=Paper= {{Paper |id=Vol-2991/paper08 |storemode=property |title=The Adoption Of Artificial Intelligence In SMEs - A Cross-National Comparison In German And Chinese Healthcare |pdfUrl=https://ceur-ws.org/Vol-2991/paper08.pdf |volume=Vol-2991 |authors=Philipp Dumbach,Ruining Liu,Max Jalowski,Bjoern M. Eskofier |dblpUrl=https://dblp.org/rec/conf/bir/DumbachLJE21 }} ==The Adoption Of Artificial Intelligence In SMEs - A Cross-National Comparison In German And Chinese Healthcare== https://ceur-ws.org/Vol-2991/paper08.pdf

The Adoption Of Artificial Intelligence In SMEs
- A Cross-National Comparison In German And
Chinese Healthcare

Philipp Dumbach1 , Ruining Liu1,2 , Max Jalowski2 , and Bjoern M. Eskofier1
1
Machine Learning and Data Analytics Lab,
Department Artificial Intelligence in Biomedical Engineering (AIBE),
Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany
philipp.dumbach@fau.de
2
Chair of Information Systems - Innovation and Value Creation,
Friedrich-Alexander-Universtität Erlangen-Nürnberg (FAU), Germany
max.jalowski@fau.de

Abstract. Artificial Intelligence (AI) as an emerging technology is in-
creasingly applied in the healthcare sector. Moreover, the AI-related
progress and technology application is not only driven by traditional
companies but even more by the establishment of small and medium-
sized enterprises (SME) in healthcare, the innovation process as well as
dynamic product development in the very same organizations. We chose a
multiple-case study design using expert interviews with 14 SMEs, equally
distributed from China and Germany to analyze the adoption of AI in
healthcare SMEs. Our results contribute to current empirical research
with a cross-national comparison in Germany and China on the status
of AI development and adoption, the perceived advantages and challenges
of AI, as well as the expected future development and implementation
of AI in healthcare in the upcoming five years.

Keywords: Artificial Intelligence · Adoption · Healthcare · SME · Digi-
tal Transformation · Germany · China · Benefits · Challenges · Qualitative-
Empirical Study · Interview Study.

1 Introduction
Artificial Intelligence (AI) is rapidly being applied to a wide range of fields,
including healthcare. It has been considered as a technological approach that
may augment or substitute human professionals in healthcare [1]. With recent
progress in digitized data acquisition, machine learning, and computing infras-
tructure, AI applications are expanding into areas that were thought to be re-
served for human experts [2]. A significant application in healthcare is collecting,
storing, normalizing, and tracing data [3], where AI has the potential for doing
transformative work, such as mining medical records, assisting repetitive jobs [4],
intelligent decision support in diagnosis or to correct medical decisions [5, 6]. In
the future, AI could further support digital transformation and revolutionize the

Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0)

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information supply of healthcare practitioners and executives as well as their in-
teraction with patients, clinical and operational staff [6, 7].
China is a leading global AI development hub with a vast population and
industry mix that can generate a great data volume and provide an enormous
market [8]. McKinsey Global Institute published a study in 2017, that estimated,
half of all work activities in China could be automated, illustrating the nation’s
automation potential [8]. The Chinese State Council issued a guideline in 2018
to improve healthcare service efficiency [9]. According to the ‘Made in China
2025’ (MIC25) plan, the healthcare sector is prioritized in many ways. AI, one
of the industry-spanning core elements covered by MIC25, is expected to have
a significant impact on the transformation of healthcare [10]. In 2019 China
was Germany’s most important trading partner for the fourth year in a row
and both started the deployment in digital technology to create new industrial
environments, produce new products, and improve established brands [11, 12].
In contrast to various studies on AI applications, e.g. in form of wearable
devices [13] or autonomous robotics [14], few studies have spotlighted the cur-
rent status of AI development and the prospects of AI technologies from the
company perspective. To fill this research gap, a qualitative-empirical study us-
ing semi-structured interviews was conducted with Chinese and German small
and medium-sized enterprises (SME) in healthcare, especially in micro and small
companies. Compared to global players, startups show significant differences in
acquiring and processing data as well as a differing philosophy and unique dy-
namism [15]. In total, managers of 14 healthcare SMEs (equally from Germany
and China) were interviewed to investigate the benefits and challenges regarding
the adoption of AI as well as the future technological development in 5 years’
time, including organizational requirements to adapt for a future with AI.

