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AI+ Doctor™

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  • Redefining Healthcare with AI-Driven Diagnosis
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$85.00

Intermediate
AI+ Doctor.
40h
1 Year.
$85.00
Training 5 or more people?
Get a Quote

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
  • Enhances Diagnostic Precision: Gain tools to support faster, more accurate diagnoses using AI algorithms trained on vast clinical data.
  • Bridges Medicine and Technology: Empowers doctors to collaborate with AI systems, fostering a hybrid model of care that boosts efficiency.
  • Future-Proofs Medical Practice: Equips healthcare professionals with AI skills essential for adapting to rapidly evolving clinical technologies.
  • Improves Patient Outcomes: Learn to leverage AI for personalized treatment plans, predictive analytics, and real-time patient monitoring.
  • Validates Cutting-Edge Competence: Earn recognition for mastering AI integration in healthcare—an asset in research, hospitals, and tech-driven medical settings.

Course Curriculum

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

Learning Objectives

  • The AI+ Doctor course is designed to provide healthcare professionals with a comprehensive understanding of the integration of artificial intelligence in clinical settings.
  • Covering AI's role in diagnostics, patient care, and workflow optimization, this course equips clinicians with the knowledge to implement and evaluate AI tools effectively.
  • Key topics include identifying department-specific use cases, integrating AI across patient care stages, evaluating AI performance, and ensuring regulatory compliance.
  • The course also emphasizes understanding algorithmic bias, improving transparency, and ensuring ethical AI use.
  • By the end, participants will be prepared to drive AI adoption, enhance clinical decision-making, and improve patient outcomes.

Resource Center

  • Videos, Use cases, Workshops, E-books and tools.

Target Audience

  • Medical Practitioners.
  • Medical Students.
  • Healthcare Administrators.
  • Clinical Researchers.
  • Health Tech Enthusiasts.

Job Roles & Industry Outlook 

Al Medical Data Analyst
Develop and apply Al models to analyze patient data, predict health trends, and support evidence-based treatment decisions.
Healthcare Innovation Manager
Drive Al integration in medical practice to enhance patient outcomes and streamline clinical processes.
Chief Medical Al Officer (CMAIO)
Lead strategic Al adoption in healthcare to drive innovation, digital transformation, and personalized medicine.
Al Healthcare Consultant
Advise hospitals and clinics on adopting Al solutions to improve diagnostics, patient care, and operational efficiency.
Clinical Al Implementation Lead
Oversee the deployment of Al- powered systems in clinical settings to streamline workflows, reduce errors, and enhance care delivery.

Exam Information

Duration
90 minutes
Passing Score
70% (35/50)
Format
50 multiple-choice/multiple-response questions
Delivery Method
Online via proctored exam platform (flexible scheduling)
Fees include
One year of access to course content, including continuous updates. Certification exam. One exam re-take.

Exam Blueprint:

What is AI for Doctors? - 9%
AI in Diagnostics & Imaging - 13%
Introduction to Fundamental Data Analysis - 13%
Predictive Analytics & Clinical Decision Support – Empowering Proactive Patient Care - 13%
NLP and Generative AI in Clinical Use - 13%
Ethical and Equitable AI Use - 13%
Evaluating AI Tools in Practice - 13%
Implementing AI in Clinical Settings - 13%

Explore our Schedules

Sep 13 - Sep 17
GMT 09:00 AM - 05:00 PM
In-person
Doha
724.5$
5 Days
Enroll Now
Sep 13 - Sep 17
GMT 09:00 AM - 05:00 PM
In-person
Muscat
724.5$
5 Days
Enroll Now
Dec 13 - Dec 17
GMT 09:00 AM - 05:00 PM
In-person
Cairo
369.6$
5 Days
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Aug 24 - Aug 31
GMT 06:00 PM- 10:00 PM
LVT
Zoom
396.8$
6 Days
Enroll Now

Certificate of Completion

AI+ Doctor.

$85.00

Intermediate
AI+ Doctor.
40h
1 Year.
$85.00
Training 5 or more people?
Get a Quote

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