AI-Assisted vs Usual Follow-up in Kidney Transplantation

Rationale. Kidney transplant recipients require lifelong, protocol-driven outpatient follow-up. After the first 6 to 12 months, care of stable recipients becomes standardized, and a large share of each routine visit is spent on standardizable tasks: laboratory review, immunosuppression reconciliation, screening and vaccination checks, and clinical documentation. Growing recipient numbers, a shortage of nephrologists, and the concentration of specialists in tertiary centers lengthen follow-up intervals, particularly within the Brazilian Unified Health System (SUS). Large language models (LLMs) have shown promise on patient-facing tasks, but existing evidence derives mainly from vignette- or forum-based comparisons, without real-workflow evaluation, prospective safety endpoints, or blinded quality instruments. A retrieval-augmented generation (RAG) architecture that grounds model outputs in an indexed base of guidelines and institutional protocols, with verifiable citations and explicit uncertainty flagging, addresses hallucination risk and enables auditable, specialty-specific use. This trial moves the evaluation of LLM support from vignettes to a real ambulatory workflow under mandatory human supervision.

Study design and setting. This is a randomized, controlled, parallel-group, non-inferiority trial conducted at the kidney transplant outpatient clinic of the Faculdade de Medicina de Botucatu, Universidade Estadual Paulista (UNESP), Brazil. Reporting follows the CONSORT 2010 statement. Allocation is 1:1 to an AI-assisted follow-up consultation or a usual physician-led consultation.

Intervention (AI-assisted arm). A trained health professional conducts the routine follow-up consultation while a specialized LLM assistant provides real-time support. The assistant is built on a general-purpose LLM adapted for clinical dialogue and operates through a RAG pipeline that indexes KDIGO guidelines and clinic standard operating procedures. During the encounter the assistant supplies a structured content checklist, guideline-anchored suggestions with source citations, uncertainty flags, and a draft structured clinical note and orders. Guardrails prevent the assistant from finalizing medication changes, orders, or diagnoses. Every proposed action is flagged for clinician review; the responsible clinician verifies vital signs and findings, corrects inaccuracies, and co-signs the encounter before any action is enacted (human-in-the-loop). Transcripts and structured data are stored in the electronic data capture system.

Comparator (usual-care arm). Standard physician-led transplant follow-up per routine clinic practice and current guidelines, comprising history, medication reconciliation with a focus on immunosuppression, focused examination, and management plan.

To limit performance bias, both arms follow an identical pre-specified content checklist and target a similar consultation duration. All consultations are audio-recorded to permit later blinded assessment.