Digital Intelligence for Healthcare

Recommended Prerequisites

Not applicable.

Teaching Methods

Teaching methodologies include exposition classes, mainly using the interrogative and expository methods, seeking the participation of students in discussions on the topics covered. The active model will also be used, in which students will develop 

tasks individually in a guided and autonomous way, including guided readings, exploration and analysis of available tools and platforms, case resolution, among others. In the seminars the students will present and discuss the work developed.

The evaluation will be carried out through the elaboration, presentation and discussion of works. Critical evaluation of colleagues' work will also be valued.

Learning Outcomes

Under the co-coordination of Doctor Mafalda Laranjo and Professor Dr. Maria Filomena Botelho, this course aims to:

• Identify the applications of digital technologies to health.
• Understand the challenges and applications of telemedicine in medical practice.

Identify the systems available for electronic registration of health data 

• Understand the importance of the electronic health data correct record.
• Recognize the challenges, legislation and risks related to the protection of health data.
• Recognize the potential of mobile health and exercise monitoring technologies.
• Recognize the application and potential of robotics, internet of things and artificial intelligence in medical practice.

Identify digital intelligence responses to public health challenges, including the Covid-19 pandemic.

Work Placement(s)

No

Syllabus

Trends in digital intelligence.

Telemedicine.

Electronic health record systems.

Databases, protection of patient data (GPDR), cybersecurity, risks.

Medical robots (surgery, internet of things, artificial intelligence).

Medical training and simulators.

Artificial intelligence in machine learning.

Artificial intelligence in pattern detection.

Mobile health technologies.

Digital intelligence applied to dentistry.

Digital intelligence and ageing.

Digital intelligence, exercise and health.

Mobile technologies applied to exercise monitoring.

Covid-19.

Head Lecturer(s)

Carlos Manuel Silva Robalo Cordeiro

Assessment Methods

Assessment
The evaluation will be carried out through the elaboration, presentation and discussion of works. Critical evaluation of colleagues' work will also be valued: 100.0%

Bibliography

Waller M, Stotler C. Telemedicine: a Primer. Curr Allergy Asthma Rep. 2018;18:54.

Tajgardoon M, et al. Modeling physician variability to prioritize relevant medical record information. JAMIA Open. 2020;3:602.

Hordern V. Data Protection Compliance in the Age of Digital Health. Eur J Health Law. 2016;23:248.

Elson DS, et al. Medical Robotics. Ann Biomed Eng. 2018;46:1433.

Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69S:S36.

Ting DSW, et al. Digital technology and COVID-19. Nat Med. 2020;26:459.

Mahboobi S, et al. Simulator-Based Training of Workflow in Echocardiography. J Cardiothorac Vasc Anesth. 2019;33:1533.

Grischke J, et al. Dentronics: Towards robotics and artificial intelligence in dentistry. Dent Mater. 2020;36:765.

Castro PC, et al. Tailoring digital apps to support active ageing in a low income community. PLoS One. 2020;15:e0242192.

ACSM´s Guidelines for Exercise Testing and Prescription (10^th Ed) 2018 Philadelphia: Wolters Kluwer