AI+ Doctor™

AI Professional

AI+ Doctor™

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

All prices are in NZD, ex GST (15%).

Funding support available — “eligible learners may access government grants or employer sponsorships for this course.”

Overview
What You’ll Learn
Certification Modules
Industry Opportunities
FAQ’s
Prerequisites
Exam Details
Exam Blueprint
Core AI Tools Covered

At a Glance: Course + Exam Overview

Category	AI Professional AI Specialization	Program Name:	AI+ Doctor™
Exam Format	50 questions, 70% passing, 90 Minutes	Duration:	Instructor-Led: 1 Day Self-Paced: 8 hours of content

🤝 You’re never on your own — Parasol Concierge Support provides integration guidance and expert VA assistance so you can focus on mastering skills while we handle the setup details.

WHAT You'll Learn

AI in Clinical Settings

Gain a comprehensive understanding of AI's role in diagnostics, patient care, and workflow optimization in clinical settings.

AI Integration in Patient Care

Learn how to identify department-specific AI use cases and integrate AI across different stages of patient care.

Evaluating AI Performance

Understand how to evaluate AI performance, ensuring its effectiveness and regulatory compliance in healthcare environments.

Ethical AI Implementation

Explore ethical considerations, algorithmic bias, and transparency to ensure responsible and effective AI implementation in healthcare.

CERTIFICATION Modules

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

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

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

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

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

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

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

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

Industry opportunities

AI Healthcare Consultant

Advise hospitals and clinics on adopting AI solutions to improve diagnostics, patient care, and operational efficiency.

Clinical AI Implementation Lead

Oversee the deployment of AI-powered systems in clinical settings to streamline workflows, reduce errors, and enhance care delivery.

AI Medical Data Analyst

Develop and apply AI models to analyze patient data, predict health trends, and support evidence-based treatment decisions.

Healthcare Innovation Manager

Drive AI integration in medical practice to enhance patient outcomes and streamline clinical processes.

Chief Medical AI Officer (CMAIO)

Lead strategic AI adoption in healthcare to drive innovation, digital transformation, and personalized medicine.

FREQUENTLY ASKED QUESTIONS

Yes, this certification equips you with practical skills through real clinical scenarios and hands-on projects. You'll be ready to apply AI tools directly in healthcare settings.

This certification combines clinical context with hands-on AI training, focusing on real-world applications in diagnostics and patient care.

You’ll work on AI diagnostics, image analysis, EHR mining, and predictive models—simulating real clinical challenges for job-ready skills.

This course blends expert lessons, interactive modules, and hands-on projects with real clinical case studies. This ensures practical learning and strong skill retention.

It equips you with in-demand AI skills, real-world healthcare projects, and domain knowledge aligned with current industry job roles.

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.

EXAM DETAILS

Duration

90 Minutes

Passing Score

70%

Format

50 multiple-choice/multiple-response questions

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%