@inproceedings {43344,
	title = {A Survey on the Perception of Opportunities and Limitations of Generative AI in the Public Sector},
	journal = {2nd World Conference on Information Systems for Business Management (ISBM 2023)},
	year = {2023},
	month = {09/2023},
	publisher = { Springer},
	abstract = {Recent breakthroughs in Artificial Intelligence (AI) have increased the interest in applications of AI in general and Generative AI in particular. This interest is manifest in public debate on the promise and limitations of AI in various sectors, but also on the risks of relying upon such technology. However, little is known about the interest in and perceptions of Generative AI among Information Technology (IT) professionals likely to take responsibility for developing and implementing AI-based solutions in the public sector. We present findings from a survey we developed (with help from ChatGPT) and conducted among IT practitioners in the Norwegian public sector. Through this survey, we investigated how opportunities and limitations of the use Generative AI were perceived and understood. We found ambivalent attitudes: while the interest in Generative AI is high, the level of perceived knowledge is low; there is considerable optimism about the value of AI, but an equivalent level of concern about threats this technology represents; and although respondents suspected AI had helped generate the survey, they found the survey questions relevant and fully context-oriented. These findings provide background and context for further research into the adoption of AI and help practitioners establish a baseline for efforts to build and manage AI-based solutions in public institutions.},
	keywords = {artificial intelligence, ChatGPT, Digitalization, Generative AI, Public sector, survey},
	author = {Knutsen, Leif and Jose David {Pat{\'o}n-Romero} and Jo Erskine {Hannay} and Sinan Sigurd {Tanilkan}}
}
@article {28224,
	title = {Artificial intelligence in dry eye disease},
	journal = {The Ocular Surface},
	volume = {23},
	year = {2022},
	month = {Jan-01-2022},
	pages = {74 - 86},
	publisher = {Elsevier},
	abstract = {Dry eye disease (DED) has a prevalence of between 5 and 50\%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. Many tests used in the diagnosis of DED rely on an experienced observer for image interpretation, which may be considered subjective and result in variation in diagnosis. Since artificial intelligence (AI) systems are capable of advanced problem solving, use of such techniques could lead to more objective diagnosis. Although the term {\textquoteleft}AI{\textquoteright} is commonly used, recent success in its applications to medicine is mainly due to advancements in the sub-field of machine learning, which has been used to automatically classify images and predict medical outcomes. Powerful machine learning techniques have been harnessed to understand nuances in patient data and medical images, aiming for consistent diagnosis and stratification of disease severity. This is the first literature review on the use of AI in DED. We provide a brief introduction to AI, report its current use in DED research and its potential for application in the clinic. Our review found that AI has been employed in a wide range of DED clinical tests and research applications, primarily for interpretation of\&nbsp;interferometry, slit-lamp and\&nbsp;meibography\&nbsp;images. While initial results are promising, much work is still needed on model development, clinical testing and standardisation.},
	keywords = {artificial intelligence, Dry eye disease, Machine learning},
	issn = {15420124},
	doi = {10.1016/j.jtos.2021.11.004},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1542012421001324},
	author = {Stor{\r a}s, Andrea and Str{\"u}mke, Inga and Riegler, Michael and Grauslund, Jakob and Hugo Lewi {Hammer} and Yazidi, Anis and Halvorsen, P{\r a}l and Kjell G. {Gundersen} and Tor P. {Utheim} and Catherine J. {Jackson}}
}
@article {43079,
	title = {State of Gender Equality in and by Artificial Intelligence},
	journal = {IADIS International Journal on Computer Science \& Information Systems},
	volume = {17},
	number = {2},
	year = {2022},
	month = {12/2022},
	pages = {31-48},
	publisher = {IADIS},
	abstract = {When talking about sustainability, we usually think that it is only about safeguarding the environment; nothing is further from reality. Of course, the environment is a crucial component of sustainability and our survival, but it is important to recall that the society and the economy play important roles in this regard, and without the interconnection and development of these three perspectives it will not be possible to achieve sustainable progress. The Sustainable Development Goals (SDGs) established by the United Nations (UN) defend this idea and address the main challenges that humanity faces. One of these challenges is gender equality, which is identified in the perspective of social sustainability through SDG 5. Gender equality is a very complex and difficult challenge to address due to the great cultural diversity of our society. Thus, achieving this goal will require laying a solid foundation and working together by combining very different fields of knowledge. In this sense, Artificial Intelligence (AI) is one of the fields that is currently having the greatest impact and relevance for the development of new technologies and for the advancement of numerous areas. This growing evolution of AI demonstrates that its repercussions at the social level must be analyzed and addressed in such a way that AI becomes a positive asset for sustainability and, in this particular case, for gender equality.For all these reasons, this study aims to analyze the current state of the art and collect the existing knowledge in the fields of AI and gender equality, by conducting a Systematic Mapping Study (SMS). The obtained results and findings have allowed us to identify the most relevant advances in this regard, as well as the gaps and drawbacks that currently exist and on which we must urgently focus to address gender equality both in and by AI. In the same way, these findings demonstrate the limited joint development of both fields, but also indicate an increase in the relevance and the number of proposals that these fields are receiving in recent years.},
