**3.3 An occupational therapy perspective**

Other examples of using this salutogenic approach are related to health promotion, both in healthcare settings and in communities. In occupational therapy, humans are seen as active beings, taking part in, and creating, meaningful activities resulting in improved health, quality of life, and well-being [30]. This can be seen as another way of understanding the sense of coherence raised by Antonovsky. The process of implementing technologies in people's lives is very complex and elaborates on interactions between actors in the field. In occupational therapy, studies have also been conducted by working user oriented and involving older adults in technology research and development. We present two examples where qualitative methods are applied.

Example 1 elaborates on that digital assistive technology has the potential to support older adults who depend upon community healthcare services. In the Assisted Living Project, we engaged older adults in co-creating knowledge about users' needs to guide the development of technological solutions [31]. In this study, user engagement was applied and aligns with the term occupational engagement [30] meaning to involve oneself or participate in occupations to create meaning. User engagement is an important strategy toward facilitating dialog, reflexivity, and the co-creation of knowledge, it can cast users in separate roles: as informants, as partners with researchers, and as independent investigators in relation to researchers as mentors [32]. To ensure the co-creation of knowledge about diverse occupations over a 3-year period, as well as considering older adults as experts on their own lives, we considered user engagement as a partnership arguing that co-creation entails engaging citizens

#### *A Salutogenic Approach for Collaboration in Health and Technology DOI: http://dx.doi.org/10.5772/intechopen.111866*

in actively taking part in innovation processes aimed at creating new and improved solutions for society [7]. The project demonstrates that older adults with impairments could meaningfully contribute with opinions on their needs. Applying a critical occupational perspective raised awareness regarding sociocultural assumptions about older adults in assisted living as frail and unable to participate, which may reinforce ageist and ableist stereotypes, as well as promote occupational injustice. This can also be related to that the participants created a sense of coherence and meaningfulness related to Antonovsky.

Example 2 is about involving older adults in technology research and development discussions through dialog cafés [33]. Citizen involvement is important for ensuring the relevance and quality of many research and innovation efforts. Literature shows that inadequate citizen involvement poses an obstacle during the research, development, and implementation of assistive technology. Previous studies have addressed the advantages and disadvantages of citizen engagement in health research and technology development, and there is concern about how to ensure valuable engagement to avoid situations where they do not have influence. Older adults are often excluded from being active partners in research projects. The overall objective of this project is to describe a case where dialog cafés were used as a method for involving assisted living residents in technology discussions, with the following research question: In what ways are dialog cafés useful for directing research and development and for engaging residents in assisted living facilities in assistive technology discussions? Six dialog cafés with assisted living residents (aged between 65 and 92) as participants were carried out over a period of 3 years (2016–2019). Reports that were written after each café by the group leaders and rapporteurs provide the material for the analyses in this paper.

This study demonstrates an example of facilitating user involvement where the participants felt useful by contributing to research and discussions on assistive technology and where this contribution in fact directed the research and development of the overall Assisted Living Project. This study also shows.

that dialog cafés enable older residents at an assisted living facility to contribute with opinions about their needs and perspectives on assistive technologies. This negates the view of older adults as too frail to participate and demonstrates the importance of including and collaborating with older adults in research. The findings can also be related to the fact that the participants created a sense of coherence and meaning by participating in the dialog cafés in line with the salutogenetic perspective by Antonovsky.

#### **3.4 A perspective of artificial intelligence and biomedicine**

A sense of coherence perspective can be applied to interdisciplinary work within biomedicine and artificial intelligence (AI), including the process of creating a collective understanding of the task at hand and interpreting the data resulting from the AI algorithms in a biological and patient-related context. The integration of AI in healthcare has the potential to revolutionize diagnostics, treatment, and patient care both in hospitals and in-home services for patients. However, interdisciplinary collaboration between medical professionals and AI experts is essential for the successful implementation of such technologies. The following examples show some challenges, highlighting the importance of interdisciplinary collaboration and suggesting potential pedagogical approaches.

The first example is using machine learning for predicting exposure to tacrolimus, an immunosuppressive drug, for individual dose adaptations in kidney-transplanted

patients [34]. In this study, machine learning techniques are employed to estimate tacrolimus exposure in kidney transplant recipients. The success of this approach relies on the collaboration between AI experts who design the algorithms and healthcare professionals who understand the clinical context and the pharmacokinetics of the drug. The challenges in the project were that different terminologies and methodologies used by AI experts and healthcare professionals may lead to misunderstandings. In an educational setting, balancing the technical aspects of AI and the clinical aspects of healthcare can be difficult.

The second example was a project about human reproduction, including embryo and sperm motility assessment using AI algorithms. Semen analysis is used as a part of male infertility assessment and the analysis protocols are standardized by the World Health Organization (WHO) [35]. This study used deep convolutional neural networks to predict sperm motility categories based on videos captured with a microscope-mounted camera [36]. Motility assessment of sperm is an important parameter in infertility investigations and the collaboration between AI experts and medical professionals was crucial in designing and evaluating the performance of these networks. For the AI-experts, it was important to understand some biological concepts and observe the samples together with the medical experts. This could be done using a discussion microscope (**Figure 3**) or looking at video clips. Another aspect is to investigate which part of the video was used by the AI model to ensure that a meaningful part of the image is analyzed by AI (**Figure 4**). This also leads potentially to a more visual understanding of what the algorithms actually are producing compared to showing numbers of difficult to interpret metrics.

Challenges in this project included the need for extensive training of the AI models to ensure accurate and reproducible results in both AI and a biological context. Furthermore, ensuring that AI models align with the standards and guidelines set in the WHO protocols. Relevant approaches for educational purposes would be integrating AI and biomedical concepts in the curriculum to expose students to the interdisciplinary nature of the field, utilizing case studies, simulations (**Figure 4**), or collaborative projects, to provide students with hands-on experience in interdisciplinary problem-solving.

**Figure 3.** *Discussion microscope.*

**Figure 4.** *Visualization of output from image analysis by an AI algorithm.*

Interdisciplinary research and teaching in the context of AI, biomedicine and healthcare can be challenging due to differences in terminology, methodologies, and educational backgrounds. In addition, there is also a dramatic difference in the ethical aspects of different disciplines. Medical sciences have a culture of ethical behavior and commitment to the patient's best outcome, whereas in technology this is radically less developed. With the fact that algorithms become a part of our daily life, technologists also need to understand and develop a culture of ethical considerations focused on implications of the products they built. However, pedagogical approaches like Problem-Based Learning (PBL) [27] and the Forming-Storming-Norming-Performing stages of team building [16], can help students navigate these challenges and develop the interdisciplinary skills necessary for successful collaboration and a better cross-disciplinary understanding. By fostering a salutogenic approach, educators can promote a sense of coherence, and a deeper understanding in their students, empowering them to create innovative solutions in the ever-evolving fields of AI and healthcare.
