Candidate:
Pinto Francisco Impito

Date, Time and Location:
19 June 2026, at 14:00, Sala de Atos, Faculdade de Engenharia da Universidade do Porto

Chair of the Examination Committee:
Pedro Nuno Ferreira da Rosa da Cruz Diniz (PhD), Full Professor in the Department of Informatics Engineering, Faculdade de Engenharia da Universidade do Porto.

Members:
Felisbela Maria Carvalho Lopes (PhD), Full Professor in the Department of Communication Sciences, Instituto de Ciências Sociais da Universidade do Minho;
Ivone Marília Carinhas Ferreira (PhD), Assistant Professor in the Department of Communication Sciences at the Faculdade de Ciências Sociais e Humanas da Universidade Nova de Lisboa;
Carla Susana Moiteiro Ganito Afonso (PhD), Associate Professor at the Faculdade de Ciências Humanas da Universidade Católica Portuguesa;
José Manuel Pereira Azevedo (PhD), Full Professor in the Department of Communication and Information Sciences at the Faculdade de Letras da Universidade do Porto (Supervisor);
Ricardo José Pinheiro Fernandes Morais (PhD), Assistant Professor in the Department of Communication and Information Sciences at the Faculdade de Letras da Universidade do Porto.

The thesis was co-supervised by Dr Vasco Francisco Japissane Cumbe, a lecturer at the Faculdade de Medicina da Universidade Católica de Moçambique.

Abstract:

Introduction:
The increasing complexity of health systems, combined with rapid technological advances, has transformed the ways digital tools are produced, disseminated, and used in healthcare. At the same time, the unprecedented availability of mobile devices and audiovisual resources has expanded access to technologies but also introduced new challenges, such as usage limitations due to structural and infrastructural issues, as well as inequalities in access.
Objective:
This study aimed to evaluate the implementation of digital technologies in health communication in Mozambique, focusing on the use of videos, with particular attention to doctor–patient interaction and medical training. The study implemented and assessed digital resources based on interactive simulation, education, and modeling, proposing a Mozambican digital innovation model adapted to the local context.
Methodology:
The research was conducted through three studies: (1) Interactive Simulation Video (ISV) in Medical Training, (2) Augmented Education Video (AEV) in Pre-ART Counseling for HIV-positive patients, and (3) Modeling Video (MV) in Rehabilitation Strategies. Study 1 evaluated the usability, educational value, and interactive experience of the ISV with medical students (N = 93), using an exploratory sequential approach. Study 2 employed a quasi-experimental design with an intervention group (IG = 23) and a comparison group (CG = 23), involving HIV-positive patients, using the Mann–Whitney U test to compare dimensions related to counseling quality. Study 3 followed a longitudinal design, with pre- and post-intervention assessments, to evaluate the impact of the MV on caregivers’ execution of motor recovery exercises for patients with stroke.
Results:
The studies revealed (1) high levels of agreement regarding realism, clinical relevance, and pedagogical usefulness (ISV); (2) statistically significant differences between groups (IG, CG) concerning time spent on care, appropriateness of counseling format, and decision-making for ART adherence (AEV); and (3) meaningful improvements after the intervention in strengthening practical learning and confidence in performing motor recovery procedures (MV). Overall, the research demonstrated that the structured and adapted use of digital resources, as proposed in this thesis, has high potential to transform communication processes in resource-limited environments.
Conclusion:
The research highlights the importance of structured and adapted integration of digital media in resource-limited contexts. Future investigations should explore more robust experimental designs, with larger samples and longitudinal follow-up, as well as assess the impact of these interventions on real clinical practice and treatment adherence indicators. The consolidation of the Mozambican digital innovation model can advance sustainable health education policies and promote the digital transformation of medical training in Mozambique.

Keywords: Digital innovation; Video; Medical Education; doctor–patient interaction; Mozambique.

