One of the most promising applications of AI is in cancer diagnosis and prognosis, specifically for colorectal and breast cancers. Traditionally, pathologists analyze tumour tissue under a microscope to count Tumour-Infiltrating Lymphocytes (TILs)—immune cells that indicate how well the body is attacking the cancer. However, human counting is subject to variability and is limited to small 'high power fields' within massive images (often 400,000 by 400,000 pixels). This lesson explores how AI algorithms automatically segment these gigapixel images into small patches, identify broad tumour and stroma regions, and consistently count every single cell (up to 600,000 per slide). This consistency allows for accurate risk stratification, helping oncologists decide whether chemotherapy is necessary based on the likelihood of cancer recurrence.
Australian government bodies and research institutes provide extensive data and frameworks for digital health and cancer research.
Recovery from a stroke often requires long-term therapy, which can be difficult to manage once a patient leaves the hospital. This topic covers the 'Staying Connected' initiative, which utilizes AI to facilitate home-based care. The system uses wearable accelerometers and computer vision technology to track upper limb movements and deliver gamified therapy exercises. An 'experience sampling' app collects data on the patient's mood and activities. By analyzing this data, AI can create individualized therapy plans that adapt to the patient's specific recovery rate and needs. Additionally, Large Language Models (LLMs) are used to assist patients with speech difficulties, patching up broken speech to improve communication in social settings.
Non-profit organizations and health agencies in Australia offer resources regarding stroke recovery and the integration of digital health technologies.
Emergency Departments (EDs) face significant challenges with overcrowding and patient flow. This topic examines a case study involving St. Vincent's Hospital, where researchers analyze millions of patient records—including triage notes, arrival methods, and clinical data—to predict patient disposition (admission vs. discharge). By identifying patterns in this data, AI models can forecast bed requirements and prioritize resources to reduce delays. The lesson also touches on the use of 'self-triage' tools powered by AI assistants that capture patient information before they even see a nurse, streamlining the administrative process while ensuring critical cases are flagged immediately.
State health departments and major hospital networks in Australia publish updates on digital health strategies and emergency care optimization.