Julia Amann
Biographic Data
| ID | 6960216 |
|---|---|
| NAME | Julia Amann |
| GIVEN NAMES | Julia |
| FAMILY NAME | Amann |
| SIGNATURE | AMANN J |
| AFFILIATIONS | ETH Zurich |
| ORCID | 0000-0003-2155-5286 |
| VERIFIED | Yes |
| TOTAL WORKS | 7 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 7 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2021 |
| H-INDEX | 0 |
Mental Health and Integration
Introduction: Forcibly displaced people are at particular risk of mental health problems and also face specific integration challenges upon resettlement. Existing literature suggests that there may be a bidirectional relationship between mental health and integration. The present study seeks to understand the relationship between integration processes and mental health problems or significant negative emotional experiences among adult refugees in…
Too Vulnerable to Involve? Challenges of Engaging Vulnerable Groups in the Co-production of Public Services through Research
Involving the public in the co-production of research to inform public service delivery can help to ensure the appropriateness of services. However, vulnerable groups are often systematically excluded from these activities. To raise awareness and stimulate discussion, this review 1) describes how vulnerable groups are currently involved in the co-production of research aimed at improving public services; (2) consolidates the challenges researcher…
A qualitative study on resilience in adult refugees in Germany
This study adds to a growing body of knowledge about resilience among adult refugees. It may support clinicians working with refugees by making them aware of specific manifestations of resilience and factors promoting positive adaptation specific to this client group. It also contributes to a more strengths-based view on refugee mental health and processes of integration
Qualitative analysis of visual risk communication on twitter during the Covid-19 pandemic
Our findings can inform the didactics of future crisis communication. The results underscore the value of engaging individuals, particularly influencers, as advocates to spread health risk messages and promote solidarity. Further, our findings on the visual characteristic of the most retweeted tweets highlight factors that health and government organisations should consider when creating visual health messages for Twitter. However, that more twee…
On Assessing Trustworthy AI in Healthcare. Machine Learning as a Supportive Tool to Recognize Cardiac Arrest in Emergency Calls
Artificial Intelligence (AI) has the potential to greatly improve the delivery of healthcare and other services that advance population health and wellbeing. However, the use of AI in healthcare also brings potential risks that may cause unintended harm. To guide future developments in AI, the High-Level Expert Group on AI set up by the European Commission (EC), recently published ethics guidelines for what it terms “trustworthy” AI. These guidel…
Co-Design of a Trustworthy AI System in Healthcare
This paper documents how an ethically aligned co-design methodology ensures trustworthiness in the early design phase of an artificial intelligence (AI) system component for healthcare. The system explains decisions made by deep learning networks analyzing images of skin lesions. The co-design of trustworthy AI developed here used a holistic approach rather than a static ethical checklist and required a multidisciplinary team of experts working w…
Explainability for artificial intelligence in healthcare
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Explainability for artificial intelligence in healthcare
Mental Health and Integration
Introduction: Forcibly displaced people are at particular risk of mental health problems and also face specific integration challenges upon resettlement. Existing literature suggests that there may be a bidirectional relationship between mental health and integration. The present study seeks to understand the relationship between integration processes and mental health problems or significant negative emotional experiences among adult refugees in…
Too Vulnerable to Involve? Challenges of Engaging Vulnerable Groups in the Co-production of Public Services through Research
Involving the public in the co-production of research to inform public service delivery can help to ensure the appropriateness of services. However, vulnerable groups are often systematically excluded from these activities. To raise awareness and stimulate discussion, this review 1) describes how vulnerable groups are currently involved in the co-production of research aimed at improving public services; (2) consolidates the challenges researcher…
A qualitative study on resilience in adult refugees in Germany
This study adds to a growing body of knowledge about resilience among adult refugees. It may support clinicians working with refugees by making them aware of specific manifestations of resilience and factors promoting positive adaptation specific to this client group. It also contributes to a more strengths-based view on refugee mental health and processes of integration
Qualitative analysis of visual risk communication on twitter during the Covid-19 pandemic
Our findings can inform the didactics of future crisis communication. The results underscore the value of engaging individuals, particularly influencers, as advocates to spread health risk messages and promote solidarity. Further, our findings on the visual characteristic of the most retweeted tweets highlight factors that health and government organisations should consider when creating visual health messages for Twitter. However, that more twee…
On Assessing Trustworthy AI in Healthcare. Machine Learning as a Supportive Tool to Recognize Cardiac Arrest in Emergency Calls
Artificial Intelligence (AI) has the potential to greatly improve the delivery of healthcare and other services that advance population health and wellbeing. However, the use of AI in healthcare also brings potential risks that may cause unintended harm. To guide future developments in AI, the High-Level Expert Group on AI set up by the European Commission (EC), recently published ethics guidelines for what it terms “trustworthy” AI. These guidel…
Co-Design of a Trustworthy AI System in Healthcare
This paper documents how an ethically aligned co-design methodology ensures trustworthiness in the early design phase of an artificial intelligence (AI) system component for healthcare. The system explains decisions made by deep learning networks analyzing images of skin lesions. The co-design of trustworthy AI developed here used a holistic approach rather than a static ethical checklist and required a multidisciplinary team of experts working w…
Political science (5 works) · Medicine (4 works) · Psychology (4 works) · Artificial Intelligence in Healthcare and Education (3 works) · Computer Science (3 works) · Engineering (3 works) · Health care (3 works) · Knowledge management (3 works) · Artificial Intelligence (2 works) · Education and experiences of immigrants and refugees (2 works)