AI-assisted retrospective reconstruction of contested psychiatric care: an autoethnographic case study

Abstract

Contested psychiatric care may involve asymmetries in medical knowledge, institutional authority, procedural literacy, and access to documentary information. Generative artificial intelligence (AI), particularly large language models, has created new possibilities for patients to organize heterogeneous records and to explore alternative interpretations of complex healthcare experiences. This analytic autoethnographic case study examines a retrospective reconstruction of a psychiatric care trajectory spanning 1998–2026 and focuses on publicly accessible generative AI interfaces during the 2026 analytic period. The corpus combined autobiographical recollections, personal medical and administrative records, communication materials, longitudinal body-weight records, and AI-generated outputs from iterative analytic interactions. AI outputs were treated as interpretive prompts and organizational aids rather than as factual evidence. The analysis developed the zero-cost Distributed AI-assisted Interpretation (zDAI) framework as a case-specific heuristic for three linked activities: narrative organization, documentary and temporal checking, and structural synthesis. Differences among ChatGPT, Gemini, and Claude are reported as context-bound analytic tendencies rather than stable properties or performance rankings. An independently issued administrative review determination was analyzed separately from AI outputs and used only as documentary evidence concerning continued involuntary hospitalization at the time of review. The case suggests that multi-system AI interaction may support patient-led documentary organization, retrospective reconstruction, and reflective interpretation under conditions of informational complexity, while exposing risks concerning hallucination, reproducibility, privacy, relational ethics, platform instability, unequal access, and the possibility that AI may amplify rather than resolve epistemic asymmetries. The article therefore offers a preliminary conceptual basis for future research rather than evidence that zDAI is generally effective.