Um estudo de caso no Brasil com validação da experiência do usuário usando IA
Bibliographic record
Abstract
O uso da Tecnologia da Informação e Comunicação (TIC) é uma ferramenta poderosa em todas as áreas do conhecimento, inclusive na saúde. No setor saúde, há contribuições significativas do uso de novas tecnologias, e a Organização Mundial da Saúde considera ouso de tecnologias digitais uma ação importante na redução da incidência de tuberculose em populações vulneráveis. Este artigo tem como objetivo descrever o processo de desenvolvimento de aplicativo para controle e monitoramento da tuberculose por meio dogerenciamento do seu processo terapêutico. A abordagem metodológica inclui a Metodologia de Pesquisa Design Science, abrangendo revisão de literatura, desenvolvimento inicial de protótipo no Brasil e experiência do usuário em Portugal utilizando validação de Inteligência Artificial (IA) com Eye Tracking. Os resultados orientam a equipe de pesquisa a melhorar os recursos de monitoramento e acompanhamento de pacientes do aplicativo de tuberculose. Simulações utilizando IA para validação da aplicação demonstraram a possibilidade de simular sua utilização, possibilitando antecipar problemas e melhorar a aplicação. The use of Information and Communication Technology (ICT) is a powerful tool in all fields of knowledge, including health. In the health sector, there are significant contributions from using new technologies, and the World Health Organization considers the use of digital technologies an important action in reducing tuberculosis incidence in vulnerable populations. This article aims to describe the process of app development for controlling and monitoring tuberculosis through the management of its therapeutic process. The methodological approach includes Design Science Research Methodology, encompassing literature review,initial prototype development in Brazil, and user experience in Portugal using Artificial intelligence (IA) validation with Eye Tracking. Results guide the research team to improve the patient monitoring and follow-up features of the tuberculosis app. Simulations using AI to validate the application demonstrated the possibility of simulating its use, making it possible to anticipate problems and improve the application.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".