Artificial intelligence (AI) healthcare technology innovations: the current state and challenges from a life science industry perspective

Figures & data

Table 1. Functions of innovation systems and their indicators of strength (Bergek et al. 2008; Hekkert et al. 2007; Larisch, Amer-Wåhlin, and Hidefjäll 2016; Markard and Truffer 2008; Rickne 2000).

System function		Function description	Indicators of function strength
F1	Knowledge development and diffusion	Refers to the scientific, technological and market knowledge base.	Number of scientific publications; assessments of function by experts.
F2	Legitimation	Relates to the social acceptance and compliance of the technology.	How legitimacy influences demands and behaviours; length from application development to installation.
F3	Resource mobilisation	Mobilisation of infrastructure and human and financial resources.	Accessibility of data. Volumes of financial and human capital.
F4	Guidance of search	Influence on the direction of development.	End-users’ expectations and needs for the technology.
F5	Entrepreneurial experimentation	Refers to experimentation and testing of new applications.	Number of different types of applications.
F6	Market formation	When innovations are widely available on the market.	Timing, size and type of markets and customers.
F7	System-wide synergies	Reinforcing system where all functions are fulfilled and spread positive effects.	Establishment of standards and formal networks between actors.

Table 2. Categorisation of AI healthcare technology innovations based on end-user applications.

End-user application	Example
Pharmaceutical development	Drug discovery and drug design.
Medical device therapy	Artificial body parts translate signals from the brain.
Diagnostics	Tools for detecting correlations between symptoms and diseases.
Education	Medical simulators are used for the training of healthcare professionals.
Decision support	Advanced image analysis is used to make better decisions.
Patient support	Digital apps are used by patients to increase compliance.

Table 3. Metrics are used to rank the level of adoption AI healthcare technology innovations.

Metric	Level of adoption	Description
1	Pre-development	Concepts and models are being developed.
3	Development	Concepts or prototypes were further developed into pilot projects.
5	Take-off	Technology was deployed in niche markets.
7	Acceleration	Technology applications were adopted in established markets.
9	Stabilization	Technologies had replaced existing technologies.

Table 4. Categorisation of participants from companies a, b, c, d, e and f.

Department or subdivision	Company size
	Micro	Small	Medium	Large
Market & business development	c (1)	d (1)	b (1)	a (1)
Computer science or IT	—	d (1)	f (1)	a (1)
Research & development	e (1)	—	f (1)	a (4)

Note: The number in parenthesis indicates the number of persons interviewed at the specific company.

Figure 1. Selection of key actors inspired by Larisch, Amer-Wåhlin, and Hidefjäll (2016). Boxes indicate study participants included in phases I–III. Dotted boxes indicate actors who were contacted to identify participants for phases I–II.

Figure 2. The number of published scientific articles related to AI healthcare technology innovations in Sweden and in West Sweden during 1998–2017.

Table 5. Level of commercially deployed AI healthcare technology innovations.

	Commercially deployed AI healthcare technology innovations
Innovation type	Pre-development	Development	Take-off	Acceleration	Stabilization
Product innovation	33% (2)	17% (1)	50% (3)	—	—
Service innovation	73% (16)	18% (4)	9% (2)	—	—

Note: Absolute numbers are outlined in parentheses.

Table 6. Level of adoption of commercially deployed AI healthcare technology innovations according to study participants.

Company size	Commercially deployed AI healthcare technology innovations
	Pre-development	Development	Take-off	Acceleration	Stabilization
Micro	50% (7)	21% (3)	29% (4)	–	–
Small	–	50% (1)	50% (1)	–	–
Medium	100% (3)	–	–	–	–
Large	89% (8)	11% (1)	–	–	–

Note: Absolute numbers are outlined in parentheses.

Table 7. Identified system strengths and weaknesses.

Assessment	System function		Function strengths	Function weaknesses
Intermediate	F1	Knowledge development and diffusion	Broad technological knowledge base due to large amount of activities in other sectors (S1).	Knowledge gap between decision-makers and AI experts was identified in a few of the medium- and large-sized companies (W1).
				Absolute number of scientific publications related to life science and healthcare was low (W2).
	F5	Entrepreneurial experimentation	The number of new company entrants relating to the TIS has increased (S2).	Few established medium- and large-sized life science companies had not yet initiated AI innovation projects (W9).
			Variety of end-user applications (S3).
Weak	F2	Legitimation		Difficulties with the use of health data due to data liability and privacy concerns (W3).
				Low level of adoption of healthcare technology innovations (W4).
	F3	Resource mobilization		Restrictions to use patient data from healthcare databases and quality registries (W5).
				Difficulties to attract AI-skilled talents to life science (W6).
				Low financial resources (W7).
	F4	Guidance of search		Insufficient articulation by leading healthcare professionals of the needs to improve healthcare with AI technology innovations (W8).
	F6	Market formation		No AI technology innovations for healthcare applications commercially deployed among medium- and large-sized companies (W10).
				Commercial AI innovations developed by micro- and small-sized companies deployed to niche markets and did not yet generate significant revenues (W11).
	F7	System-wide synergies		Formal networks were sought to further develop knowledge and increase interdisciplinary collaborations (W12).
				Restrictive collaboration rules between industry and healthcare (W13).
				Lack of national and international standards and regulations for AI healthcare technology innovations (W14).

Figure 3. Interactions between system functions for AI healthcare technology innovations.

Figure 4. System-blocking mechanisms relating to the innovation system performance.

Figure 5. Interdependencies between system functions relating to the innovation system performance.

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