AI+ Doctor

Hours: 8 / Access Length: 12 Months / Delivery: Online, Self-Paced
Online Hours: 8
Retail Price: $195.00

Course Overview:

This course is specifically designed for medical professionals looking to seamlessly integrate clinical intelligence and AI into patient care and diagnostics. By providing comprehensive knowledge of AI applications—ranging from predictive analytics and medical imaging to virtual health—the program equips practitioners with the tools needed to interpret complex data for precise treatment planning. Ultimately, this curriculum empowers healthcare leaders to make data-driven decisions and gain the future-ready expertise necessary to lead AI-driven innovations in clinical practice.

Recommended Prerequisites:

• Basic Medical Knowledge: Participants should have foundational knowledge of clinical practices, medical terminology and patient care processes.
• Familiarity with Healthcare Systems: A basic understanding of healthcare systems, including electronic health records (EHRs) and patient workflows will be beneficial.
• Interest in Technology Integration: A keen interest in exploring the intersection of AI and healthcare, along with a willingness to learn about AI applications in medical settings.
• Data Literacy: A basic understanding of data concepts, including data collection, analysis, and interpretation, is recommended for understanding AI models and metrics.
• Problem-Solving Mindset: Ability to approach challenges with a solutions-oriented mindset, especially when evaluating AI systems and adapting them to clinical settings.

Course Outline:

Lesson 1: What 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

Lesson 2: AI in Diagnostics & Imaging

• 2.1 Introduction to Neural Networks: Unlocking the Power of AI
• 2.2 Convolutional Neural Networks (CNNs) for Visual Data: Seeing with AI’s Eyes
• 2.3 Image Modalities in Medical AI: AI’s Multi-Modal Vision
• 2.4 Model Training Workflow: From Data Labeling to Deployment – The AI Lifecycle in Medicine
• 2.5 Human-AI Collaboration in Diagnosis: The Power of Augmented Intelligence
• 2.6 FDA-Approved AI Tools in Diagnostic Imaging: Trust and Validation
• 2.7 Hands-on Activity: Exploring AI-Powered Differential Diagnosis with Symptoma

Lesson 3: Introduction to Fundamental Data Analysis

• 3.1 Understanding Clinical Data Types – EHRs, Vitals, Lab Results
• 3.2 Structured vs. Unstructured Data in Medicine
• 3.3 Role of Dashboards and Visualization in Clinical Decisions
• 3.4 Pattern Recognition and Signal Detection in Patient Data
• 3.5 Identifying At-Risk Patients via Trends and AI Scores
• 3.6 Interactive Activity: AI Assistant for Clinical Note Insights

Lesson 4: Predictive Analytics & Clinical Decision Support – Empowering Proactive Patient Care

• 4.1 Predictive Models for Risk Stratification – Sepsis and Hospital Readmissions
• 4.2 Logistic Regression, Decision Trees, Ensemble Models
• 4.3 Real-Time Alerts – Early Warning Systems (MEWS, NEWS)
• 4.4 Sensitivity vs. Specificity – Metric Choice by Clinical Need
• 4.5 ICU and ER Use Cases for AI-Triggered Interventions

Lesson 5: NLP and Generative AI in Clinical Use

• 5.1 Foundations of NLP in Healthcare
• 5.2 Large Language Models (LLMs) in Medicine
• 5.3 Prompt Engineering in Clinical Contexts
• 5.4 Generative AI Use Cases – Summarization, Counselling Scripts, Translation
• 5.5 Ambient Intelligence: Next-Gen Clinical Documentation
• 5.6 Limitations & Risks of NLP and Generative AI in Medicine
• 5.7 Case Study: Transforming Clinical Documentation and Enhancing Patient Care with Nabla Copilot

Lesson 6: Ethical and Equitable AI Use

• 6.1 Algorithmic Bias – Race, Gender, Socioeconomic Impact
• 6.2 Explainability and Transparency (SHAP and LIME)
• 6.3 Validating AI Across Populations
• 6.4 Regulatory Standards – HIPAA, GDPR, FDA/EMA Compliance
• 6.5 Drafting Ethical AI Use Policies
• 6.6 Case Study – Biased Pulse Oximetry Detection

Lesson 7: Evaluating AI Tools in Practice

• 7.1 Core Metrics: Understanding the Basics
• 7.2 Confusion Matrix & ROC Curve Interpretation
• 7.3 Metric Matching by Clinical Context
• 7.4 Interpreting AI Outputs: Enhancing Clinical Decision-Making
• 7.5 Critical Evaluation of Vendor Claims: Ensuring Reliability and Effectiveness
• 7.6 Red Flags in Commercial AI Tools: Recognizing and Mitigating Risks
• 7.7 Checklist: “10 Questions to Ask Before Buying AI Tools”
• 7.8 Hands-on

Lesson 8: Implementing AI in Clinical Settings

• 8.1 Identifying Department-Specific AI Use Cases
• 8.2 Mapping AI to Workflows (Pre-diagnosis, Treatment, Follow-up)
• 8.3 Pilot Planning: Timeline, Data, Feedback Cycles
• 8.4 Team Roles – Clinical Champion, AI Specialist, IT Admin
• 8.5 Monitoring AI Errors – Root Cause Analysis
• 8.6 Change Management in Clinical Teams
• 8.7 Example: ER Workflow with Triage AI Integration
• 8.8 Scaling AI Solutions Across the Healthcare System
• 8.9 Evaluating AI Impact and Performance Post-Deployment

All necessary course materials are included.

System Requirements:

Internet Connectivity Requirements:

• Cable, Fiber, DSL, or LEO Satellite (i.e. Starlink) internet with speeds of at least 10mb/sec download and 5mb/sec upload are recommended for the best experience.

NOTE: While cellular hotspots may allow access to our courses, users may experience connectivity issues by trying to access our learning management system.  This is due to the potential high download and upload latency of cellular connections.   Therefore, it is not recommended that students use a cellular hotspot as their primary way of accessing their courses.

Hardware Requirements:

• CPU: 1 GHz or higher
• RAM: 4 GB or higher
• Resolution: 1280 x 720 or higher.  1920x1080 resolution is recommended for the best experience.
• Speakers / Headphones
• Microphone for Webinar or Live Online sessions.

Operating System Requirements:

• Windows 7 or higher.
• Mac OSX 10 or higher.
• Latest Chrome OS
• Latest Linux Distributions
  

NOTE: While we understand that our courses can be viewed on Android and iPhone devices, we do not recommend the use of these devices for our courses. The size of these devices do not provide a good learning environment for students taking online or live online based courses.

Web Browser Requirements:

• Latest Google Chrome is recommended for the best experience.
• Latest Mozilla FireFox
• Latest Microsoft Edge
• Latest Apple Safari

Basic Software Requirements (These are recommendations of software to use):

• Office suite software (Microsoft Office, OpenOffice, or LibreOffice)
• PDF reader program (Adobe Reader, FoxIt)
• Courses may require other software that is described in the above course outline.