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Paper short abstract

The paper proposes “hypothetical enrolment” as a methodological contribution to move between the expectations about AI diagnostics and their integration into clinical settings. This approach allows to appreciate the potential organizational and epistemic consequences of adopting AI diagnostics.

Paper long abstract

Despite the supposed potentialities of AI tools for medical diagnosis, their adoption is a slow and troubled process. Recent empirical studies illustrated the misalignment between the narratives and expectations about these tools and how they work in real-world settings (Carboni et al. 2023, Kusta et al. 2024). These studies suggest that the adoption of AI diagnostic tools transforms the organizational workflows, professional competences and epistemic practices in the clinical settings in which they are deployed. Yet, there is a substantial lack of research frameworks and methods for addressing the prospects of integrating AI diagnostic tools into real-world settings (Williams et al. 2024).

To assess the potential consequences of adopting AI tools, we propose “hypothetical enrolment” as a methodological framework. We conceive of “hypothetical enrolment” as a situated, anticipatory and performative approach. Our methods focuses on actors’ expectations about the consequences brought by intelligent machines in clinical practices and it analyzes such expectations by paying attention to the knowledge infrastructures in which AI tools would be implemented. We tested the validity of our method against an empirical case, the start-up Autism Scope (AS). AS applies machine learning models for the early detection of autism-spectrum-disorder. We conducted interviews with AS developers and with neuropsychiatrists, exploring the “hypothetical enrolment” of AS in clinical settings. Overall, our method offers a methodological contribution which combines two interrelated dimensions: first, actors’ promises and expectations about AI diagnostic tools; second, the infrastructural and organizational features shaping the settings in which they would be adopted.