2 Related Work

The literature review consists of two parts: first, we review research on AI in
healthcare in terms of AI-driven applications, challenges in AI development,
and healthcare SMEs. Second, we present an overview of several representative
empirical studies on AI and digitization.

2.1 Artificial Intelligence in Healthcare

As AI in healthcare becomes more widespread, a wealth of theoretical research
on AI applications is emerging. One of the most prosperous areas to use AI is
automated medical image diagnosis, where AI-powered algorithms have made in-
roads in medical specialties including radiology, ophthalmology, pathology, and
dermatology [2, 16, 17]. In addition, wearable devices [2, 18], autonomous robotic
surgery, [14, 19] and patient care [20–22] are relevant scenarios for AI applica-
tions. Although AI promises to revolutionize medical practice, many challenges
lie ahead. Obermeyer et al. have noted that AI algorithms might ‘overfit’ predic-
tions to spurious correlations in the data, leading to exaggerated claims about

85
real-world performance. Data from different healthcare environments can contain
various types of bias and noise, which may cause a model, trained on one hospi-
tal’s data, to fail to generalize to another. They pointed out that the quantity
and quality requirements for input data in AI applications may need an upgrade
of the current databases [23]. Bartoletti highlighted that AI in healthcare will
also challenge the boundaries of current regulatory systems and privacy princi-
ples. For him, it is essential to adopt a cautious approach in order to maximize
the positive whilst reducing the risks of privacy, bias, and ethics harms [24].

2.2 Empirical Studies

Compared to the extensive theoretical studies on AI applications, there are rela-
tively few studies that explore the current status and prospects of applying new
technologies like AI in the form of empirical studies, especially those employing
interviews or questionnaires to collect data. Table 1 shows a brief review.

Table 1: Literature Review - Adoption of emerging technologies including AI

Paper Objective Method Sample Findings
Leung Views of hotel Interview 9 (Tai- No clear definition of smart
(2019), stakeholders in wanese) hotel; several barriers that
[25] Taiwan on smart prohibit hotel owners in
technology implementing smart systems
Sun and Challenges of AI Interview 18 Stakeholders have diverse, and
Medaglia in the public (Chinese) sometimes contradictory,
(2018), sector opinions of the challenges
[26]
Brooks et Pressures and Interview 18 Professional norms, tradition,
al. (2020), challenges of AI (British) and culture maintain existing
[27] in legal structures and business models
facing a technological change
Laı̈ et al. Perceptions of Interview 40 The development of AI tools in
(2020), French (French) healthcare would be
[28] stakeholders on satisfactory for everyone only
AI in healthcare by initiating a collaborative
effort between all those involve
Wangmo Ethical issues of Interview 20 (Euro- Clear disagreements among
et al. intelligent pean) professional stakeholders
(2019), assisting regarding solutions for ethical
[29] technology (IAT) challenges and the adoption of
in elderly and strategies to implement IAT
dementia care safely and effectively
Nelson et Patients’ Interview 48 (US) Patients are receptive to AI for
al. (2020), opinions of the skin cancer screening if applied
[30] use of AI for skin in a manner that preserves
cancer screening integrity of human
physician-patient relationship

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Table 1: (continued)

Blease et View of general Questionnaire 720 Most GPs considered the
al. (2019), practitioner (GP) (British) potential of AI to be limited
[31] on impact of because of the lack of
future technology communication and empathy of
in primary care this future technology
Ye et al. Public Questionnaire 474 Underdeveloped AI use in
(2019), acceptance of (Chinese) clinical laboratory analysis &
[32] ophthalmic AI diagnostics; mistrust of medical
devices AI systems in Chinese public
Lackes et Affects on Questionnaire 129 Trust in the manufacturer
al. (2020), acceptance of (mainly affects trust in IPA and
[33] intelligent German) perceived advantages; trust in
personal IPA influences the perceived
assistants (IPA) advantages & disadvantages as
well as the acceptance
Bérubé et Barriers Interview 18 Lack of data-related
al. (2021), regarding AI (French, organizational capabilities and
[34] implementation Canadian) of AI experts; generic
in organizations implementation barriers

