Browsing by Author "Sarmet, Max"
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- The importance of ethical reasoning in next generation tech educationPublication . Reis, Sara; Coelho, Luis; Sarmet, Max; Araújo, Joana; Corchado, Juan M.Artificial intelligence (AI) is having a profound impact on human life, with both benefits and drawbacks in the societal, environmental, and technological realms. However, the ethical implications of AI are often not addressed in technology education, leaving future professionals with a lack of awareness in this area. This is concerning, as AI has the potential to greatly information delivery and affect human thinking, interaction, decision-making, and communication. To address these issues, there is a need for a framework to guide and help future AI developers make ethically responsible decisions. In this paper we propose a framework to foster ethical awareness and promote respect for human dignity and well-being, while also preventing harm. It is designed to be incorporated into technology education, ensuring that future professionals are equipped to navigate the ethical implications of AI. By prioritizing ethical reasoning in technology education, we can build a better and more responsible AI industry, ensuring that AI can provide benefits for society and does not cause harm. Additionally, a tech industry that values ethics and social responsibility will be better equipped to build technology that serves the public interest, rather than solely maximizing profits. Teaching ethical reasoning in technology education is a crucial step in preparing future professionals to make informed and ethical decisions in the development and use of AI systems. It will lead to a better and more responsible AI industry that benefits all of society.
- The use of natural language processing in palliative care research: A scoping reviewPublication . Sarmet, Max; Kabani, Aamna; Coelho, Luis; Reis, Sara Seabra dos; Zeredo, Jorge L; Mehta, Ambereen KBackground: Natural language processing has been increasingly used in palliative care research over the last 5 years for its versatility and accuracy. Aim: To evaluate and characterize natural language processing use in palliative care research, including the most commonly used natural language processing software and computational methods, data sources, trends in natural language processing use over time, and palliative care topics addressed. Design: A scoping review using the framework by Arksey and O’Malley and the updated recommendations proposed by Levac et al. was conducted. Sources: PubMed, Web of Science, Embase, Scopus, and IEEE Xplore databases were searched for palliative care studies that utilized natural language processing tools. Data on study characteristics and natural language processing instruments used were collected and relevant palliative care topics were identified. Results: 197 relevant references were identified. Of these, 82 were included after full-text review. Studies were published in 48 different journals from 2007 to 2022. The average sample size was 21,541 (median 435). Thirty-two different natural language processing software and 33 machine-learning methods were identified. Nine main sources for data processing and 15 main palliative care topics across the included studies were identified. The most frequent topic was mortality and prognosis prediction. We also identified a trend where natural language processing was frequently used in analyzing clinical serious illness conversations extracted from audio recordings. Conclusions: We found 82 papers on palliative care using natural language processing methods for a wide-range of topics and sources of data that could expand the use of this methodology. We encourage researchers to consider incorporating this cutting-edge research methodology in future studies to improve published palliative care data.