	keywords = {artificial intelligence, Gender Equality, Literature Analysis, Literature Review, Social Sustainability},
	issn = {1646-3692},
	url = {https://www.iadisportal.org/ijcsis/papers/2022170203.pdf},
	author = {Jose David {Pat{\'o}n-Romero} and Vinuesa, Ricardo and Jaccheri, Letizia and Maria Teresa {Baldassarre}}
}
@inbook {28006,
	title = {Artificial Intelligence in Gastroenterology},
	booktitle = {Artificial Intelligence in Medicine},
	year = {2021},
	month = {09/2021},
	pages = {1 - 20},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {The holy grail in endoscopy examinations has for a long time been assisted diagnosis using Artificial Intelligence (AI). Recent developments in computer hardware are now enabling technology to equip clinicians with promising tools for computer-assisted diagnosis (CAD) systems. However, creating viable models or architectures, training them, and assessing their ability to diagnose at a human level, are complicated tasks. This is currently an active area of research, and many promising methods have been proposed. In this chapter, we give an overview of the topic. This includes a description of current medical challenges followed by a description of the most commonly used methods in the field. We also present example results from research targeting some of these challenges, and a discussion on open issues and ongoing work is provided. Hopefully, this will inspire and enable readers to future develop CAD systems for gastroenterology.},
	keywords = {Anomaly detection, artificial intelligence, Gastrointestinal endoscopy, Hand-crafted features, Neural Networks, Performance, Semantic segmentation},
	isbn = {978-3-030-58080-3},
	doi = {10.1007/978-3-030-58080-3_163-2},
	url = {https://link.springer.com/referenceworkentry/10.1007\%2F978-3-030-58080-3_163-2},
	author = {Str{\"u}mke, Inga and Hicks, Steven and Thambawita, Vajira and Jha, Debesh and Parasa, Sravanthi and Riegler, Michael and Halvorsen, P{\r a}l},
	editor = {Lidstr{\"o}mer, Niklas and Ashrafian, Hutan}
}
@inproceedings {10.1145/3394171.3416287,
	title = {ACM Multimedia BioMedia 2020 Grand Challenge Overview},
	journal = {Proceedings of the 28th ACM International Conference on Multimedia},
	year = {2020},
	pages = {4655{\textendash}4658},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	abstract = {The BioMedia 2020 ACM Multimedia Grand Challenge is the second in a series of competitions focusing on the use of multimedia for different medical use-cases. In this year{\textquoteright}s challenge, participants are asked to develop algorithms that automatically predict the quality of a given human semen sample using a combination of visual, patient-related, and laboratory-analysis-related data. Compared to last year{\textquoteright}s challenge, participants are provided with a fully multimodal dataset (videos, analysis data, study participant data) from the field of assisted human reproduction. The tasks encourage the use of the different modalities contained within the dataset and finding smart ways of how they may be combined to further improve prediction accuracy. For example, using only video data or combining video data and patient-related data. The ground truth was developed through a preliminary analysis done by medical experts following the World Health Organization{\textquoteright}s standard for semen quality assessment. The task lays the basis for automatic, real-time support systems for artificial reproduction. We hope that this challenge motivates multimedia researchers to explore more medical-related applications and use their vast knowledge to make a real impact on people{\textquoteright}s lives.},
	keywords = {artificial intelligence, Machine learning, male fertility, semen analysis, spermatozoa},
	isbn = {9781450379885},
	doi = {10.1145/3394171.3416287},
	url = {https://doi.org/10.1145/3394171.3416287},
	author = {Hicks, Steven and Thambawita, Vajira and Hugo Lewi {Hammer} and Trine B. {Haugen} and Jorunn M. {Andersen} and Witczak, Oliwia and Halvorsen, P{\r a}l and Riegler, Michael}
}
@inproceedings {27612,
	title = {Scalable Infrastructure for Efficient Real-Time Sports Analytics},
	journal = {Companion Publication of the 2020 International Conference on Multimodal Interaction},
	year = {2020},
	publisher = {ACM},
	address = {New York, NY, USA},
	abstract = {Recent technological advances are adapted in sports to improve performance, avoid injuries, and make advantageous decisions. In this paper, we describe our ongoing efforts to develop and deploy PMSys, our smartphone-based athlete monitoring and reporting system. We describe our first attempts to gain insight into some of the data we have collected. Experiences so far are promising, both on the technical side and for athlete performance development. Our initial application of artificial-intelligence methods for prediction is encouraging and indicative.},
	keywords = {algorithmic analysis, artificial intelligence, Machine learning, privacy-preserving data collection, Sports performance logging},
	isbn = {9781450380027},
	doi = {10.1145/339503510.1145/3395035.3425300},
	url = {https://dl.acm.org/doi/proceedings/10.1145/3395035https://dl.acm.org/doi/10.1145/3395035.3425300https://dl.acm.org/doi/pdf/10.1145/3395035.3425300},
	author = {H{\r a}vard D. {Johansen} and Johansen, Dag and Kupka, Tomas and Riegler, Michael and Halvorsen, P{\r a}l},
	editor = {Truong, Khiet and Heylen, Dirk and Czerwinski, Mary and Berthouze, Nadia and Chetouani, Mohamed and Nakano, Mikio}
}
