AI+ Doctor™

Redefining Healthcare with AI-Driven Diagnosis

* Clinical Intelligence Focus: Designed for medical professionals to integrate AI into patient care and diagnostics
* Data-Driven Decisions: Equips doctors with tools to interpret AI-generated insights for precise treatment planning
* Comprehensive Medical AI Knowledge: Covers AI applications from predictive analytics to medical imaging and virtual health
* Future-Ready Expertise: Empowers healthcare practitioners to lead AI-driven innovations in clinical practice

Module 1: What is AI for Doctors?

* 1.1 From Decision Support to Diagnostic Intelligence
* 1.2 What Makes AI in Medicine Unique?
* 1.3 Types of Machine Learning in Medicine
* 1.4 Common Algorithms and What They Do in Healthcare
* 1.5 Real-World Use Cases Across Medical Specialties
* 1.6 Debunking Myths About AI in Healthcare
* 1.7 Real Tools in Use by Clinicians Today
* 1.8 Hands-on: Medical Imaging Analysis using MediScan AI Module 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 Module 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 Module 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 Module 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 Module 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 Module 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 Module 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 Tools you will explore

* Python
* TensorFlow
* Scikit-learn
* Keras
* Hugging Face Transformers
* Jupyter Notebooks
* Tableau
* Matplotlib
* SQL

Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam

Access to all materials and exams is provided for 365 days after delivery.

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