Multiple studies focused on the healthcare industry [28–32], while others inves-
tigated areas like the public sector [25] or legal [27]. Various studies used inter-
views [25–30, 34], and questionnaires [31–33, 35] to collect data. Regarding the
choice of interviewees or respondents, most researchers looked at stakeholders of
AI technology and products [25–29, 31, 34], or targeted the potential purchasers
of AI products and services [33, 35] as well as patients [30, 32]. There are few
empirical studies on AI application in healthcare, and to the knowledge of the
authors no studies that considered healthcare SMEs as research subjects. Fur-
thermore, most studies have sampled from one country rather than making a
cross-national comparison. To fill this research gap, this paper aims for an alter-
native perspective by interviewing German and Chinese healthcare SMEs. The
analysis and comparison of the interview results support the further exploration
of AI’s development status and adoption in healthcare SMEs in both countries.

3 Research Design
This section describes the research design and the research method, including
data collection and analysis. We apply a case study research approach to answer:
how is the adoption of AI among German and Chinese healthcare SMEs and what
are the differences between the results of both countries?

3.1 Method
Case studies are a design of inquiry found in many fields. The researcher conducts
an in-depth analysis of a case, often a program, event, activity, process, or one

87
or more individuals. Cases are bounded by time and activity, and the researcher
uses a variety of data collection procedures to collect detailed information over
a sustained period of time [36, 37]. According to Yin [37], a general definition of
a case study has two main parts: (a) The scope of a case study: Investigating a
contemporary phenomenon (the “case”) in depth and within its real-world con-
text, especially when the boundaries between phenomenon and context may not
be clearly evident; and (b) a case study’s features: The situation where there
will be many more variables of interest than data points, thereby relying on
multiple sources of evidence and benefiting from the development of theoretical
propositions to guide data collection and analysis [37]. Case studies are espe-
cially suitable when it comes to how and why questions are being asked about a
contemporary set of events over which a researcher has little or no control [37].

Fig. 1: Research Procedure following Yin [37]

We chose a multiple-case study design with expert interviews to collect pri-
mary data. Each case can be represented by an interview that reflects the com-
pany’s AI use. The general research procedure is shown in Fig. 1. This flowchart,
as described by Yin [37], illustrates the primary steps of the empirical part.

3.2 Setting

The setting consists of a German and a Chinese perspective (see Fig. 1), which
consist of interviewing suitable experts found in the preparation phase. In total
seven healthcare SMEs from each country were interviewed. Table 2 summarizes
the 14 interviews.
The selection of German interviewees is mainly centered on the members of
the Medical Valley Europäische Metropolregion Nürnberg. It is a leading interna-
tional cluster in the field of medical technology, medicine, and health [41]. After

88
Table 2: Demographic characteristics of participants and related SMEs

Firm Est. Employees Job Description Location Primary Business
G1 2014 70-80 Product Manager Munich Medical Imaging
Structured Data
G2 2019 15 Co-Founder/CEO Munich
Platform
G3 2016 6 Co-Founder/CEO Erlangen Medical Sensor Systems

G4 2018 <10 Managing Director Erlangen Medical Imaging

G5 2019 15 Co-Founder/CEO Würzburg Surgical Robots

G6 2016 53 CEO Erlangen Medical Imaging

G7 2014 ∼50 Senior Consultant Fürth Medical Database

C1 2017 10 CEO Shenyang Medical Imaging

C2 2015 180 CEO Assistant Shanghai Medical Service Robots
Medical Information
C3 2015 12 CEO Shenyang
Management
Medical Equipment
C4 2010 16 Deputy CEO Shanghai
Asset Management
C5 2014 20 Founder/CEO Shanghai Medical Imaging

C6 2012 40 CEO Shanghai Medical Imaging

C7 2012 41 CEO Beijing Medical Imaging

passing the initial screening the companies were selected as potential intervie-
wees, and interview invitations were sent to the founders, CEOs, or qualified
technical staff. For the selection of the Chinese SMEs, an expert recommenda-
tion approach was adopted for optimizing the selection of interviewees. Several
industry experts with in-depth knowledge of and contact with Chinese health-
care SMEs were chosen as direct contacts who make company recommendations.
The selected potential interviewees were contacted respectively and invited for
the interviews. For Chinese interviewees, WeChat was chosen as the primary
communication tool. Companies were filtered by business type: they had to be
involved in the healthcare industry, and by company size: they had to fulfill the
European Commission’s definition of SME [42].

