AI+ Doctor™ - eLearning (exam included)

AI+ Doctor™ - eLearning (exam included)

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.

Transforming Healthcare with AI-Powered Diagnostics

• Clinical Intelligence Integration: Tailored for medical professionals aiming to apply AI in diagnostics and patient management.

• Insight-Driven Care: Provides doctors with the skills to analyze and utilize AI-generated data for accurate and personalized treatment decisions.

• Comprehensive AI in Medicine: Explores a wide range of applications—from predictive analytics and medical imaging to virtual healthcare solutions.

• Next-Generation Clinical Expertise: Prepares healthcare practitioners to spearhead AI-driven advancements in modern medicine.

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.

Key Features

• Course and material in English 
• Beginner-Intermediate level  (Category: AI+ Professional)
• 1 year access to the platform 24/7

• 8 hours of video lessons & multimedia resources
• 15 hours of study time recommendation 
• Quizzes, Assessments, and Course Resources
• Online Proctored Exam with One Free Retake
• Certification of completion included
• Tools You’ll Explore:  TensorFlow, Python, Scikit-learn, Keras, Jupyter Notebooks, Tableau, Matplotlib, SQL

Learning Outcomes

• 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.
• 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.

Target Audience

• Physicians, clinicians, and medical specialists
• Medical students preparing for future roles in AI-enhanced healthcare
• Healthcare administrators and clinical operations leaders
• Clinical researchers and data scientists in medicine
• Health tech enthusiasts interested in AI applications in medicine

Prerequisites

• 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

Course Content

Module 1: Understanding AI for Doctors

• 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

Module 2: AI in Diagnostics and Imaging

• 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

Module 3: Fundamentals of Clinical Data Analysis

• 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

Module 4: Predictive Analytics and Clinical Decision Support

• 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

Module 5: NLP and Generative AI in Clinical Practice

• 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

Module 6: Ethical and Responsible AI in Medicine

• 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

Module 7: Evaluating and Selecting AI Tools

• 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

Module 8: Implementing AI in Healthcare Operations

• 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

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.

FAQ

Can I apply what I learn in this course to real-world healthcare scenarios?
Absolutely. This certification provides hands-on training through real clinical case studies and applied projects, enabling you to use AI tools directly in healthcare environments.

What makes this course unique compared to other AI in Healthcare programs?
Unlike general AI courses, this program integrates medical context with practical AI implementation, emphasizing diagnostic applications and patient-centered outcomes.

What kinds of projects will I complete?
You’ll engage in projects involving AI-assisted diagnostics, medical image interpretation, electronic health record (EHR) data analysis, and predictive modeling—mirroring real-world clinical use cases.

How is the learning structured to ensure mastery of skills?
The course combines interactive learning modules, and hands-on clinical simulations to ensure a strong balance between theory and practice.

How does this certification support my career growth?
You’ll gain the AI and data-driven competencies most sought after in the healthcare industry, preparing you for roles at the intersection of medicine, analytics, and innovation.

How Can AVC Help Foster an AI-Ready Culture?
While AI offers significant advantages, many organizations struggle with challenges like talent gaps, complex data environments, and system integration barriers. At AVC, we understand these obstacles and have tailored our certification programs to help businesses overcome them effectively.

Our strategic approach focuses on building a culture that embraces AI adoption and innovation. Through our industry-recognized certifications and in-depth training, we equip your workforce with the skills and knowledge needed to lead your organization confidently into an AI-powered future.

Customized for Impact: Our programs aren't one-size-fits-all. We offer specialized training designed by industry experts to equip your workforce with the specific skills and knowledge needed for critical AI roles.

Practical, Real-World Learning: We prioritize hands-on experience over theory, using real-world projects and case studies. This approach ensures your team gains the confidence and capability to implement AI solutions effectively, driving innovation and measurable business outcomes.