Digital Intelligence for Medical Research

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 main applications of digital technologies in medical research.
• Recognize the ethical and privacy challenges in the use of medical data for research.
• Understand the potential of digital intelligence in the analysis, processing and reuse of data for research.
  • Recognize research and innovation challenges for the development of machine learning and clinical diagnostic tools.
  • Know useful digital tools in clinical research.
  • Use digital platforms for information and collaboration research.
  • Know relevant strategies and parameters of medical image analysis.
  • Communicate a topic using multimedia tools.
  • Perform bibliographic searches in databases and repositories.
  • Understand traditional metrics and altmetrics.

Work Placement(s)

No

Syllabus

Ethics and privacy.

Data science, data sharing and data reuse.

Machine learning and clinical diagnosis.

Digital tools for clinical trials.

Digital tools for search and information.

Image analysis: radiomics, theranostics and biomarkers.

Digital tools for collaboration.

Metrics and Altmetrics.

Head Lecturer(s)

Mafalda Sofia Laranjo Cândido

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

Inan OT, et al. Digitizing clinical trials. NPJ Digit Med. 2020, 31;3:101.

Barber K. Altmetrics and Sunscreens. J Cutan Med Surg. 2019, 23(4):355-356.

Taberner R. Altmetrics: Beyond the Impact Factor. Actas Dermosifiliogr. 2018, 109(2):95-97.

Baheti AD, Bhargava P. Altmetrics: A Measure of Social Attention toward Scientific Research. Curr Probl Diagn Radiol. 2017, 46(6):391-392.

Hordern V. Data Protection Compliance in the Age of Digital Health. Eur J Health Law. 2016, 23(3):248-64.

Romansky RP, Noninska IS. Challenges of the digital age for privacy and personal data protection. Math Biosci Eng. 2020,17(5):5288-5303.

Niazi MKK, et al. Digital pathology and artificial intelligence. Lancet Oncol. 2019, 20(5):e253-e261.

Colling R, et al. Artificial intelligence in digital pathology: a roadmap to routine use in clinical practice. J Pathol. 2019, 249(2):143-150.

Acs B, et. Al. Artificial intelligence as the next step towards precision pathology. J Intern Med. 2020, 288(1):62-81.