3.3 Data Collection and Analysis

Case study data collection is crucial and involves a wide range of procedures as
the researcher builds an in-depth picture of the case [39].

89
Interviews are commonly used in case studies as a form of data collection
to obtain primary data, consisting of asking open-ended questions to partici-
pants [40]. The interview protocol design follows the procedures from Yin [37]
and Creswell [40]. It starts with questions on the AI background and if the
company already applies AI. If they do so, questions are asked about AI imple-
mentation, AI benefits and challenges, and future possibilities. If they do not
use AI, questions are asked about reasons for not using AI, the general prospect
of AI, and further plans about AI.
All interviews were conducted online and recorded with the interviewees’ con-
sent in order to preserve the data for subsequent analysis. A combination of
automatic machine transcription and follow-up manual correction is used in the
transcribing process. The interview languages are English and Chinese. For the
English audio, the transcription is carried out using the automatic speech recog-
nition function provided by YouTube [43], whose transcription function has a
lower word error rate and therefore better performance [44]. For the Chinese
audio processing, a transcription software called iFlytek Hears is chosen [45].
The interview data is first transcribed into Chinese text form, and then further
translated into English using the translation software DeepL and manual correc-
tions. Finally, the English translations are combined with the English interview
transcripts to form the primary data set of this study.
Following Yin [37], the collected data was imported, organized, and analyzed
systematically. After collecting all interview information, the demographic char-
acteristics of interviewees are summarized to gain an overview of the interviewed
companies. Secondly, each question’s results are analyzed as a whole, then sep-
arately within and between countries, to inquire the similarities and differences.
Specifically, in the analysis of questions on the benefits and challenges of AI
use, descriptive analysis is used to count the frequency of the occurrence of each
response among the interviewees, as the responses are relatively homogeneous.
With the results of all interview questions collated and aggregated separately,
the interview questions are then grouped according to the sub-questions they de-
scribe. Among the interview questions, the ones regarding AI background and AI
implementation do not cover specific research questions. Therefore, the responses
from the two sections are used to measure an overall attitude towards AI among
SMEs. The results are represented in the form of a matrix for a more visualized
exploration in terms of cross-national comparison. For each sub-question, the
corresponding interview questions are summarised and compared to investigate
the differences in answers between both countries.

4 Results

After presenting the research design and the interviewees, this section will have a
stronger focus on the twelve SMEs (5 German, 7 Chinese) who were declared as
AI adopters. The taxonomy of AI adopters and non-adopters followed out of the
interviewee’s answers regarding the AI importance for the companies’ business
as well as the organizational capabilities to apply AI.

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4.1 Implementation Fields of AI
Two German (G3, G4) and five Chinese (C1-4, C6) SMEs considered AI as a core
element and central for their business. Another four firms, two from each country
(G1-2, C5, C7), confirmed the importance of AI but estimated the AI influence
is still limited. Therefore, AI plays a supporting role in products and services
or is seen in the research and development phase. Three German SMEs (G5-7)
perceived AI as relatively unimportant to their business. Two of those had no
plans to develop AI and according to their lack of capabilities did not apply AI
so far. This study investigated different application scenarios in healthcare SMEs
and looked at the AI adoption by the following two dimensions (see Table 3).

Table 3: Application Scenarios of Artificial Intelligence

Product Component Product Core

AI in Research G1 G3, G4, C5, C6

AI in Use G2, G5, C7 C1, C2, C3, C4

On one hand, the AI usage is classified as product component (integration in
products for the optimization of functionalities), or product core which implies
AI as absolutely relevant for the product and its functionality. On the other
hand, two categories of the current stage of AI development are distinguished:
AI in the research stage or AI already applied to products or services.

4.2 Benefits and Challenges of AI
Depending on the company background and the current AI development stage,
the German and Chinese experts named multiple benefits (see Fig. 2) as well
as challenges (see Fig. 3) that accompany the AI implementation within their
organizations. The percentages are calculated separately for the two countries
to eliminate the differences in the number of interviewees.
All twelve AI adopters highlighted ‘efficiency improvement’ as a benefit, that
manifests in e.g. speeding up data and image processing (G1-5, C1, C5-7) or
improving management efficiency (C2-4). ‘Selling point’ was mentioned by eight
companies as an advantage to convince investors and to highlight the innova-
tiveness of AI-driven specifications. Furthermore, AI is seen as a technology
that leads to better performance compared to humans or traditional algorithms.
Higher accuracy (C4-5, C7), better data processing (G3-4), path planning for
medical robots (C2), or the ability to find solutions for existing problems (G1)
are linked to this benefit category. Additionally, ‘talent attraction’, ‘avoidance
of human error’ e.g. due to fatigue, ‘cost reduction’ because of fast and accurate
data processing or labour cost savings, and ‘workload reduction for physicians’
to enable more patient quality time were discussed. Especially regarding the last
four benefits, a differentiated view of Chinese and German experts is observed.

