Artificial Intelligence and Machine Learning Courses

AI+ Doctor - Self-paced

Length 365 days access
Price NZD 500 exc GST
Inclusions Online exam

Course overview

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Why study this course

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 optimisation, 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 emphasises 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.

Exam and certification

This course prepares students for the corresponding certification. The exam/assessment is completed online and provided as part of the course content.

The exam is:

90 minutes
50 multiple choice / multiple response questions
Pass mark is 35 out of 50 (i.e. 70%)
Online via AI Proctoring platform

Course availability update

Lumify Work is actively monitoring demand for this course. It is currently offered as a self-paced eLearning course and includes the certification exam. If you're interested in joining a waitlist for future instructor-led training — or exploring options for a tailored delivery for your organisation — please contact us. Your feedback helps shape our activation roadmap.

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What you’ll learn

This course is designed to teach participants how to:

Integrate AI into patient care and diagnostics
Interpret AI-generated insights for precise treatment planning
Understand AI applications from predictive analytics to medical imaging and virtual health
Lead AI-driven innovations in clinical practice

AI CERTs at Lumify Work

AI CERTs® stands at the forefront of AI and blockchain certification, offering world-class programs that prepare individuals to lead in these rapidly growing fields. AI CERTs courses and certifications are vendor agnostic and designed to bridge the gap between theoretical knowledge and practical application, ensuring learners are equipped to make an immediate impact in their careers.
Lumify Work is a Platinum Authorized Training Partner for AI CERTs in Australia, New Zealand, and the Philippines.

View all AI CERTs at Lumify Work courses

Who is the course for?

Healthcare Professionals - Physicians, Nurses, and Medical Practitioners
Medical Researchers
Healthcare Administrators
Medical Students & Residents
Clinicians with an interest in technology

Course subjects

Module 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

Module 2: 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

Module 3: Introduction to Fundamental Data Analysis

Understanding Clinical Data Types – EHRs, Vitals, Lab Results
Structured vs. Unstructured Data in Medicine
Role of Dashboards and Visualisation 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

Module 4: 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

Module 5: 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 – Summarisation, 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

Module 6: 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

Module 7: 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: Recognising and Mitigating Risks
Checklist: “10 Questions to Ask Before Buying AI Tools”
Hands-on

Module 8: 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

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 keen interest in exploring the intersection of AI and healthcare, along with a willingness to learn about AI applications in medical settings
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

Terms & Conditions

The supply of this course by Lumify Work is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.