| CODE | RAD3404 | ||||||||
| TITLE | Digital Health | ||||||||
| UM LEVEL | 03 - Years 2, 3, 4 in Modular Undergraduate Course | ||||||||
| MQF LEVEL | 6 | ||||||||
| ECTS CREDITS | 2 | ||||||||
| DEPARTMENT | Radiography | ||||||||
| DESCRIPTION | The study-unit will provide an overview of digital health systems including digital infrastructures, telemedicine, data mining, machine learning, artificial intelligence and personalized medicine. The applications of these techniques in both diagnostic and therapeutic radiography will be discussed. The challenges and legal issues related to the implementation of digital health technologies in routine clinical practice will also be evaluated. Study-Unit Aims: The study unit aims to introduce undergraduate radiography students to the: - Different types of digital health technologies that are/can be used in healthcare; - Potential applications of digital health technologies in both diagnostic and therapeutic radiography; and - Opportunities and challenges in implementing digital health technologies in routine clinical practice. Learning Outcomes: 1. Knowledge & Understanding By the end of the study-unit the student will be able to: - Evaluate the role of different digital health technologies, including telemedicine, data mining, machine learning, artificial intelligence and personalised medicine in both diagnostic and therapeutic radiography; - Critically evaluate the opportunities and challenges in introducing these technologies in routine clinical practice; and - Analyse the legal and ethical implications of introducing digital health technologies in clinical practice. 2. Skills By the end of the study-unit the student will be able to: - Apply knowledge of digital health technologies to improve diagnostic and therapeutic radiography services; and - Handle digital health data ethically and legally while maximising the links with other healthcare providers, management, information technology experts and researchers. Main Text/s and any supplementary readings: - Lotfi C (2020) Digital Health in Focus of Predictive, Preventative and Personalised medicine, Springer Nature Switzerland. - Ranschaet ER et al (2019). Artificial Intelligence in Medical Imaging: Opportunities, Applications and Risks - Santosh KC et al (2019). Medical Imaging: Artificial Intelligence, Image Recognition and Machine Learning techniques. |
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| STUDY-UNIT TYPE | Lecture and Independent Study | ||||||||
| METHOD OF ASSESSMENT |
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| LECTURER/S | Susan Mercieca Jonathan Loui Portelli |
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2024/5. It may be subject to change in subsequent years. |
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