Candidate:
Maria Helena Sampaio de Mendonça Montenegro e Almeida

Date Time and Location:
16 June 2026, 14:30, Sala de Atos, Faculdade de Engenharia da Universidade do Porto

President of the Jury:
Carlos Miguel Ferraz Baquero-Moreno (PhD), Full Professor at the Department of Informatics Engineering of the Faculdade de Engenharia da Universidade do Porto

Members:
Peter Johannes Schüffler (PhD), Professor at the Technical University of Munich, Germany;
Carlos Jorge Andrade Mariz Santiago (PhD), Research Assistant at the Robotics and Engineering Systems Laboratory (LARSyS) and Invited Assistant Professor of the Department of Electrical and Computer Engineering of the Instituto Superior Técnico da Universidade de Lisboa;
Jaime dos Santos Cardoso (PhD), Full Professor of the Department of Electrical and Computer Engineering of the Faculdade de Engenharia da Universidade do Porto (Supervisor);
Luís Filipe Pinto de Almeida Teixeira (PhD), Associate Professor of the Department of Informatics Engineering of the Faculdade de Engenharia da Universidade do Porto.

Abstract:

Artificial Intelligence models have been extensively applied to medical image analysis tasks over the past years, achieving outstanding results. However, the obscure reasoning of the models and the lack of supportive evidence causes both clinicians and patients to distrust their predictions, hindering their adoption in clinical practice. In recent years, the research community has focused on developing explanations capable of revealing a model’s reasoning. Among the various types of explanations, case-based explanations emerged as particularly intuitive for medical practitioners. While these types of explanations have been widely researched, they still possess limitations that compromise their real-world application. The main goal of this thesis is to overcome the limitations of medical case-based explanations, enabling their deployment in clinical practice.

To identify the main weaknesses of case-based explanations, we conduct a literature review on models that provide such explanations in healthcare. Through the analysis of existing works, we verify that the explanations raise privacy concerns by sharing sensitive images of patients, and lack the interactiveness required for clinicians to engage with the explanation. Furthermore, most works do not evaluate nor clinically validate the explanations through user studies with clinicians. Targeting these limitations, we propose deep generative models to obtain privacy-preserving, controllable and interactive explanations to explain the decisions of deep learning models.

To propose a privacy-preserving system for safely sharing case-based explanations, we start by reviewing the literature on image anonymisation techniques and identifying their vulnerabilities. In particular, we identify vulnerabilities by proposing two privacy attacks: a membership inference attack targeting deep generative models that generate synthetic images, and a superimposed image decomposition model to reverse the common anonymisation strategy of image averaging. After outlining the requirements that a privacy-preserving system for medical case-based explanations must fulfil, we propose privacy-preserving models capable of simultaneously anonymising medical images and generating counterfactual explanations. The proposed models rely on disentangled representation learning to separate identity and clinical traits, enabling their individual manipulation. Through this strategy, we can also separate and manipulate causal factors related with the clinical task to obtain controllable counterfactual explanations. To obtain interactive counterfactual explanations, we propose models to manipulate regions of medical images according to a segmentation mask, measuring their impact on a prediction. To evaluate the proposed models, we perform experiments on medical datasets like chest radiographs, verifying the models’ capacity to manipulate medical images for the purposes of generating explanations.

Finally, to show the wide applicability of the proposed models beyond their original purpose of generating case-based explanations, we apply them as decision-support systems in healthcare. More specifically, we adapt the models for predicting the aesthetic outcomes of breast cancer treatment, to facilitate the patients’ choice of treatment. Furthermore, we conduct a clinical study with breast surgeons from various health institutes to assess the predictions of the proposed models.

To conclude, through various contributions on explainable Artificial Intelligence and decision-support systems, this thesis contributes towards the safe use of trustworthy and privacy-preserving Artificial Intelligence models in healthcare.

Candidate: Samuel Filipe da Silva Pereira Oliveira