AI+ Doctor™ - eLearning (exam included)
275,00 EUR
- 15 hours
The AI+ Doctor™ certification is designed to empower medical professionals, clinical researchers, and health tech innovators with the skills to integrate artificial intelligence into clinical practice. This program blends medical domain knowledge with AI techniques to support diagnosis, patient monitoring, decision support, and healthcare innovation.
Key Features
Language
Course and material in English
Level
Beginner level
Access
1 year access to the platform 24/7
6 hours of video lessons & multimedia
15 hours of study time recommendation
Material
Video, PDF Material, audio eBook, Podcasts, quizzes and assessments.
Tools You’ll Explore
TensorFlow, Python, Scikit-learn, Keras, Jupyter Notebooks, Tableau, Matplotlib, SQL
Exam
Online Proctored Exam with One Free Retake
Certificate
Certification of completion included
Transforming Healthcare with AI-Powered Diagnostics
Tailored for medical professionals aiming to apply AI in diagnostics and patient management
Learning Outcomes
At the end of this course, you will be able to:
Understand core AI
and machine learning principles and their practical applications in modern healthcare.
Apply AI techniques to clinical workflows
improving diagnosis, treatment planning, and patient outcomes.
Analyze medical imaging data
using AI-powered tools for accurate and efficient diagnostics.
Utilize predictive analytics
to identify health risks, forecast disease progression, and support preventive care.
Leverage NLP
to interpret Electronic Health Records (EHRs) and extract meaningful clinical insights.
Implement AI-driven clinical decision support systems (CDSS)
for data-informed patient management.
Design and evaluate AI models
for personalized medicine and precision treatment approaches.
Ethics
Ensure ethical, transparent, and regulatory-compliant AI deployment in healthcare environments.
Demonstrate hands-on proficiency
through clinical simulations and capstone projects solving real-world medical challenges.
Course timeline
Understanding AI for Doctors
Lesson 1
- From Clinical Decision Support to Diagnostic Intelligence
- What Makes AI in Medicine Distinctive?
- Machine Learning Applications in Healthcare
- Common Algorithms and Their Functions in Medical Practice
- Real-World Applications Across Medical Specialties
- Dispelling Myths About AI in Healthcare
- AI Tools Currently Used by Clinicians
- Hands-on: Medical Imaging Analysis with MediScan AI
AI in Diagnostics and Imaging
Lesson 2
- Neural Networks Fundamentals in Medicine
- Convolutional Neural Networks (CNNs): AI Vision in Medical Imaging
- Understanding Image Modalities in Medical AI
- The AI Model Lifecycle: From Data Preparation to Deployment
- Human–AI Collaboration in Clinical Diagnosis
- FDA-Approved Diagnostic AI Tools: Ensuring Trust and Validation
- Hands-on: Exploring AI-Powered Differential Diagnosis with Symptoma
Fundamentals of Clinical Data Analysis
Lesson 3
- Overview of Clinical Data Types – EHRs, Lab Results, and Vitals
- Structured vs. Unstructured Healthcare Data
- The Role of Dashboards in Clinical Decision-Making
- Detecting Patterns and Anomalies in Patient Data
- Identifying At-Risk Patients Using Predictive AI Scores
- Interactive Activity: Using an AI Assistant for Clinical Note Insights
Predictive Analytics and Clinical Decision Support
Lesson 4
- Predictive Modeling for Risk Stratification (e.g., Sepsis, Readmissions)
- Understanding Key Algorithms – Logistic Regression, Decision Trees, Ensembles
- Real-Time Alerts: Early Warning and Monitoring Systems
- Sensitivity vs. Specificity – Choosing the Right Metrics
- AI-Driven Use Cases in ICU and Emergency Response
NLP and Generative AI in Clinical Practice
Lesson 5
- Foundations of Natural Language Processing (NLP) in Medicine
- Role of Large Language Models (LLMs) in Healthcare
- Prompt Engineering for Clinical Use Cases
- Generative AI Applications – Summarization, Translation, and Patient Communication
- Ambient Intelligence: Automating Clinical Documentation
- Challenges and Limitations of NLP in Medicine
- Case Study: Enhancing Patient Care through Nabla Copilot
Ethical and Responsible AI in Medicine
Lesson 6
- Addressing Algorithmic Bias and Its Clinical Impact
- Explainability Tools – SHAP, LIME, and Model Transparency
- Validating AI Performance Across Diverse Populations
- Navigating Regulatory Compliance – HIPAA, GDPR, FDA/EMA
- Developing Ethical AI Policies for Clinical Institutions
- Case Study: Detecting Bias in Pulse Oximetry Devices
Evaluating and Selecting AI Tools
Lesson 7
- Core AI Evaluation Metrics Explained
- Interpreting Confusion Matrices and ROC Curves
- Choosing the Right Metrics for Clinical Applications
- Understanding AI Outputs to Support Clinical Judgment
- Assessing Vendor Claims and Solution Reliability
- Identifying Red Flags in Commercial AI Tools
- Checklist: “10 Key Questions Before Adopting AI Solutions”
- Hands-on: Evaluating AI Tool Performance
Implementing AI in Healthcare Operations
Lesson 8
- Identifying Practical AI Use Cases Across Departments
- Mapping AI into Clinical Workflows (Diagnosis, Treatment, Follow-up)
- Planning Pilot Projects – Data, Timelines, and Feedback Loops
- Defining Key Roles – Clinical Lead, AI Specialist, IT Support
- Monitoring AI Errors and Conducting Root Cause Analyses
- Change Management for AI Adoption in Healthcare Teams
- Example: Integrating Triage AI in Emergency Room Workflows
- Scaling AI Solutions Across Health Systems
- Measuring AI Performance and Clinical Impact
Why Take This Course
- Enhance diagnostic accuracy: Use AI models trained on clinical data to support faster and more precise diagnoses
- Bridge medicine and technology: Equip yourself to work fluently at the intersection of healthcare and AI.
- Future-proof your practice: Gain expertise in AI tools that are increasingly adopted in modern clinical settings.
- Improve patient outcomes: Learn how data-driven insights, predictive models, and real-time monitoring can enhance care.
- Earn recognized certification: Validate your competence in medical AI, opening doors in research, hospitals, and health tech.
Who Should Enroll in this Program?
Physicians, clinicians, and medical specialists
Healthcare administrators and clinical operations leaders
Clinical researchers and data scientists in medicine
Health tech enthusiasts interested in AI applications in medicine
Medical students preparing for future roles in AI-enhanced healthcare
More Details
Pre-requisites
- Fundamental knowledge of medical concepts, clinical workflows, and patient care
- Awareness of healthcare systems and familiarity with electronic health records (EHRs)
- Basic understanding of data handling, statistics, or medical research
- An openness to learning AI concepts and tools in a clinical context
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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