Investigating Context in How Clinicians Make Sense of AI: Qualitative Interview Study

Background AI-driven clinical decision support systems (AI-CDSS) are increasingly being introduced to enhance clinical decision-making, yet their adoption in health care remains limited. Research has examined technical, organizational, and contextual barriers to adoption, but less attention has been paid to how knowledge, values, and responsibility around AI-CDSS are constructed and negotiated in practice. Objective This study examines how clinicians and AI experts make sense of AI-CDSS by reanalyzing qualitative interview data through a feminist and constructivist science and technology studies (STS) perspective. Whereas the original study examined informational needs, this reanalysis asks how participants construct relevant knowledge, evaluate the value of AI-CDSS, and negotiate appropriate use and responsibility in clinical practice. Methods The authors conducted a reanalysis of semistructured interviews held in 2024 with clinicians (n=8) and AI experts (n=8) in the Netherlands. The original study focused on informational needs for AI-CDSS. For this analysis, a deductive coding framework based on European Commission guidelines for trustworthy AI was first applied, and the material was then inductively reorganized into 5 empirical themes. Subsequently, feminist and constructivist STS concepts were used as sensitizing concepts in an abductive analysis, and these concepts were brought into dialogue with the empirical themes to examine the sociotechnical relations underlying participants’ accounts. Results Situated knowledge shows that the informational needs of clinicians are not universal but relate to their roles and specific clinical contexts, with understanding oriented less toward technical transparency than toward actionable guidance, relevance, and integration into decision-making. Standpoint theory illuminates differences between stakeholder priorities: clinicians emphasize information on how to use and act on AI outputs within existing workflows, whereas AI experts focus more on providing technical characteristics and performance metrics. Tinkering captures how clinicians experiment with AI systems in practice to develop situated, lay forms of expertise. Situated value highlights that perceptions of an AI-CDSS’s worth are relational, with clinicians valuing novel, context-sensitive insights and developers prioritizing performance metrics. Finally, response-ability reveals how trust and accountability are largely delegated to institutional structures, while clinicians report limited legal, organizational, and structural support to respond meaningfully to uncertainty or errors when using AI-CDSS. Conclusions Reanalyzing the same interview material through an abductive STS approach makes sociotechnical dimensions visible that were not the focus of the original analysis. Rather than treating informational requirements as universal or adoption as primarily a matter of technical performance and communication, the findings show how knowledge, value, and responsibility are constituted through situated professional practices and institutional arrangements. Processes and considerations regarding how clinicians make sense of AI-CDSS are not universal but are shaped by social, cultural, and institutional contexts. This analytical perspective underscores the need for context-sensitive, multistakeholder strategies to support meaningful and responsible AI adoption in clinical practice.