Clinical AI and Sensing Technologies

The Clinical AI and Sensing Technologies stream focuses on the use of artificial intelligence and machine learning methods to develop patient models and personalised predictions of diagnosis and care. The stream also studies how sensing can be used to predict medical conditions, and how clinicians and patients interact with health technologies.

The work undertaken by the stream can be partitioned into 3 research activities:

Clinical AI applications. This addresses an emerging approach for personalised disease treatment and prevention that takes into consideration individual variability in genes, environment, and lifestyle. AI methods can be applied for personalised diagnosis, prognosis, and treatment prediction purposes alike. The key research question of this activity is, “How can artificial intelligence and machine learning methods be applied to personalise healthcare?”

Human-technology interaction. Information and communication technologies have been increasingly used for various healthcare tasks. They are utilised by both clinicians (for example, automated decision support) and patients (for example, activity and medication monitoring). The key research question targeted by this activity is, “How can we improve interaction of people – both clinicians and customers – with health technologies?”

Sensing and signal processing. Wearable sensing technologies can collect precious data about human behaviour and condition. The collected data is accurate and reliable, and, if processed and mined, can surface insightful information enabling monitoring of medical conditions and their progression. The key research question of this activity is, “How can sensing technologies, signal processing, and AI be applied for detection of medical conditions?”

Sample projects undertaken by the Precision Health stream:

• Prediction of treatment response in melanoma patients
• Fine-grained predictions for frail patients admitted to hospitals
• Evolution of clinician trust in decision-support AI tools
• Personal data and privacy concerns in mobile health apps
• Predictions of ADHD with brain signal and response data
• Detection of freezing-of-gait episodes with EEG data
• Use of natural language processing in general practice

For more information or to join our team

Contact Professor Shlomo Berkovsky, shlomo.berkovsky@mq.edu.au

Team members

Selected stream projects

• Characterising human cognitive processes through behavioural and physiological analysis
• COVID-19 cough screening tool
• Frailty identification for older people using electronic hospital records
• Trust in automated medical imaging technology

Research centre

Centre for Health Informatics