Cross-interface persistent memory for large language model-assisted radiology research workflows: a Claude-based...

Large language model (LLM)-assisted radiology research and tool-development work is hampered by a practical form of memory loss we term cross-interface fragmentation: project context built in a web-based conversational interface is not available in a local coding-agent environment, and neither environment retains state across sessions. This forces researchers to manually reconstruct context at every session start and every interface transition. This technical development describes the design and a single-user, proof-of-concept functional demonstration of a Claude-based persistent-memory system intended to reduce this fragmentation for the software-engineering and research-organization layer of radiology AI projects. The system is not a clinical tool and does not process patient data.

We implemented an end-to-end pipeline integrating a custom web application (Created.app/Supabase), a self-hosted n8n automation server, the Claude API (Anthropic), GitHub version control, and the Obsidian knowledge-management application. A web session is summarized by the Claude API into six structured fields (summary, decisions, pending tasks, artifacts, errors, continuation point) and written as a per-project MEMORY.md file to GitHub; a complementary CLAUDE.md file holds accumulated technical context for the local coding agent. The study design is a single-user, single-operator proof-of-concept engineering validation across three concurrent development projects; no efficacy, usability, or clinical-outcome claim is made.

In functional testing, the pipeline transferred project context between the web and local-coding environments without manual copy-paste of session content. A representative 558-token session (n = 1 observation) was processed end-to-end with complete six-field extraction; create and edit operations against the GitHub Contents API succeeded across five consecutive runs without overwrite errors. On a fresh launch, the local coding agent read the memory files and reported project identity, prior technical decisions, and pending tasks without user-provided context. Reported timings are single-run observations, not benchmarked measurements.

A persistent, cross-interface memory layer for LLM-assisted radiology research workflows is technically feasible with currently available components. As a proof of concept it is explicitly limited: single user, Claude-based, text-only, without quantitative extraction-accuracy benchmarking, and dependent on commercial APIs whose cost scales with use (fixed infrastructure plus variable per-session API cost). We discuss a vendor-neutral, open-weights configuration as the recommended direction for cost- and dependency-sensitive settings such as Latin America, and we report that the system has been used in practice to help organize the development of a separate radiology report quality-control tool.