Table 1.

A summary of contributing papers in our special issue

Authors	Method	Dataset Used	Key Contributions
Fosso Wamba and Queiroz (2023)	Bibliometric Analysis	14,128 papers from 51,458 authors considered	• Identified four distinct periods in the publication dynamics and the most popular approaches of AI in healthcare. • Presented a framework integrating AI technologies and applications with responsible AI and ethical considerations.
Trocin et al. (2023)	Systematic Literature Review	34 papers included	• Presented a systematic description and explanation of the intellectual structure of Responsible AI in digital health and proposed an agenda for future research.
Al-Dhaen et al. (2023)	Empirical Study (Survey)	Survey data from 276 healthcare professionals in Bahrain	• Despite contradictions associated with AI, continuous intention to use behaviour can be predicted during the diffusion of IoMT.
Johnson et al. (2023)	Design Science Research (DSR)	-	• Developed an RAI solution for identifying potentially denied claims, leading to reduced operational costs and improved efficiency of insurance claim processes.
Kumar et al. (2023)	Mixed Method	Data from 12 in-depth interviews and 290 survey responses	• Identified facets of responsible AI guiding healthcare firms in evidence-based medicine and improved patient-centred care. • Established responsible AI as a third-order factor.
El-Haddadeh et al. (2023)	Empirical Study (Analysis of Applications)	Data from two AI-based COVID-19 tracking and tracing applications	• Highlighted the need for a practical and contextual view for a comprehensive discourse on responsible AI in healthcare.
Wang et al. (2023)	Empirical Study (Survey)	404 valid responses were obtained from healthcare professionals	• Five signals of AI responsibility significantly increase healthcare practitioners’ engagement, which leads to more favourable attitudes, satisfaction, and higher usage intentions with AI technology.
Gupta et al. (2023)	Empirical Study (Survey)	246 respondents in India	• Explored a positive association between AI risks in digital healthcare and responsible AI with trust and privacy risks as moderating factors.
Liu et al. (2023)	Empirical Study (Interviews)	Data from 25 in-depth interviews of health care professionals	• Abiding by responsible AI principles can improve effectiveness of social media marketing initiatives in healthcare businesses.