91
Fig. 2: Benefits of AI in German and Chinese healthcare SMEs

Similarly, Fig. 3 summarizes the main challenges named by healthcare experts
according to the adoption of AI in their organizations.

Fig. 3: Challenges linked to AI in German and Chinese healthcare SMEs

Ten interviewees showed a consistent opinion regarding ‘reliability and tech-
nological limitations’, concerning the current AI accuracy and needed supervi-
sion (G3, C1, C5-7) or existing issues of non-reproducibility and robustness in
heterogeneous environments (G2-3). In terms of ‘data quality’, as a result of
non-standardized and unstructured data (G1, G4-5, C1, C5-7), and the lacking
‘trust of physicians’ in AI products and services (G3-4, C3, C5, C7) experts from

92
both countries had a mutual understanding. Nevertheless, when looking at ‘data
accessibility’, ‘transparency & interpretability’, e.g. insufficient traceability and
causality of AI decision-making processes, as well as ‘regulations’ and ‘lack of
experts’ the majority of German representatives underlined stronger concerns
compared to Chinese counterparts.

4.3 Prospective AI Development and Non-adopters of AI

A positive view on the future development of AI in the upcoming five years was
observed more from Chinese (C2-4, C6) than from German experts (G2, G4).
Chinese representatives expect an increasing trend of AI in the healthcare sector
and a further boost to their business. Activities in Chinese politics to promote
digital transformation and intelligent management in hospitals are seen as con-
tributions to AI development. From a technical perspective, they still expressed
concerns regarding the maturity of AI applications, e.g. to make autonomous de-
cisions in the early future. Moreover, German SMEs underlined a prospective AI
development in personalized medicine and within applications to support diag-
nostic and therapeutic solutions for individual patients. Healthcare SMEs from
both countries expressed a mixed (G1, C5) or negative view (G3, G5, C1, C7)
when explaining their expectations in the next five years. Optimism regarding
an increase of AI-related healthcare companies goes along with skepticism when
looking at the cooperation with physicians and the ‘unlikable replacement’ of
physicians in the future. The negative concerns have their origin in the lack of
AI experts and strict regulations, named mainly by German managers.
As mentioned at the beginning of the results section, there were two com-
panies (G6-7) that have not adopted AI so far. Both interviewees were neutral
regarding the outlook of AI. On one hand, they could imagine the benefit of AI
in supporting customers to reduce their workload and its ability to potentially
make its own decision. On the other hand, they were pessimistic according to reg-
ulations and their dependence on further research and development in the field
of AI. Both non-adopters prefer cooperation with external AI-skilled companies
instead of independently deploy AI within their organizations in the future.

5 Discussion

This study investigated the AI implementation in four areas based on the di-
mensions of AI importance and current stage to fill the research gap in empirical
research addressing the AI application in SMEs. After the analysis, no Chinese
and only one German company is located in the area ‘AI in Research x Product
Component’. The stronger focus especially of Chinese SMEs on AI adoption as
product core and their status being in the AI in use than in the research phase
can be named as reasons. This outcome might be explained by the Chinese pol-
icy to encourage the opening of healthcare data, the digitization of hospitals and
AI in healthcare [9]. Beyond, no German company is located in the area ‘AI in
use x Product Core’. This absence is justified by the maturity of AI applications

93
and declared as in research phase or as product component. The situation re-
flects the stricter regulations in German healthcare industry [46] and 3-4 years
to conduct a clinical validation and get the medical device certification (G3).

