Patient-Specific Implant Design: Current Evidence for Artificial Intelligence, Imaging and Computational Planning—A Systematic Review of the Biomedical-Indexed Literature

Background and objectives: Patient-specific skeletal implants are designed through a pipeline of imaging, computational planning and, increasingly, artificial intelligence. Each pillar has its own validation literature, but no review has asked what evidence supports each one in implant design itself. We mapped that evidence from the biomedical-indexed literature in this paper. Methods: A systematic review was conducted under a protocol committed before retrieval (PRISMA 2020, PRISMA-S, SWiM). MEDLINE, Europe PMC, Crossref and OpenAlex were searched on 2 and 3 September 2026 for primary studies from 2015 onwards applying artificial intelligence, an evaluated imaging pipeline or computational planning to an implant whose geometry derives from the individual patient’s anatomy. Engineering indexes were unavailable, so this review maps biomedical-indexed literature, and a bounded probe was used to estimate what that omits. Two artificial intelligence screeners from different vendors screened every record; every clinical study was appraised twice, independently, by assessors from different vendors. Claims about validation quality are reported on the studies read in full. All screening, charting and appraisals were machine-performed and no human-verified subsample exists. Results: Of 5059 records, 639 studies were included: 373 technical studies, 179 clinical series, 75 comparative studies and 12 randomised trials. Computational planning was applied in 601, an evaluated imaging step in 219 and artificial intelligence in 36. Randomised evidence came from 12 small trials, 10 of them craniomaxillofacial, which evaluated the delivered implant rather than the planning method; virtual surgical planning was used in 11 of them. When restricting to the four studies that measure deviation from the virtual plan, with smaller meaning better, patient-specific implants deviated less from the plan (standardised mean difference −1.21, 95% CI −1.69 to −0.74). Revision or removal was less frequent (3 of 135 against 25 of 149; risk ratio 0.28, 95% CI 0.11 to 0.72); operative time, complications and infection gave intervals compatible with a difference either way. No randomised trial was at low risk of bias, and 69 of 75 comparative studies were at serious risk, mostly of unadjusted confounding, so all estimates are of very low certainty. Among technical studies read in full, independent validation material was used in fewer than half, and files were shared in about one in fourteen. Of 36 artificial intelligence studies, 4 were evaluated on independent data, 2 were used for a real patient, none were clinically comparative, and 8 of 17 cranial studies relied on AutoImplant family benchmarks. Conclusions: The design pipeline is unequally validated: planning has clinical evidence concentrated in one region, computational methods are widely applied but rarely validated independently or shared, and artificial intelligence has benchmark evidence for cranial defect completion and little else. Outcome studies of patient-specific implants should be read as evidence about whole pipelines, and future studies should evaluate the components.