Ethical AI in Healthcare

TBC

Duration

On Demand

Flexible Learning

Lifetime Access

Full Access

Smart Self-Testing

Practice Anytime, Anywhere

Course Overview

Navigate the moral, legal, and professional boundaries of digital medicine. This vital masterclass examines the profound ethical responsibilities of embedding artificial intelligence into patient workflows-giving you a comprehensive checklist to detect bias, prevent health inequalities, secure informed consent, and champion equitable, person-centred care.

Why This Course Matters

AI algorithms can generate incredibly convincing outputs that look medically flawless, yet they can easily harbour hidden biases from their training data or generate outright errors. Relying too heavily on digital tools without understanding their limitations threatens patient trust and risks exacerbating health inequalities. This module provides healthcare leaders and clinicians with the critical reasoning needed to challenge automated choices, enforce human oversight, and use AI ethically.

WHO SHOULD ATTEND

A fundamental requirement for any professional selecting, evaluating, or interacting with digital health tools:

Doctors, Nurses, Allied Health Professionals, and Pharmacists using AI decision-support tools
Medical, Nursing, Pharmacy, and Allied Health Students building professional, ethical baselines
Healthcare Managers and Service Leads considering AI for triage, documentation, or operations
Digital Health, Innovation, and Transformation Teams testing or implementing clinical algorithms
Governance, Data Protection, and Clinical Safety Leads designing AI safety policies
Researchers and Audit Teams evaluating AI-driven clinical evidence reviews

COURSE FORMAT

Flexible E-Learning: Self-paced, remote digital learning featuring interactive clinical cases and step-by-step guided explanations.
Face-to-Face Teaching: Dynamic, classroom-based workshops packed with facilitated peer discussions, practical case applications, and expert clinician-led teaching.

In-Person Booking

Secure your place early. Spaces are limited due to small group teaching.

KEY FEATURES

Bias Detection Systems: Practical techniques to identify and neutralise algorithmic bias and inequalities.
Bias Detection Systems: Practical techniques to identify and neutralise algorithmic bias and inequalities.
Oversight Architecture: Robust models to define exactly where AI assistance stops and clinical liability begins.
Informed Consent Blueprints: Guidelines to ensure patients understand how and when AI shapes their care.

LEARNING OBJECTIVES

By the end of this module, you will be able to:
Identify, mitigate, and challenge data bias to prevent digital tools from widening health inequalities.
Balance automated recommendations with independent professional judgment to prevent dangerous over-reliance.
Maintain transparency with patients and colleagues regarding how AI is used within care pathways.
Apply local governance standards to ensure clear professional accountability across all AI-supported decisions.

WHAT MAKES THIS COURSE DIFFERENT

We move past basic academic philosophy to focus on active frontline protection. This masterclass gives you a clear ethical compass and practical toolkits to evaluate any piece of software before it touches a patient, ensuring you pioneer digital change without compromising human dignity, equity, or professional safety.

In-Person Booking

Secure your place early. Spaces are limited due to small group teaching.