Course Overview
TOPThe AI+ Doctor™ is about empowering medical professionals to integrate artificial intelligence into clinical practice, diagnostics, and patient care. Learners explore AI-driven healthcare technologies for disease prediction, medical imaging, and data analytics. The course provides hands-on understanding of how AI enhances diagnosis accuracy and treatment outcomes. It bridges medical expertise with AI innovation to improve patient management and healthcare efficiency. By taking this course, doctors can lead the digital transformation of modern healthcare.
Scheduled Classes
TOP| 01/21/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 02/18/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 03/18/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 04/15/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 05/20/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 06/17/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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What You'll Learn
TOP- Understand the role of AI in modern medicine and healthcare delivery
- Apply AI tools for diagnostics, predictive analytics, and patient monitoring
- Use AI-driven insights to improve decision-making and clinical workflows
- Explore ethical and regulatory aspects of AI in healthcare
- Integrate AI solutions to enhance patient care and medical efficiency
Outline
TOPWhat is AI for Doctors?
- From Decision Support to Diagnostic Intelligence
- What Makes AI in Medicine Unique?
- Types of Machine Learning in Medicine
- Common Algorithms and What They Do in Healthcare
- Real-World Use Cases Across Medical Specialties
- Debunking Myths About AI in Healthcare
- Real Tools in Use by Clinicians Today
- Hands-on: Medical Imaging Analysis using MediScan AI
AI in Diagnostics & Imaging
- Introduction to Neural Networks: Unlocking the Power of AI
- Convolutional Neural Networks (CNNs) for Visual Data: Seeing with AI’s Eyes
- Image Modalities in Medical AI: AI’s Multi-Modal Vision
- Model Training Workflow: From Data Labeling to Deployment – The AI Lifecycle in Medicine
- Human-AI Collaboration in Diagnosis: The Power of Augmented Intelligence
- FDA-Approved AI Tools in Diagnostic Imaging: Trust and Validation
- Hands-on Activity: Exploring AI-Powered Differential Diagnosis with Symptoma
Introduction to Fundamental Data Analysis
- Understanding Clinical Data Types – EHRs, Vitals, Lab Results
- Structured vs. Unstructured Data in Medicine
- Role of Dashboards and Visualization in Clinical Decisions
- Pattern Recognition and Signal Detection in Patient Data
- Identifying At-Risk Patients via Trends and AI Scores
- Interactive Activity: AI Assistant for Clinical Note Insights
Predictive Analytics & Clinical Decision Support – Empowering Proactive Patient Care
- Predictive Models for Risk Stratification – Sepsis and Hospital Readmissions
- Logistic Regression, Decision Trees, Ensemble Models
- Real-Time Alerts – Early Warning Systems (MEWS, NEWS)
- Sensitivity vs. Specificity – Metric Choice by Clinical Need
- ICU and ER Use Cases for AI-Triggered Interventions
NLP and Generative AI in Clinical Use
- Foundations of NLP in Healthcare
- Large Language Models (LLMs) in Medicine
- Prompt Engineering in Clinical Contexts
- Generative AI Use Cases – Summarization, Counselling Scripts, Translation
- Ambient Intelligence: Next-Gen Clinical Documentation
- Limitations & Risks of NLP and Generative AI in Medicine
- Case Study: Transforming Clinical Documentation and Enhancing Patient Care with Nabla Copilot
Ethical and Equitable AI Use
- Algorithmic Bias – Race, Gender, Socioeconomic Impact
- Explainability and Transparency (SHAP and LIME)
- Validating AI Across Populations
- Regulatory Standards – HIPAA, GDPR, FDA/EMA Compliance
- Drafting Ethical AI Use Policies
- Case Study – Biased Pulse Oximetry Detection
Evaluating AI Tools in Practice
- Core Metrics: Understanding the Basics
- Confusion Matrix & ROC Curve Interpretation
- Metric Matching by Clinical Context
- Interpreting AI Outputs: Enhancing Clinical Decision-Making
- Critical Evaluation of Vendor Claims: Ensuring Reliability and Effectiveness
- Red Flags in Commercial AI Tools: Recognizing and Mitigating Risks
- Checklist: “10 Questions to Ask Before Buying AI Tools”
- Hands-on
Implementing AI in Clinical Settings
- Identifying Department-Specific AI Use Cases
- Mapping AI to Workflows (Pre-diagnosis, Treatment, Follow-up)
- Pilot Planning: Timeline, Data, Feedback Cycles
- Team Roles – Clinical Champion, AI Specialist, IT Admin
- Monitoring AI Errors – Root Cause Analysis
- Change Management in Clinical Teams
- Example: ER Workflow with Triage AI Integration
- Scaling AI Solutions Across the Healthcare System
- Evaluating AI Impact and Performance Post-Deployment
Prerequisites
TOPRequired
- Participants should have foundational knowledge of clinical practices, medical terminology and patient care processes
- A basic understanding of healthcare systems, including electronic health records (EHRs) and patient workflows will be beneficial
- A basic understanding of data concepts, including data collection, analysis, and interpretation, is recommended for understanding AI models and metrics
- Ability to approach challenges with a solutions-oriented mindset, especially when evaluating AI systems and adapting them to clinical settings
Recommended
- A keen interest in exploring the intersection of AI and healthcare, along with a willingness to learn about AI applications in medical settings
Who Should Attend
TOP- Healthcare Professionals
