Answers could include 3 of the following:

• AI can improve diagnostic accuracy by detecting patterns in scans and identifying disease earlier.
• AI can personalise treatment plans by analysing genetic and test data for individual patients.
• AI can improve access to care through telehealth and predicting where medicines are needed most.
• AI can speed up the discovery of new medicines by analysing large datasets to identify disease causes and design new drugs.

Overview

• AI in healthcare can reduce inequities in Australia by improving diagnosis, personalised treatment and expanding access. These factors directly contribute to closing health gaps.

Gender-based diagnosis

• Gender differences in disease symptoms often lead to unequal outcomes.
• AI can analyse diverse datasets that reveal how conditions appear differently in men and women.
• For example, female heart attack symptoms often differ from men’s. AI systems identify these patterns, which leads to earlier and more accurate diagnoses.
• Mental health tools using AI can also encourage earlier intervention for men, who often avoid seeking help.
• This results in reduced mortality for both genders.

Rural and chronic disease access

• People in rural areas face reduced access to specialists. AI-enabled telehealth interacts with geography by connecting patients remotely to care.
• Chronic conditions like diabetes and cardiovascular disease depend on early detection and monitoring. Machine learning predicts disease progression and enables preventive care.
• This is significant since chronic disease is the largest burden in Australia.
• In this way, AI helps reduce inequity caused by remoteness as well as addressing disease management challenges.

Implications and Synthesis

• AI in healthcare functions by standardising diagnosis, supporting prevention, and expanding access. These elements combine to reduce health gaps between groups.
• The significance is that AI has strong potential to deliver fairer outcomes.
• However, its impact depends on reliable, balanced data and its ethical use.

• A is correct. Limited female-specific data increases the risk that AI will misinterpret or overlook women’s symptoms, leading to misdiagnosis.

Other options:

• B is incorrect. While treatment plans may be adversely affected, the most likely (primary) risk is misdiagnosis.
• C is incorrect. Equal access to services is not guaranteed by AI and does not address algorithmic bias.
• D is incorrect. Delayed care may occur in some contexts, but the more direct and likely impact is misdiagnosis.

• D is correct. The scenario shows AI being used to quickly allocate medicines where they are needed, ensuring faster access to treatment.

Other options:

• A is incorrect. This refers to public health surveillance, whereas the scenario focuses on medication access.
• B is incorrect. Personalised care involves tailoring treatment to an individual’s genetics, not population supply.
• C is incorrect. Forecasting hospital demand is related but broader; the scenario highlights the immediate delivery of medicines.

• C is correct. Deep learning is currently used in healthcare to analyse medical images and identify potential cancerous tissue.

Other options:

• A is incorrect. Too advanced; current surgical robots are AI-assisted but not autonomous.
• C is incorrect. This relates to infrastructure planning, not a current medical use of AI.
• D is incorrect. Mobile health apps support learning, but they are not an AI function in healthcare.