.2 Biomarker Discovery\n- 4.3 Case Study: AI-Assisted Biomarker Discovery and Validation in Cancer Treatments\n- 4.4 Hands-on: Hands-On Genomic Analysis \u2013 Exploring AI-Driven Genomic Interpretation Using CBioPortal\nModule 5: Regulatory and Ethical AI in Pharma\n\n\n- 5.1 Ethical Considerations and AI Governance\n- 5.2 AI Compliance and Regulatory Frameworks\n- 5.3 Case Study: Analyzing Ethical and Regulatory Challenges Encountered in Major AI-Driven Pharma Initiatives\n- 5.4 Hands-on: Developing AI Governance Strategies Based on Ethical Frameworks\n- 5.5 Hands-on: Literature Mining with LitVar 2.0\nModule 6: Implementing AI in Pharma Projects\n\n\n- 6.1 AI Project Management\n- 6.2 Evaluating AI Tools and ROI\n- 6.3 Hands-On: Practical AI Project Management Using Airtable for Tracking, Collaboration, and Management\nModule 7: Future Trends and Sustainability in Pharma AI\n\n\n- 7.1 Emerging AI Technologies in Pharma\n- 7.2 AI for Sustainable Healthcare\n- 7.3 Case Study: Analysis of Sustainability Initiatives Driven by AI in Pharmaceutical Industry Leaders\n- 7.4 Hands-on: Scenario Planning and Predictive Analytics Using Dashboards for Future-Focused Decision Making\nModule 8: Capstone Project\n\n\n- 8.1 Capstone Project 1: Predictive Modeling for Adverse Drug Reactions in Polypharmacy\n- 8.2 Capstone Project 2: AI-Enhanced Clinical Trial Recruitment and Retention\n- 8.3 Capstone Project 3: AI-Powered Drug Design for Rare Diseases\n- 8.4 Capstone Project Evaluation Scheme","summary_plain":"Harness AI in Pharma\u2122 to speed drug discovery, optimize trials, and enable precision therapies.\n\nRevolutionize Healthcare Expertise with AI+ Pharma\u2122 for Smarter, Data-Driven Decisions\n\n\n- Beginner-Friendly Pathway: Ideal for learners and professionals entering the world of AI in pharmaceuticals, offering clear fundamentals and easy-to-grasp concepts\n- Integrated Learning Experience: Combines core pharma knowledge with intuitive AI tools, real-world case studies, and guided practice to strengthen analytical and operational skills\n- 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","version":"1.0","duration":{"unit":"d","value":1,"formatted":"1 Tag"},"pricelist":{"List Price":{"DE":{"country":"DE","currency":"EUR","taxrate":19,"price":995},"AT":{"country":"AT","currency":"EUR","taxrate":20,"price":995},"SE":{"country":"SE","currency":"EUR","taxrate":25,"price":995},"SI":{"country":"SI","currency":"EUR","taxrate":20,"price":995},"CH":{"country":"CH","currency":"CHF","taxrate":8.1,"price":995}}},"lastchanged":"2026-04-01T15:53:11+02:00","parenturl":"https:\/\/portal.flane.ch\/swisscom\/json-courses","nexturl_course_schedule":"https:\/\/portal.flane.ch\/swisscom\/json-course-schedule\/36658","source_lang":"de","source":"https:\/\/portal.flane.ch\/swisscom\/json-course\/aicerts-apharma"}}