BAIMGPT-PN-RWE: protocol for a multicenter real-world study on six-element medical agent-based closed-loop management of pulmonary nodules

Screening with low-dose helical computed tomography (LDCT) has substantially reduced lung cancer mortality in high-risk populations, but it has also led to the detection of large numbers of pulmonary nodules that require further risk stratification and long-term follow-up. Existing imaging artificial intelligence (AI) tools have focused primarily on nodule detection or prediction of malignancy risk, and smart reminder tools can reduce missed follow-up in some patients. However, evidence from single AI models has not yet answered whether multi-module medical agents can improve patient-level follow-up outcomes. Therefore, this study aims to determine whether a medical agent equipped with six capabilities—perception, comprehension, prediction, decision-making, execution, and learning—can improve whole-process follow-up management of pulmonary nodules and patient clinical outcomes. BAIMGPT-PN-RWE is a multicenter, prospective, active-controlled real-world study enrolling patients with newly detected nodules. We plan to enroll 6,000 adults with newly detected pulmonary nodules measuring 4– 30 mm from healthcare institutions at different tiers. The primary comparison is between patients who initiate the BAIMGPT six-element closed-loop follow-up management system within 7 days after time zero and those who initiate routine manual follow-up or an existing Internet of Things (IoT)–assisted management system. The follow-up period is 12 months (extended to 24 months in a subset of the cohort). The primary analysis emulates a target trial: propensity score overlap weighting will be used to estimate the average treatment effect in the overlap population; a difference-in-differences approach will be used to assess the effect of the staggered rollout across centers; and marginal structural models will be constructed to control for time-dependent confounding arising from the actual intensity of system use. The primary outcome is the completion rate of guideline-concordant follow-up management during the 12-month follow-up period, i.e., the proportion of patients in whom risk stratification, follow-up planning, follow-up execution, reporting of follow-up results, and next-step decision-making are all completed within prespecified time windows. Key safety endpoints include missed diagnosis of malignant nodules, excessive invasive examinations of benign nodules, failure to escalate follow-up for high-risk nodules, and follow-up delays attributable to the system itself. This study evaluates, in the real world, the value of a six-capability medical agent for follow-up management of patients with pulmonary nodules. Based on the study data, we will further delineate the conditions under which six-element closed-loop medical agents can be generalized, should be restricted, or should be discontinued.

Key Words: pulmonary nodules; medical agents; closed-loop management; real-world evidence; target trial emulation; clinical artificial intelligence