Hypothetical enrollment. An anticipatory and situated approach to assess the integration of AI diagnostics in clinical settings

Purpose: The paper proposes “hypothetical enrollment” as an anticipatory, situated and performative methodological approach to appreciate the organizational and epistemological consequences of adopting AI diagnostics into clinical settings. Our method provides a methodological contribution to move between the expectations about AI diagnostics and their integration into real world, clinical settings Design/methodology/approach: We tested the validity of our method against an empirical case, the start-up Autism Scope (AS), which applies machine learning models for the early detection of autism-spectrum-disorder. As part of this pilot study, we carried on two interviews in person with designers from AS, and three interviews with pediatric neuropsychiatrists. Findings: Notwithstanding a generally positive attitude, several organizational and professional challenges emerged thanks to our method, such as the integration of the tool into hospital workflows and the potential effects for professional identity in neuropsychiatry. Originality: The “hypothetical enrollment” interviews allowed comparing the expectations and implementation strategies devised by AS’ designers with the impediments and challenges highlighted by neuropsychiatrists, that is, potential users who have not been involved in development yet. Research limitations/implications: Other healthcare stakeholders, such as hospital mangers or policy makers, were not interviewed. Keywords: hypothetical enrollment – AI diagnostics – autism – organization of work - method