Experts from both countries had a consistent understanding of three main
AI benefits when looking at performance and efficiency improvement as well as
AI as a selling point. Nevertheless, German interviewees valued AI more with
80% for its effect in terms of talent attraction and easier recruitment compared
to only 14% in China. This significant difference could reflect the higher demand
for AI talents in Germany and is evident in the identified challenge and lack of AI
experts. These findings approve previous exploratory studies, which concentrated
on French and Canadian experts and allow a deeper comparison of the national
circumstances [34]. When looking at AI application fields, a distinct opinion can
be observed. A 57% ratio in China versus 20% in Germany sees the workload
reduction as the main AI benefit. The higher product amount with AI in use in
Chinese companies as well as the stronger focus on physician- than on patient-
oriented products and services may explain this outcome.

In the perceptions of AI challenges, there are significant disparities between
Germany and China. German interviewees not only named more challenges but
also the corresponding percentages when looking at data accessibility as well
as at transparency and interpretability are noticeably higher (80% versus 14%
each). The perception of more barriers to AI use in German healthcare SMEs
may objectively be explained by more difficulties in implementing AI (e.g. caused
by stricter data protection laws or healthcare approvals) but also by the in-
creased desire for a better understanding and explainability of AI. These find-
ings go along with the barriers to AI implementation, presented by Berubé et
al. [34], who identified the lack of high-quality data and data governance issues
as crucial. They focused on the adoption in all types of organizations. Our study
contributed to close the gap in investigating one particular type and focused on
healthcare SMEs. Meanwhile, it is worth noting that Chinese healthcare SMEs
have relatively easy access to hospital and patient data for example in form of
temporary GPU workstations in order to use images locally.

Preparing for a future of AI in healthcare, SMEs agreed on the continuing op-
timization of algorithms and of existing AI applications as well as the expansion
to new application areas. New AI technologies need to be tracked and care-
fully introduced to the healthcare sector. German and Chinese interviewees plan
to strengthen external collaborations but are following different strategies. In
both countries, companies are collaborating with universities and research lab-
oratories, when cooperating with external AI experts and software companies
healthcare SMEs in China showed stronger activities. These identified strategic
approaches show and approve the necessity of collaboration in the field of AI be-
tween all stakeholders to initiate and increase their efforts for a better transition
from AI in the development phase to its application [28].

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6 Conclusion, Limitations, and Outlook

This study concentrated on the adoption of AI in healthcare SMEs, which were
identified as an organizational group with a less received focus in previous work.
We contribute to current empirical research with a cross-national comparison of
Germany and China. On the one hand, representatives of both countries under-
lined several advantages of the adoption like efficiency improvement or workload
reduction of physicians. On the other hand, various thoughts are necessary re-
garding aspects like data quality as well as transparency and accountability to
successfully proceed with the implementation of AI in healthcare organizations.
Especially the German interviewees pointed out a stronger focus on the chal-
lenges within the adoption procedure and had concerns regarding legal guide-
lines and questions to be solved in the context of data access and transparency.
In both countries interviewees are expecting a continuing trend within the up-
coming years regarding the integration of AI in new areas and applications in
healthcare, but also a better performance compared to the existing use of AI in
applications. Chinese representatives gave insights in a stronger strategic focus
on future research and development activities, whereas in Germany the improve-
ment of AI transparency and interpretability is seen as a goal.
There are few limitations that need to be considered for the result inter-
pretation and for upcoming research. 14 SMEs were equally distributed from
China and Germany. This definitely gives valuable insights into the healthcare
landscape and the scope of SMEs, but the sample size is not representative for
both countries as a whole. The headquarters of the companies were located in
southern Germany, mainly linked to the Medical Valley. In China, the selected
SMEs stronger represent the eastern part, which is seen as economically more
developed than the western part of the country [47].
Due to the COVID-19 pandemic all interviews were conducted online, some
without video. The non-face-to-face format might have led to a different inter-
view behaviour of the interviewees and influenced the interpretation of their
answers without the ability to see the facial expressions and gestures during the
interviews.
As a follow-up to this study, the sample size could be expanded regarding
both countries and views of SMEs compared to those of traditional and estab-
lished companies further explored. In addition, the investigation within other
countries could follow the approach of Laı̈ et al. [28] who concentrated on French
health professionals instead of SME representatives or addresses AI adoption in
other industries besides its presented influence in healthcare.

7 Acknowledgements

Bjoern M. Eskofier gratefully acknowledges the support of the German Research
Foundation (DFG) within the framework of the Heisenberg professorship pro-
gram (grant number ES 434/8-1).

95
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